From 2e764174bf039e5004bb4fda5b156b3c15ecca2a Mon Sep 17 00:00:00 2001 From: Mark Mikkelsen Date: Mon, 5 Feb 2024 13:07:18 -0500 Subject: [PATCH] Update 2024-02-05 --- _site/additional-tools.html | 2 +- _site/batch-script.html | 2 +- _site/{references.bib => bibliography.bib} | 452 ++++++++++---------- _site/data-quality-metrics.html | 128 +++++- _site/faq.html | 31 +- _site/gannetpreinitialise-settings.html | 2 +- _site/getting-started.html | 2 +- _site/index.html | 8 +- _site/joining-data-files.html | 2 +- _site/loading-data.html | 2 +- _site/output-structure-attributes.html | 2 +- _site/preprocessing.html | 4 +- _site/quantification-tissue-correction.html | 4 +- _site/reference.html | 4 +- _site/release-notes.html | 2 +- _site/signal-modeling.html | 4 +- references.bib => bibliography.bib | 452 ++++++++++---------- data-quality-metrics.Rmd | 23 +- faq.Rmd | 16 +- index.Rmd | 4 +- preprocessing.Rmd | 2 +- quantification-tissue-correction.Rmd | 2 +- reference.Rmd | 6 +- signal-modeling.Rmd | 2 +- 24 files changed, 650 insertions(+), 508 deletions(-) rename _site/{references.bib => bibliography.bib} (91%) mode change 100755 => 100644 rename references.bib => bibliography.bib (91%) mode change 100755 => 100644 diff --git a/_site/additional-tools.html b/_site/additional-tools.html index 162c2db..a08d70d 100644 --- a/_site/additional-tools.html +++ b/_site/additional-tools.html @@ -532,7 +532,7 @@

Additional tools

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Last updated: January 24, 2024

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Last updated: February 05, 2024

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Batch processing script

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Last updated: January 24, 2024

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Last updated: February 05, 2024

diff --git a/_site/references.bib b/_site/bibliography.bib old mode 100755 new mode 100644 similarity index 91% rename from _site/references.bib rename to _site/bibliography.bib index d289d02..c0d7b1e --- a/_site/references.bib +++ b/_site/bibliography.bib @@ -1,36 +1,71 @@ -@article{Barkhuijsen1987, -abstract = {The authors are concerned with a new method of fitting a physical model function to a magnetic resonance signal, directly in the time domain. Their primary aim is analysis of the signal in quantitative terms, i.e., describing the signal in terms of physically meaningful parameters with their statistical errors. Before explaining the new method they make some remarks about the place of time-domain model fitting in spectral analysis}, -author = {Barkhuijsen, H. and de Beer, R. and van Ormondt, D.}, -doi = {10.1016/0022-2364(87)90023-0}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Barkhuijsen, de Beer, van Ormondt/Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals.pdf:pdf}, -isbn = {0022-2364}, -issn = {00222364}, -journal = {Journal of Magnetic Resonance}, +@article{Mikkelsen2018, +author = {Mikkelsen, Mark and Saleh, Muhammad G. and Near, Jamie and Chan, Kimberly L. and Gong, Tao and Harris, Ashley D. and Oeltzschner, Georg and Puts, Nicolaas A.J. and Cecil, Kim M. and Wilkinson, Iain D. and Edden, Richard A.E.}, +doi = {10.1002/mrm.27027}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Frequency and phase correction for multiplexed edited MRS of GABA and glutathione.pdf:pdf}, +issn = {07403194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {frequency,gaba,gsh,her-,mes,mrs,phase correction,spectral registration}, month = {jul}, -number = {3}, -pages = {553--557}, -title = {{Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals}}, -url = {http://linkinghub.elsevier.com/retrieve/pii/0022236487900230 https://linkinghub.elsevier.com/retrieve/pii/0022236487900230}, -volume = {73}, -year = {1987} +number = {1}, +pages = {21--28}, +title = {{Frequency and phase correction for multiplexed edited MRS of GABA and glutathione}}, +url = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.27027}, +volume = {80}, +year = {2018} } -@article{Edden2012, -abstract = {PURPOSE: To develop an experimental approach for determining in vivo transverse relaxation rates (T(2)) of metabolites that are detected by spectral editing without using simulations, and to demonstrate this approach to measure the T(2) of $\gamma$-aminobutyric acid (GABA).\n\nMATERIALS AND METHODS: The proposed method first determines the TE-dependence of the edited signals using measurements in a pure phantom solution (10 mM $\gamma$-aminobutyric acid; GABA); the phantom T(2) is also determined. Once the editing echo time (TE)-modulation pattern is known, it can then be used to determine T(2) in vivo. The method was applied to measure GABA T(2) in the occipital lobe of five healthy adult subjects at 3T, using a J-difference editing method. Unwanted macromolecular contributions to the GABA signal were also measured.\n\nRESULTS: The in vivo T(2) of edited GABA signal was 88 ± 12 ms; this preliminary result is somewhat shorter than other metabolite T(2) values in the literature at this field strength.\n\nCONCLUSION: Spectral editing methods are now widely used to detect low concentration metabolites, such as GABA, but to date no edited acquisition methods have been proposed for the measurement of transverse relaxation times (T(2)). The method described has been successfully applied to measuring the T(2) of GABA.}, -author = {Edden, Richard A.E. and Intrapiromkul, Jarunee and Zhu, He and Cheng, Ying and Barker, Peter B}, -doi = {10.1002/jmri.22865}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Edden et al/Measuring T2 in vivo with J-difference editing Application to GABA at 3 Tesla.pdf:pdf}, -isbn = {1522-2586 (Electronic)\r1053-1807 (Linking)}, -issn = {10531807}, +@article{Mescher1998, +abstract = {Water suppression is typically performed in vivo by exciting the longitudinal magnetization in combination with dephasing, or by using frequency-selective coherence generation. MEGA, a frequency-selective refocusing technique, can be placed into any pulse sequence element designed to generate a Hahn spin-echo or stimulated echo, to dephase transverse water coherences with minimal spectral distortions. Water suppression performance was verified in vivo using stimulated echo acquisition mode (STEAM) localization, which provided water suppression comparable with that achieved with four selective pulses in 3,1-DRYSTEAM. The advantage of the proposed method was exploited for editing J-coupled resonances. Using a double-banded pulse that selectively inverts a J-coupling partner and simultaneously suppresses water, efficient metabolite editing was achieved in the point resolved spectroscopy (PRESS) and STEAM sequences in which MEGA was incorporated. To illustrate the efficiency of the method, the detection of gamma-aminobutyric acid (GABA) was demonstrated, with minimal contributions from macromolecules and overlying singlet peaks at 4 T. The estimated occipital GABA concentration was consistent with previous reports, suggesting that editing for GABA is efficient when based on MEGA at high field strengths.}, +author = {Mescher, M and Merkle, H and Kirsch, J and Garwood, M and Gruetter, R}, +doi = {10.1002/(SICI)1099-1492(199810)11:6<266::AID-NBM530>3.0.CO;2-J}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mescher et al/Simultaneous in vivo spectral editing and water suppression.pdf:pdf}, +isbn = {0952-3480 (Print)}, +issn = {0952-3480}, +journal = {NMR in Biomedicine}, +keywords = {Frequency selective water suppression,GABA,Human brain,In vivo 1H MRS}, +month = {oct}, +number = {6}, +pages = {266--272}, +pmid = {9802468}, +title = {{Simultaneous in vivo spectral editing and water suppression}}, +url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1099-1492(199810)11:6%3C266::AID-NBM530%3E3.0.CO;2-J}, +volume = {11}, +year = {1998} +} +@article{Lu2005, +abstract = {PURPOSE: To establish image parameters for some routine clinical brain MRI pulse sequences at 3.0 T with the goal of maintaining, as much as possible, the well-characterized 1.5-T image contrast characteristics for daily clinical diagnosis, while benefiting from the increased signal to noise at higher field. MATERIALS AND METHODS: A total of 10 healthy subjects were scanned on 1.5-T and 3.0-T systems for T(1) and T(2) relaxation time measurements of major gray and white matter structures. The relaxation times were subsequently used to determine 3.0-T acquisition parameters for spin-echo (SE), T(1)-weighted, fast spin echo (FSE) or turbo spin echo (TSE), T(2)-weighted, and fluid-attenuated inversion recovery (FLAIR) pulse sequences that give image characteristics comparable to 1.5 T, to facilitate routine clinical diagnostics. Application of the routine clinical sequences was performed in 10 subjects, five normal subjects and five patients with various pathologies. RESULTS: T(1) and T(2) relaxation times were, respectively, 14% to 30% longer and 12% to 19% shorter at 3.0 T when compared to the values at 1.5 T, depending on the region evaluated. When using appropriate parameters, routine clinical images acquired at 3.0 T showed similar image characteristics to those obtained at 1.5 T, but with higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), which can be used to reduce the number of averages and scan times. Recommended imaging parameters for these sequences are provided. CONCLUSION: When parameters are adjusted for changes in relaxation rates, routine clinical scans at 3.0 T can provide similar image appearance as 1.5 T, but with superior image quality and/or increased speed.}, +author = {Lu, Hanzhang and Nagae-Poetscher, Lidia M. and Golay, Xavier and Lin, Doris and Pomper, Martin and van Zijl, Peter C.M.}, +doi = {10.1002/jmri.20356}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Lu et al/Routine clinical brain MRI sequences for use at 3.0 Tesla.pdf:pdf}, +isbn = {1522-2586}, +issn = {1053-1807}, journal = {Journal of Magnetic Resonance Imaging}, -keywords = {GABA,T 2,brain,edited MR spectroscopy,transverse relaxation}, -month = {jan}, +keywords = {Brain,Clinical MRI,FLAIR,High field,T1,T2}, +month = {jul}, number = {1}, -pages = {229--234}, -pmid = {22045601}, -title = {{Measuring T2 in vivo with J-difference editing: Application to GABA at 3 Tesla}}, -url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3377980&tool=pmcentrez&rendertype=abstract http://doi.wiley.com/10.1002/jmri.22865}, -volume = {35}, -year = {2012} +pages = {13--22}, +pmid = {15971174}, +title = {{Routine clinical brain MRI sequences for use at 3.0 Tesla}}, +url = {http://doi.wiley.com/10.1002/jmri.20356}, +volume = {22}, +year = {2005} +} +@article{Piechnik2009, +abstract = {Cerebrospinal fluid (CSF) provides hydraulic suspension for the brain. The general concept of bulk CSF production, circulation, and reabsorption is well established, but the mechanisms of momentary CSF volume variation corresponding to vasoreactive changes are far less understood. Nine individuals were studied in a 3T MR scanner with a protocol that included visual stimulation using a 10-Hz reversing checkerboard and administration of a 5% CO(2) mix in air. We acquired PRESS-localized spin-echoes (TR = 12 sec, TE = 26 ms to 1.5 sec) from an 8-mL voxel located in the visual cortex. Echo amplitudes were fitted to a two-compartmental model of relaxation to estimate the partial volume of CSF and the T(2) relaxation times of the tissues. CSF signal contributed 10.7 +/- 3% of the total, with T(2,csf) = 503.0 +/- 64.3 [ms], T(2,brain) = 61.0 +/- 2 [ms]. The relaxation time of tissue increased during physiological stimulation, while the fraction of signal contributed by CSF decreased significantly by 5-6% with visual stimulation (P < 0.03) and by 3% under CO(2) inhalation (P < 0.08). The CSF signal fraction is shown to represent well the volume changes under viable physiological scenarios. In conclusion, CSF plays a significant role in buffering the changes in cerebral blood volume, especially during rapid functional stimuli.}, +author = {Piechnik, S.K. and Evans, J. and Bary, L.H. and Wise, R.G. and Jezzard, P.}, +doi = {10.1002/mrm.21897}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Piechnik et al/Functional changes in CSF volume estimated using measurement of water T2 relaxation.pdf:pdf}, +isbn = {1522-2594 (Electronic)}, +issn = {07403194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {CO2 reactivity,Cerebral blood flow,Cerebrospinal fluid,Functional responses,Magnetic resonance imaging,Magnetic resonance spectroscopy,Vasodilatation}, +month = {mar}, +number = {3}, +pages = {579--586}, +pmid = {19132756}, +title = {{Functional changes in CSF volume estimated using measurement of water T2 relaxation}}, +url = {http://doi.wiley.com/10.1002/mrm.21897}, +volume = {61}, +year = {2009} } @article{Ernst1993, abstract = {A method is presented to determine the compartmentation of a localized region in the human brain in terms of CSF, tissue water, and an NMR-invisible rest, using a PRESS or STEAM sequence. Discrimination between CSF and tissue water is based on differences in their T2 relaxation times. The NMR-invisible compartment is assessed using an external standard. The composition of three regions in the human brain is determined. The CSF content of specific regions can be used to quantify cortical atrophy. The method provides a means for measuring the water content of brain tissue in vivo with a precision of 1.5%. After appropriate corrections, the results are in close agreement with biochemical values. The method has major applications in localized quantitative spectroscopy. The compartmentation model can be used to correct for the CSF content of the selected volume and to properly define and interconvert all major concentration units.}, @@ -47,41 +82,6 @@ @article{Ernst1993 volume = {102}, year = {1993} } -@article{Evans2013, -abstract = {PURPOSE: To compare the repeatability of $\gamma$-aminobutyric acid (GABA) measurements using J-difference editing, before and after spectral realignment-a technique which has previously been demonstrated to improve the quality of J-difference GABA spectra.\n\nMATERIALS AND METHODS: We performed in vivo measurements in three brain regions (occipital, sensorimotor, and dorsolateral prefrontal cortex [DLPFC]), and analyzed these using alternative alignment approaches to evaluate the impact of alignment on repeatability: "Independent alignment" (aligning each subspectrum independently) and "Pairwise alignment" (aligning each on and off subspectrum as a pair) were compared.\n\nRESULTS: Pairwise alignment improved the group mean coefficient of variation in all regions; 0.4% in occipital, 1.1% in sensorimotor, and 1.1% in DLPFC. Independent alignment resulted in subtraction artifacts in the majority of cases, and increased the coefficient of variation in the DLPFC by 9.4%. Simulations demonstrate that the GABA quantification error in datasets with high B0 drift, is 4.5% without alignment, but <1% with optimal alignment.\n\nCONCLUSION: Pairwise alignment improves the repeatability of GABA spectroscopy data. However, independently aligning all on and off subspectra can lead to artifacts and worse repeatability when compared with nonaligned data.}, -author = {Evans, C. John and Puts, Nicolaas A.J. and Robson, Si{\^{a}}n E. and Boy, Frederic and McGonigle, David J. and Sumner, Petroc and Singh, Krish D. and Edden, Richard A.E.}, -doi = {10.1002/jmri.23923}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Evans et al/Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing.pdf:pdf}, -issn = {10531807}, -journal = {Journal of Magnetic Resonance Imaging}, -keywords = {GABA,MRS,frequency alignment,repeatability,subtraction artifact}, -month = {oct}, -number = {4}, -pages = {970--975}, -pmid = {23188759}, -title = {{Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/23188759 http://doi.wiley.com/10.1002/jmri.23923 https://onlinelibrary.wiley.com/doi/10.1002/jmri.23923}, -volume = {38}, -year = {2013} -} -@article{Gasparovic2006, -abstract = {A strategy for using tissue water as a concentration standard in (1)H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with FAST multispectral segmentation are reported: GM [NAc] = 17.16 +/- 1.19 mM; WM [NAc] = 14.26 +/- 1.38 mM; GM [Ch] = 3.27 +/- 0.47 mM; WM [Ch] = 2.65 +/- 0.25 mM; GM [Cr] = 13.98 +/- 1.20 mM; and WM [Cr] = 7.10 +/- 0.67 mM.}, -author = {Gasparovic, Charles and Song, Tao and Devier, Deidre and Bockholt, H Jeremy and Caprihan, Arvind and Mullins, Paul G and Posse, Stefan and Jung, Rex E and Morrison, Leslie A}, -doi = {10.1002/mrm.20901}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Gasparovic et al/Use of tissue water as a concentration reference for proton spectroscopic imaging.pdf:pdf}, -isbn = {0740-3194 (Print)\n0740-3194 (Linking)}, -issn = {0740-3194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {1H-MRS,Relaxation times,Spectroscopic imaging,Tissue water,Voxel}, -month = {jun}, -number = {6}, -pages = {1219--1226}, -pmid = {16688703}, -title = {{Use of tissue water as a concentration reference for proton spectroscopic imaging}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/16688703 http://doi.wiley.com/10.1002/mrm.20901}, -volume = {55}, -year = {2006} -} @article{Harris2017, author = {Harris, Ashley D and Saleh, Muhammad G and Edden, Richard A.E.}, doi = {10.1002/mrm.26619}, @@ -97,101 +97,22 @@ @article{Harris2017 volume = {77}, year = {2017} } -@article{Harris2015, -author = {Harris, Ashley D. and Puts, Nicolaas A.J. and Edden, Richard A.E.}, -doi = {10.1002/jmri.24903}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations.pdf:pdf;:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations(2).pdf:pdf}, -issn = {10531807}, -journal = {Journal of Magnetic Resonance Imaging}, -month = {nov}, -number = {5}, -pages = {1431--1440}, -title = {{Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations}}, -url = {http://doi.wiley.com/10.1002/jmri.24903}, -volume = {42}, -year = {2015} -} -@article{Klose1990, -abstract = {Spatially localized methods in spectroscopy often operate with magnetic field gradients for volume selection. The eddy currents induced by these gradients produce time-dependent shifts of the resonance frequency in the selected volume, which results in a distortion of the spectrum after Fourier transformation. In whole-body systems the complete compensation of eddy currents is a difficult procedure. To avoid this, a correction method is proposed for proton spectroscopy, which uses the signal of prominent water protons as a reference for the water-suppressed signal. The correction is performed in the time domain, dividing the water-suppressed signal by the phase factor of the water signal for each data point. The corrected spectra have a good resolution as shown by phantom measurements and brain and muscle spectra of volunteers.}, -author = {Klose, Uwe}, -doi = {10.1002/mrm.1910140104}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Klose/In vivo proton spectroscopy in presence of eddy currents.pdf:pdf}, +@article{Near2015, +abstract = {PURPOSE: Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data.\n\nMETHODS: We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as "spectral registration." Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels.\n\nRESULTS: Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation.\n\nCONCLUSIONS: Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile. Magn Reson Med, 2014. {\textcopyright} 2014 Wiley Periodicals, Inc.}, +author = {Near, Jamie and Edden, Richard and Evans, C John and Paquin, Rapha{\"{e}}l and Harris, Ashley and Jezzard, Peter}, +doi = {10.1002/mrm.25094}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Near et al/Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.pdf:pdf}, issn = {07403194}, journal = {Magnetic Resonance in Medicine}, -keywords = {Extracellular Space,Fourier Analysis,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: methods,Protons,Time Factors}, -month = {apr}, -number = {1}, -pages = {26--30}, -pmid = {2161984}, -title = {{In vivo proton spectroscopy in presence of eddy currents}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/2161984 http://doi.wiley.com/10.1002/mrm.1910140104}, -volume = {14}, -year = {1990} -} -@article{Lu2005, -abstract = {PURPOSE: To establish image parameters for some routine clinical brain MRI pulse sequences at 3.0 T with the goal of maintaining, as much as possible, the well-characterized 1.5-T image contrast characteristics for daily clinical diagnosis, while benefiting from the increased signal to noise at higher field. MATERIALS AND METHODS: A total of 10 healthy subjects were scanned on 1.5-T and 3.0-T systems for T(1) and T(2) relaxation time measurements of major gray and white matter structures. The relaxation times were subsequently used to determine 3.0-T acquisition parameters for spin-echo (SE), T(1)-weighted, fast spin echo (FSE) or turbo spin echo (TSE), T(2)-weighted, and fluid-attenuated inversion recovery (FLAIR) pulse sequences that give image characteristics comparable to 1.5 T, to facilitate routine clinical diagnostics. Application of the routine clinical sequences was performed in 10 subjects, five normal subjects and five patients with various pathologies. RESULTS: T(1) and T(2) relaxation times were, respectively, 14% to 30% longer and 12% to 19% shorter at 3.0 T when compared to the values at 1.5 T, depending on the region evaluated. When using appropriate parameters, routine clinical images acquired at 3.0 T showed similar image characteristics to those obtained at 1.5 T, but with higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), which can be used to reduce the number of averages and scan times. Recommended imaging parameters for these sequences are provided. CONCLUSION: When parameters are adjusted for changes in relaxation rates, routine clinical scans at 3.0 T can provide similar image appearance as 1.5 T, but with superior image quality and/or increased speed.}, -author = {Lu, Hanzhang and Nagae-Poetscher, Lidia M. and Golay, Xavier and Lin, Doris and Pomper, Martin and van Zijl, Peter C.M.}, -doi = {10.1002/jmri.20356}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Lu et al/Routine clinical brain MRI sequences for use at 3.0 Tesla.pdf:pdf}, -isbn = {1522-2586}, -issn = {1053-1807}, -journal = {Journal of Magnetic Resonance Imaging}, -keywords = {Brain,Clinical MRI,FLAIR,High field,T1,T2}, -month = {jul}, +keywords = {B0 drift,Frequency drift,Magnetic resonance spectroscopy,Motion correction,Phase drift}, +month = {jan}, number = {1}, -pages = {13--22}, -pmid = {15971174}, -title = {{Routine clinical brain MRI sequences for use at 3.0 Tesla}}, -url = {http://doi.wiley.com/10.1002/jmri.20356}, -volume = {22}, -year = {2005} -} -@article{Mescher1998, -abstract = {Water suppression is typically performed in vivo by exciting the longitudinal magnetization in combination with dephasing, or by using frequency-selective coherence generation. MEGA, a frequency-selective refocusing technique, can be placed into any pulse sequence element designed to generate a Hahn spin-echo or stimulated echo, to dephase transverse water coherences with minimal spectral distortions. Water suppression performance was verified in vivo using stimulated echo acquisition mode (STEAM) localization, which provided water suppression comparable with that achieved with four selective pulses in 3,1-DRYSTEAM. The advantage of the proposed method was exploited for editing J-coupled resonances. Using a double-banded pulse that selectively inverts a J-coupling partner and simultaneously suppresses water, efficient metabolite editing was achieved in the point resolved spectroscopy (PRESS) and STEAM sequences in which MEGA was incorporated. To illustrate the efficiency of the method, the detection of gamma-aminobutyric acid (GABA) was demonstrated, with minimal contributions from macromolecules and overlying singlet peaks at 4 T. The estimated occipital GABA concentration was consistent with previous reports, suggesting that editing for GABA is efficient when based on MEGA at high field strengths.}, -author = {Mescher, M and Merkle, H and Kirsch, J and Garwood, M and Gruetter, R}, -doi = {10.1002/(SICI)1099-1492(199810)11:6<266::AID-NBM530>3.0.CO;2-J}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mescher et al/Simultaneous in vivo spectral editing and water suppression.pdf:pdf}, -isbn = {0952-3480 (Print)}, -issn = {0952-3480}, -journal = {NMR in Biomedicine}, -keywords = {Frequency selective water suppression,GABA,Human brain,In vivo 1H MRS}, -month = {oct}, -number = {6}, -pages = {266--272}, -pmid = {9802468}, -title = {{Simultaneous in vivo spectral editing and water suppression}}, -url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1099-1492(199810)11:6%3C266::AID-NBM530%3E3.0.CO;2-J}, -volume = {11}, -year = {1998} -} -@article{Mikkelsen2017, -author = {Mikkelsen, Mark and Barker, Peter B and Bhattacharyya, Pallab K and Brix, Maiken K and Buur, Pieter F. and Cecil, Kim M and Chan, Kimberly L and Chen, David Y.-T. and Craven, Alexander R and Cuypers, Koen and Dacko, Michael and Duncan, Niall W and Dydak, Ulrike and Edmondson, David A and Ende, Gabriele and Ersland, Lars and Gao, Fei and Greenhouse, Ian and Harris, Ashley D and He, Naying and Heba, Stefanie and Hoggard, Nigel and Hsu, Tun-wei and Jansen, Jacobus F.A. and Kangarlu, Alayar and Lange, Thomas and Lebel, R Marc and Li, Yan and Lin, Chien-yuan E and Liou, Jy-kang and Lirng, Jiing-Feng and Liu, Feng and Ma, Ruoyun and Maes, Celine and Moreno-Ortega, Marta and Murray, Scott O and Noah, Sean and Noeske, Ralph and Noseworthy, Michael D and Oeltzschner, Georg and Prisciandaro, James J. and Puts, Nicolaas A.J. and Roberts, Timothy P.L. and Sack, Markus and Sailasuta, Napapon and Saleh, Muhammad G and Schallmo, Michael-paul and Simard, Nicholas and Swinnen, Stephan P. and Tegenthoff, Martin and Truong, Peter and Wang, Guangbin and Wilkinson, Iain D and Wittsack, Hans-J{\"{o}}rg and Xu, Hongmin and Yan, Fuhua and Zhang, Chencheng and Zipunnikov, Vadim and Z{\"{o}}llner, Helge J. and Edden, Richard A.E.}, -doi = {10.1016/j.neuroimage.2017.07.021}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Big GABA Edited MR spectroscopy at 24 research sites.pdf:pdf}, -issn = {10538119}, -journal = {NeuroImage}, -month = {oct}, -pages = {32--45}, -publisher = {Elsevier Inc.}, -title = {{Big GABA: Edited MR spectroscopy at 24 research sites}}, -url = {https://doi.org/10.1016/j.neuroimage.2017.07.021 https://linkinghub.elsevier.com/retrieve/pii/S105381191730589X}, -volume = {159}, -year = {2017} -} -@article{Mikkelsen2020, -author = {Mikkelsen, Mark and Tapper, Sofie and Near, Jamie and Mostofsky, Stewart H. and Puts, Nicolaas A. J. and Edden, Richard A. E.}, -doi = {10.1002/nbm.4368}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Correcting frequency and phase offsets in MRS data using robust spectral registration.pdf:pdf}, -issn = {0952-3480}, -journal = {NMR in Biomedicine}, -month = {oct}, -number = {10}, -pages = {e4368}, -pmid = {32656879}, -title = {{Correcting frequency and phase offsets in MRS data using robust spectral registration}}, -url = {https://onlinelibrary.wiley.com/doi/10.1002/nbm.4368}, -volume = {33}, -year = {2020} +pages = {44--50}, +pmid = {24436292}, +title = {{Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/24436292 http://doi.wiley.com/10.1002/mrm.25094}, +volume = {73}, +year = {2015} } @article{Mullins2014, abstract = {There is increasing interest in the use of edited proton magnetic resonance spectroscopy for the detection of GABA in the human brain. At a recent meeting held at Cardiff University, a number of spectroscopy groups met to discuss the acquisition, analysis and interpretation of GABA-edited MR spectra. This paper aims to set out the issues discussed at this meeting, reporting areas of consensus around parameters and procedures in the field and highlighting those areas where differences remain. It is hoped that this paper can fulfill two needs, providing a summary of the current 'state-of-the-art' in the field of GABA-edited MRS at 3T using MEGA-PRESS and a basic guide to help researchers new to the field to avoid some of the pitfalls inherent in the acquisition and processing of edited MRS for GABA.}, @@ -210,41 +131,6 @@ @article{Mullins2014 volume = {86}, year = {2014} } -@article{Near2015, -abstract = {PURPOSE: Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data.\n\nMETHODS: We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as "spectral registration." Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels.\n\nRESULTS: Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation.\n\nCONCLUSIONS: Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile. Magn Reson Med, 2014. {\textcopyright} 2014 Wiley Periodicals, Inc.}, -author = {Near, Jamie and Edden, Richard and Evans, C John and Paquin, Rapha{\"{e}}l and Harris, Ashley and Jezzard, Peter}, -doi = {10.1002/mrm.25094}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Near et al/Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.pdf:pdf}, -issn = {07403194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {B0 drift,Frequency drift,Magnetic resonance spectroscopy,Motion correction,Phase drift}, -month = {jan}, -number = {1}, -pages = {44--50}, -pmid = {24436292}, -title = {{Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/24436292 http://doi.wiley.com/10.1002/mrm.25094}, -volume = {73}, -year = {2015} -} -@article{Piechnik2009, -abstract = {Cerebrospinal fluid (CSF) provides hydraulic suspension for the brain. The general concept of bulk CSF production, circulation, and reabsorption is well established, but the mechanisms of momentary CSF volume variation corresponding to vasoreactive changes are far less understood. Nine individuals were studied in a 3T MR scanner with a protocol that included visual stimulation using a 10-Hz reversing checkerboard and administration of a 5% CO(2) mix in air. We acquired PRESS-localized spin-echoes (TR = 12 sec, TE = 26 ms to 1.5 sec) from an 8-mL voxel located in the visual cortex. Echo amplitudes were fitted to a two-compartmental model of relaxation to estimate the partial volume of CSF and the T(2) relaxation times of the tissues. CSF signal contributed 10.7 +/- 3% of the total, with T(2,csf) = 503.0 +/- 64.3 [ms], T(2,brain) = 61.0 +/- 2 [ms]. The relaxation time of tissue increased during physiological stimulation, while the fraction of signal contributed by CSF decreased significantly by 5-6% with visual stimulation (P < 0.03) and by 3% under CO(2) inhalation (P < 0.08). The CSF signal fraction is shown to represent well the volume changes under viable physiological scenarios. In conclusion, CSF plays a significant role in buffering the changes in cerebral blood volume, especially during rapid functional stimuli.}, -author = {Piechnik, S.K. and Evans, J. and Bary, L.H. and Wise, R.G. and Jezzard, P.}, -doi = {10.1002/mrm.21897}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Piechnik et al/Functional changes in CSF volume estimated using measurement of water T2 relaxation.pdf:pdf}, -isbn = {1522-2594 (Electronic)}, -issn = {07403194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {CO2 reactivity,Cerebral blood flow,Cerebrospinal fluid,Functional responses,Magnetic resonance imaging,Magnetic resonance spectroscopy,Vasodilatation}, -month = {mar}, -number = {3}, -pages = {579--586}, -pmid = {19132756}, -title = {{Functional changes in CSF volume estimated using measurement of water T2 relaxation}}, -url = {http://doi.wiley.com/10.1002/mrm.21897}, -volume = {61}, -year = {2009} -} @article{Puts2013, abstract = {PURPOSE: To measure the in vivo longitudinal relaxation time T1 of GABA at 3 Tesla (T).\n\nMATERIALS AND METHODS: J-difference edited single-voxel MR spectroscopy was used to isolate $\gamma$-aminobutyric acid (GABA) signals. An increased echo time (80 ms) acquisition was used, accommodating the longer, more selective editing pulses required for symmetric editing-based suppression of co-edited macromolecular signal. Acquiring edited GABA measurements at a range of relaxation times in 10 healthy participants, a saturation-recovery equation was used to model the integrated data.\n\nRESULTS: The longitudinal relaxation time of GABA was measured as T(1,GABA) = 1.31 ± 0.16 s.\n\nCONCLUSION: The method described has been successfully applied to measure the T1 of GABA in vivo at 3T.}, author = {Puts, Nicolaas A.J. and Barker, Peter B and Edden, Richard A.E.}, @@ -263,6 +149,40 @@ @article{Puts2013 volume = {37}, year = {2013} } +@article{Klose1990, +abstract = {Spatially localized methods in spectroscopy often operate with magnetic field gradients for volume selection. The eddy currents induced by these gradients produce time-dependent shifts of the resonance frequency in the selected volume, which results in a distortion of the spectrum after Fourier transformation. In whole-body systems the complete compensation of eddy currents is a difficult procedure. To avoid this, a correction method is proposed for proton spectroscopy, which uses the signal of prominent water protons as a reference for the water-suppressed signal. The correction is performed in the time domain, dividing the water-suppressed signal by the phase factor of the water signal for each data point. The corrected spectra have a good resolution as shown by phantom measurements and brain and muscle spectra of volunteers.}, +author = {Klose, Uwe}, +doi = {10.1002/mrm.1910140104}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Klose/In vivo proton spectroscopy in presence of eddy currents.pdf:pdf}, +issn = {07403194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {Extracellular Space,Fourier Analysis,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: methods,Protons,Time Factors}, +month = {apr}, +number = {1}, +pages = {26--30}, +pmid = {2161984}, +title = {{In vivo proton spectroscopy in presence of eddy currents}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/2161984 http://doi.wiley.com/10.1002/mrm.1910140104}, +volume = {14}, +year = {1990} +} +@article{Kreis2004, +abstract = {In spite of the facts that magnetic resonance spectroscopy (MRS) is applied as clinical tool in non-specialized institutions and that semi-automatic acquisition and processing tools can be used to produce quantitative information from MRS exams without expert information, issues of spectral quality and quality assessment are neglected in the literature of MR spectroscopy. Even worse, there is no consensus among experts on concepts or detailed criteria of quality assessment for MR spectra. Furthermore, artifacts are not at all conspicuous in MRS and can easily be taken for true, interpretable features. This article aims to increase interest in issues of spectral quality and quality assessment, to start a larger debate on generally accepted criteria that spectra must fulfil to be clinically and scientifically acceptable, and to provide a sample gallery of artifacts, which can be used to raise awareness for potential pitfalls in MRS.}, +author = {Kreis, Roland}, +doi = {10.1002/nbm.891}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Kreis/Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts.pdf:pdf}, +issn = {0952-3480}, +journal = {NMR in Biomedicine}, +keywords = {Algorithms,Artifacts,Biomedical,Biomedical: methods,Clinical Medicine,Clinical Medicine: methods,Computer-Assisted,Computer-Assisted: methods,Diagnosis,Equipment Failure,Equipment Failure Analysis,Equipment Failure Analysis: methods,Health Care,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: instrumentation,Magnetic Resonance Spectroscopy: methods,Protons,Quality Assurance,Reproducibility of Results,Sensitivity and Specificity,Technology Assessment}, +month = {oct}, +number = {6}, +pages = {361--381}, +pmid = {15468083}, +title = {{Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/15468083 http://doi.wiley.com/10.1002/nbm.891 https://onlinelibrary.wiley.com/doi/10.1002/nbm.891}, +volume = {17}, +year = {2004} +} @article{Wansapura1999, abstract = {Relaxation time measurements at 3.0 T are reported for both gray and white matter in normal human brain. Measurements were made using a 3.0 T Bruker Biospec magnetic resonance imaging (MRI) scanner in normal adults with no clinical evidence of neurological disease. Nineteen subjects, 8 female and 11 male, were studied for T1 and T2 measurements, and 7 males were studied for T*2. Measurements were made using a saturation recovery method for T1, a multiple spin-echo experiment for T2, and a fast low-angle shot (FLASH) sequence with 14 different echo times for T*2. Results of the measurements are summarized as follows. Average T1 values measured for gray matter and white matter were 1331 and 832 msec, respectively. Average T2 values measured for gray matter and white matter were 80 and 110 msec, respectively. The average T*2 values for occipital and frontal gray matter were 41.6 and 51.8 msec, respectively. Average T*2 values for occipital and frontal white matter were 48.4 and 44.7 msec, respectively. ANOVA tests of the measurements revealed that for both gray and white matter there were no significant differences in T1 from one location in the brain to another. T2 in occipital gray matter was significantly higher (0.0001 < P < .0375) than the rest of the gray matter, while T2 in frontal white matter was significantly lower (P < 0.0001). Statistical analysis of cerebral hemispheric differences in relaxation time measurements showed no significant differences in T1 values from the left hemisphere compared with the right, except in insular gray matter, where this difference was significant at P = 0.0320. No significant difference in T2 values existed between the left and right cerebral hemispheres. Significant differences were apparent between male and female relaxation time measurements in brain. J. Magn. Reson. Imaging 1999;9:531–538. {\textcopyright} 1999 Wiley-Liss, Inc.}, author = {Wansapura, Janaka P and Holland, Scott K and Dunn, R Scott and Ball, William S}, @@ -281,6 +201,40 @@ @article{Wansapura1999 volume = {9}, year = {1999} } +@article{Barkhuijsen1987, +abstract = {The authors are concerned with a new method of fitting a physical model function to a magnetic resonance signal, directly in the time domain. Their primary aim is analysis of the signal in quantitative terms, i.e., describing the signal in terms of physically meaningful parameters with their statistical errors. Before explaining the new method they make some remarks about the place of time-domain model fitting in spectral analysis}, +author = {Barkhuijsen, H. and de Beer, R. and van Ormondt, D.}, +doi = {10.1016/0022-2364(87)90023-0}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Barkhuijsen, de Beer, van Ormondt/Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals.pdf:pdf}, +isbn = {0022-2364}, +issn = {00222364}, +journal = {Journal of Magnetic Resonance}, +month = {jul}, +number = {3}, +pages = {553--557}, +title = {{Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals}}, +url = {http://linkinghub.elsevier.com/retrieve/pii/0022236487900230 https://linkinghub.elsevier.com/retrieve/pii/0022236487900230}, +volume = {73}, +year = {1987} +} +@article{Edden2012, +abstract = {PURPOSE: To develop an experimental approach for determining in vivo transverse relaxation rates (T(2)) of metabolites that are detected by spectral editing without using simulations, and to demonstrate this approach to measure the T(2) of $\gamma$-aminobutyric acid (GABA).\n\nMATERIALS AND METHODS: The proposed method first determines the TE-dependence of the edited signals using measurements in a pure phantom solution (10 mM $\gamma$-aminobutyric acid; GABA); the phantom T(2) is also determined. Once the editing echo time (TE)-modulation pattern is known, it can then be used to determine T(2) in vivo. The method was applied to measure GABA T(2) in the occipital lobe of five healthy adult subjects at 3T, using a J-difference editing method. Unwanted macromolecular contributions to the GABA signal were also measured.\n\nRESULTS: The in vivo T(2) of edited GABA signal was 88 ± 12 ms; this preliminary result is somewhat shorter than other metabolite T(2) values in the literature at this field strength.\n\nCONCLUSION: Spectral editing methods are now widely used to detect low concentration metabolites, such as GABA, but to date no edited acquisition methods have been proposed for the measurement of transverse relaxation times (T(2)). The method described has been successfully applied to measuring the T(2) of GABA.}, +author = {Edden, Richard A.E. and Intrapiromkul, Jarunee and Zhu, He and Cheng, Ying and Barker, Peter B}, +doi = {10.1002/jmri.22865}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Edden et al/Measuring T2 in vivo with J-difference editing Application to GABA at 3 Tesla.pdf:pdf}, +isbn = {1522-2586 (Electronic)\r1053-1807 (Linking)}, +issn = {10531807}, +journal = {Journal of Magnetic Resonance Imaging}, +keywords = {GABA,T 2,brain,edited MR spectroscopy,transverse relaxation}, +month = {jan}, +number = {1}, +pages = {229--234}, +pmid = {22045601}, +title = {{Measuring T2 in vivo with J-difference editing: Application to GABA at 3 Tesla}}, +url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3377980&tool=pmcentrez&rendertype=abstract http://doi.wiley.com/10.1002/jmri.22865}, +volume = {35}, +year = {2012} +} @article{An2013, abstract = {Purpose: To propose using the generalized least square (GLS) algorithm for combining multichannel single-voxel magnetic resonance spectroscopy (MRS) signals. Materials and Methods: Phantom and in vivo brain MRS experiments on a 7 T scanner equipped with a 32-channel receiver coil, as well as Monte Carlo simulations, were performed to compare the coefficient of variation (CV) of the GLS method with those of two recently reported spectral combination methods. Results: Compared to the two existing methods, the GLS method significantly reduced CV values for the simulation, phantom, and in vivo experiments. Conclusion: The GLS method can lead to improved precision of peak quantification. J. Magn. Reson. Imaging 2013;37:14451450. (c) 2012 Wiley Periodicals, Inc.}, archivePrefix = {arXiv}, @@ -302,35 +256,95 @@ @article{An2013 volume = {37}, year = {2013} } -@article{Kreis2004, -abstract = {In spite of the facts that magnetic resonance spectroscopy (MRS) is applied as clinical tool in non-specialized institutions and that semi-automatic acquisition and processing tools can be used to produce quantitative information from MRS exams without expert information, issues of spectral quality and quality assessment are neglected in the literature of MR spectroscopy. Even worse, there is no consensus among experts on concepts or detailed criteria of quality assessment for MR spectra. Furthermore, artifacts are not at all conspicuous in MRS and can easily be taken for true, interpretable features. This article aims to increase interest in issues of spectral quality and quality assessment, to start a larger debate on generally accepted criteria that spectra must fulfil to be clinically and scientifically acceptable, and to provide a sample gallery of artifacts, which can be used to raise awareness for potential pitfalls in MRS.}, -author = {Kreis, Roland}, -doi = {10.1002/nbm.891}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Kreis/Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts.pdf:pdf}, +@article{Gasparovic2006, +abstract = {A strategy for using tissue water as a concentration standard in (1)H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with FAST multispectral segmentation are reported: GM [NAc] = 17.16 +/- 1.19 mM; WM [NAc] = 14.26 +/- 1.38 mM; GM [Ch] = 3.27 +/- 0.47 mM; WM [Ch] = 2.65 +/- 0.25 mM; GM [Cr] = 13.98 +/- 1.20 mM; and WM [Cr] = 7.10 +/- 0.67 mM.}, +author = {Gasparovic, Charles and Song, Tao and Devier, Deidre and Bockholt, H Jeremy and Caprihan, Arvind and Mullins, Paul G and Posse, Stefan and Jung, Rex E and Morrison, Leslie A}, +doi = {10.1002/mrm.20901}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Gasparovic et al/Use of tissue water as a concentration reference for proton spectroscopic imaging.pdf:pdf}, +isbn = {0740-3194 (Print)\n0740-3194 (Linking)}, +issn = {0740-3194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {1H-MRS,Relaxation times,Spectroscopic imaging,Tissue water,Voxel}, +month = {jun}, +number = {6}, +pages = {1219--1226}, +pmid = {16688703}, +title = {{Use of tissue water as a concentration reference for proton spectroscopic imaging}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/16688703 http://doi.wiley.com/10.1002/mrm.20901}, +volume = {55}, +year = {2006} +} +@article{Evans2013, +abstract = {PURPOSE: To compare the repeatability of $\gamma$-aminobutyric acid (GABA) measurements using J-difference editing, before and after spectral realignment-a technique which has previously been demonstrated to improve the quality of J-difference GABA spectra.\n\nMATERIALS AND METHODS: We performed in vivo measurements in three brain regions (occipital, sensorimotor, and dorsolateral prefrontal cortex [DLPFC]), and analyzed these using alternative alignment approaches to evaluate the impact of alignment on repeatability: "Independent alignment" (aligning each subspectrum independently) and "Pairwise alignment" (aligning each on and off subspectrum as a pair) were compared.\n\nRESULTS: Pairwise alignment improved the group mean coefficient of variation in all regions; 0.4% in occipital, 1.1% in sensorimotor, and 1.1% in DLPFC. Independent alignment resulted in subtraction artifacts in the majority of cases, and increased the coefficient of variation in the DLPFC by 9.4%. Simulations demonstrate that the GABA quantification error in datasets with high B0 drift, is 4.5% without alignment, but <1% with optimal alignment.\n\nCONCLUSION: Pairwise alignment improves the repeatability of GABA spectroscopy data. However, independently aligning all on and off subspectra can lead to artifacts and worse repeatability when compared with nonaligned data.}, +author = {Evans, C. John and Puts, Nicolaas A.J. and Robson, Si{\^{a}}n E. and Boy, Frederic and McGonigle, David J. and Sumner, Petroc and Singh, Krish D. and Edden, Richard A.E.}, +doi = {10.1002/jmri.23923}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Evans et al/Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing.pdf:pdf}, +issn = {10531807}, +journal = {Journal of Magnetic Resonance Imaging}, +keywords = {GABA,MRS,frequency alignment,repeatability,subtraction artifact}, +month = {oct}, +number = {4}, +pages = {970--975}, +pmid = {23188759}, +title = {{Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/23188759 http://doi.wiley.com/10.1002/jmri.23923 https://onlinelibrary.wiley.com/doi/10.1002/jmri.23923}, +volume = {38}, +year = {2013} +} +@article{Hui2021a, +author = {Hui, Steve C.N. and Mikkelsen, Mark and Z{\"{o}}llner, Helge J. and Ahluwalia, Vishwadeep and Alcauter, Sarael and Baltusis, Laima and Barany, Deborah A and Barlow, Laura R and Becker, Robert and Berman, Jeffrey I and Berrington, Adam and Bhattacharyya, Pallab K and Blicher, Jakob Udby and Bogner, Wolfgang and Brown, Mark S and Calhoun, Vince D and Castillo, Ryan and Cecil, Kim M and Choi, Yeo Bi and Chu, Winnie C.W. and Clarke, William T and Craven, Alexander R and Cuypers, Koen and Dacko, Michael and de la Fuente-Sandoval, Camilo and Desmond, Patricia and Domagalik, Aleksandra and Dumont, Julien and Duncan, Niall W and Dydak, Ulrike and Dyke, Katherine and Edmondson, David A and Ende, Gabriele and Ersland, Lars and Evans, C John and Fermin, Alan S.R. and Ferretti, Antonio and Fillmer, Ariane and Gong, Tao and Greenhouse, Ian and Grist, James T and Gu, Meng and Harris, Ashley D and Hat, Katarzyna and Heba, Stefanie and Heckova, Eva and Hegarty, John P. and Heise, Kirstin-friederike and Honda, Shiori and Jacobson, Aaron and Jansen, Jacobus F.A. and Jenkins, Christopher W and Johnston, Stephen J and Juchem, Christoph and Kangarlu, Alayar and Kerr, Adam B and Landheer, Karl and Lange, Thomas and Lee, Phil and Levendovszky, Swati Rane and Limperopoulos, Catherine and Liu, Feng and Lloyd, William and Lythgoe, David J and Machizawa, Maro G and MacMillan, Erin L. and Maddock, Richard J and Manzhurtsev, Andrei V and Martinez-Gudino, Mar{\'{i}}a L. and Miller, Jack J and Mirzakhanian, Heline and Moreno-Ortega, Marta and Mullins, Paul G and Nakajima, Shinichiro and Near, Jamie and Noeske, Ralph and Nordh{\o}y, Wibeke and Oeltzschner, Georg and Osorio-Duran, Raul and Otaduy, Maria C.G. and Pasaye, Erick H and Peeters, Ronald and Peltier, Scott J and Pilatus, Ulrich and Polomac, Nenad and Porges, Eric C and Pradhan, Subechhya and Prisciandaro, James Joseph and Puts, Nicolaas A and Rae, Caroline D and Reyes-Madrigal, Francisco and Roberts, Timothy P.L. and Robertson, Caroline E and Rosenberg, Jens T and Rotaru, Diana-georgiana and {O'Gorman Tuura}, Ruth L and Saleh, Muhammad G and Sandberg, Kristian and Sangill, Ryan and Schembri, Keith and Schrantee, Anouk and Semenova, Natalia A and Singel, Debra and Sitnikov, Rouslan and Smith, Jolinda and Song, Yulu and Stark, Craig and Stoffers, Diederick and Swinnen, Stephan P. and Tain, Rongwen and Tanase, Costin and Tapper, Sofie and Tegenthoff, Martin and Thiel, Thomas and Thioux, Marc and Truong, Peter and van Dijk, Pim and Vella, Nolan and Vidyasagar, Rishma and Vovk, Andrej and Wang, Guangbin and Westlye, Lars T and Wilbur, Timothy K and Willoughby, William R and Wilson, Martin and Wittsack, Hans-J{\"{o}}rg and Woods, Adam J and Wu, Yen-Chien and Xu, Junqian and Lopez, Maria Yanez and Yeung, David K.W. and Zhao, Qun and Zhou, Xiaopeng and Zupan, Gasper and Edden, Richard A.E.}, +doi = {10.1016/j.neuroimage.2021.118430}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Hui et al/Frequency drift in MR spectroscopy at 3T.pdf:pdf}, +issn = {10538119}, +journal = {NeuroImage}, +month = {nov}, +number = {21}, +pages = {118430}, +title = {{Frequency drift in MR spectroscopy at 3T}}, +url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921007059}, +volume = {241}, +year = {2021} +} +@article{Harris2015, +author = {Harris, Ashley D. and Puts, Nicolaas A.J. and Edden, Richard A.E.}, +doi = {10.1002/jmri.24903}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations.pdf:pdf;:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations(2).pdf:pdf}, +issn = {10531807}, +journal = {Journal of Magnetic Resonance Imaging}, +month = {nov}, +number = {5}, +pages = {1431--1440}, +title = {{Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations}}, +url = {http://doi.wiley.com/10.1002/jmri.24903}, +volume = {42}, +year = {2015} +} +@article{Mikkelsen2017, +author = {Mikkelsen, Mark and Barker, Peter B and Bhattacharyya, Pallab K and Brix, Maiken K and Buur, Pieter F. and Cecil, Kim M and Chan, Kimberly L and Chen, David Y.-T. and Craven, Alexander R and Cuypers, Koen and Dacko, Michael and Duncan, Niall W and Dydak, Ulrike and Edmondson, David A and Ende, Gabriele and Ersland, Lars and Gao, Fei and Greenhouse, Ian and Harris, Ashley D and He, Naying and Heba, Stefanie and Hoggard, Nigel and Hsu, Tun-wei and Jansen, Jacobus F.A. and Kangarlu, Alayar and Lange, Thomas and Lebel, R Marc and Li, Yan and Lin, Chien-yuan E and Liou, Jy-kang and Lirng, Jiing-Feng and Liu, Feng and Ma, Ruoyun and Maes, Celine and Moreno-Ortega, Marta and Murray, Scott O and Noah, Sean and Noeske, Ralph and Noseworthy, Michael D and Oeltzschner, Georg and Prisciandaro, James J. and Puts, Nicolaas A.J. and Roberts, Timothy P.L. and Sack, Markus and Sailasuta, Napapon and Saleh, Muhammad G and Schallmo, Michael-paul and Simard, Nicholas and Swinnen, Stephan P. and Tegenthoff, Martin and Truong, Peter and Wang, Guangbin and Wilkinson, Iain D and Wittsack, Hans-J{\"{o}}rg and Xu, Hongmin and Yan, Fuhua and Zhang, Chencheng and Zipunnikov, Vadim and Z{\"{o}}llner, Helge J. and Edden, Richard A.E.}, +doi = {10.1016/j.neuroimage.2017.07.021}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Big GABA Edited MR spectroscopy at 24 research sites.pdf:pdf}, +issn = {10538119}, +journal = {NeuroImage}, +month = {oct}, +pages = {32--45}, +publisher = {Elsevier Inc.}, +title = {{Big GABA: Edited MR spectroscopy at 24 research sites}}, +url = {https://doi.org/10.1016/j.neuroimage.2017.07.021 https://linkinghub.elsevier.com/retrieve/pii/S105381191730589X}, +volume = {159}, +year = {2017} +} +@article{Mikkelsen2020, +author = {Mikkelsen, Mark and Tapper, Sofie and Near, Jamie and Mostofsky, Stewart H. and Puts, Nicolaas A. J. and Edden, Richard A. E.}, +doi = {10.1002/nbm.4368}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Correcting frequency and phase offsets in MRS data using robust spectral registration.pdf:pdf}, issn = {0952-3480}, journal = {NMR in Biomedicine}, -keywords = {Algorithms,Artifacts,Biomedical,Biomedical: methods,Clinical Medicine,Clinical Medicine: methods,Computer-Assisted,Computer-Assisted: methods,Diagnosis,Equipment Failure,Equipment Failure Analysis,Equipment Failure Analysis: methods,Health Care,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: instrumentation,Magnetic Resonance Spectroscopy: methods,Protons,Quality Assurance,Reproducibility of Results,Sensitivity and Specificity,Technology Assessment}, month = {oct}, -number = {6}, -pages = {361--381}, -pmid = {15468083}, -title = {{Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/15468083 http://doi.wiley.com/10.1002/nbm.891 https://onlinelibrary.wiley.com/doi/10.1002/nbm.891}, -volume = {17}, -year = {2004} -} -@article{Mikkelsen2018, -author = {Mikkelsen, Mark and Saleh, Muhammad G. and Near, Jamie and Chan, Kimberly L. and Gong, Tao and Harris, Ashley D. and Oeltzschner, Georg and Puts, Nicolaas A.J. and Cecil, Kim M. and Wilkinson, Iain D. and Edden, Richard A.E.}, -doi = {10.1002/mrm.27027}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Frequency and phase correction for multiplexed edited MRS of GABA and glutathione.pdf:pdf}, -issn = {07403194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {frequency,gaba,gsh,her-,mes,mrs,phase correction,spectral registration}, -month = {jul}, -number = {1}, -pages = {21--28}, -title = {{Frequency and phase correction for multiplexed edited MRS of GABA and glutathione}}, -url = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.27027}, -volume = {80}, -year = {2018} +number = {10}, +pages = {e4368}, +pmid = {32656879}, +title = {{Correcting frequency and phase offsets in MRS data using robust spectral registration}}, +url = {https://onlinelibrary.wiley.com/doi/10.1002/nbm.4368}, +volume = {33}, +year = {2020} } diff --git a/_site/data-quality-metrics.html b/_site/data-quality-metrics.html index 3cc8801..602e7f9 100644 --- a/_site/data-quality-metrics.html +++ b/_site/data-quality-metrics.html @@ -62,6 +62,28 @@ } + @@ -431,7 +453,7 @@

Data quality metrics

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

@@ -476,23 +498,24 @@

Last updated: January 24, 2024

Linewidth

Linewidth is calculated as the full-width half-maximum (FWHM) (in Hz) -of fitted signals. When reporting linewidths of datasets, you may use -the FWHM of Cr, NAA, or the water reference (if provided).

+of fitted model signals. When reporting linewidths of datasets, you may +choose to use the FWHM of Cr, NAA, or the water reference (if a water +reference is provided).

Signal-to-noise ratio (SNR)

-

The SNR of fitted signals is calculated as the amplitude of the given -modeled signal divided by twice the standard deviation of the noise -signal. To estimate noise, Gannet takes two independent segments of the -OFF or DIFF spectrum (as appropriate to the modeled signal of interest) -between 8–9 ppm and 9–10 ppm, and detrends them using a second-order -polynomial function. The standard deviation of each detrended noise -segment is then calculated. Detrending is required to remove baseline -artifacts (often related to the water signal). The smaller of the two -standard deviations is then used as the estimate of noise, which is then -multiplied by 2.

+

The SNR of fitted model signals is calculated as the amplitude of the +given modeled signal divided by twice the standard deviation of the +noise signal. To estimate the noise signal, Gannet takes two independent +segments of the OFF or DIFF spectrum (as appropriate to the modeled +signal of interest) between 8–9 ppm and 9–10 ppm, and detrends them +using a second-order polynomial function. The standard deviation of each +detrended noise segment is then calculated. Detrending is performed to +remove baseline artifacts (often related to the residual water signal). +The smaller of the two standard deviations is then used as the estimate +of noise, which is then multiplied by 2.

Formulaically, this is defined as:

\[ SNR_{signal} = \frac{A_{signal}}{2\cdot\mathrm{std}(noise)} @@ -528,7 +551,53 @@

Signal-to-noise ratio (SNR)Frequency offsets (frequency drift/motion)

-

[TEXT]

+

To estimate the degree of frequency offsets that result from +scanner-related frequency drift1 and participant motion2, +Gannet calculates the average frequency offset \(\overline{\Delta\delta_{0}}\)3. +This is calculated as the mean (over the course of the acquisition) +difference between the observed frequency of the residual water signal +in the pre-frequency-corrected subspectra and the nominal water +frequency \(\delta_{0}\) at 4.68 ppm +(4.8 ppm for room-temperature phantoms), or the nominal Cr frequency at +3.02 ppm for HERMES acquisitions. It should be noted that using the mean +of offset differences does not fully characterize frequency offsets but +is a useful heuristic.

+

\[ +\overline{\Delta\delta_{0}} = \frac{1}{m}\sum{\widehat{\delta_{0,m}} - +\delta_{0}} +\] where:

+ ++++ + + + + + + + + + + + + + + + + + + + + +
ParameterDefinition
\(m\)Each individual subspectrum index number
\(\widehat{\delta_{0,m}}\)Observed water or Cr frequency in each individual +subspectrum
\(\delta_{0}\)Nominal water or Cr frequency

Fit errorFit errorTissue segmentation

[TEXT]

+


+
+

References

+
+
+
1.
Hui +SCN, Mikkelsen M, Zöllner HJ, et al. Frequency +drift in MR spectroscopy at 3T. NeuroImage. +2021;241(21):118430. doi:10.1016/j.neuroimage.2021.118430
+
+
+
2.
Evans CJ, Puts NAJ, Robson SE, et al. Subtraction artifacts and frequency (Mis-)alignment in +J-difference GABA editing. Journal of Magnetic Resonance +Imaging. 2013;38(4):970-975. doi:10.1002/jmri.23923
+
+
+
3.
Mikkelsen M, Barker PB, Bhattacharyya PK, et +al. Big GABA: Edited MR spectroscopy at 24 research +sites. NeuroImage. 2017;159:32-45. doi:10.1016/j.neuroimage.2017.07.021
+
+
+
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+
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diff --git a/_site/faq.html b/_site/faq.html index 01b7dfa..28e555a 100644 --- a/_site/faq.html +++ b/_site/faq.html @@ -532,7 +532,7 @@

FAQ

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

@@ -586,7 +586,7 @@

General

-

Can Gannet process non-edited +

1. Can Gannet process non-edited data?

No, Gannet is designed to process edited single-voxel data only. @@ -597,14 +597,14 @@

Can Gannet process non-edited fractions.

-

Can Gannet process MRSI data?

+

2. Can Gannet process MRSI +data?

No.

-

Can Gannet process GSH-edited +

3. Can Gannet process GSH-edited data?

Yes, Gannet can process GABA-, GSH-, Lac-, and EtOH-edited MEGA-PRESS @@ -615,7 +615,7 @@

Can Gannet process GSH-edited

-

Can Gannet process HERMES/HERCULES +

4. Can Gannet process HERMES/HERCULES data?

GannetLoad.m can load and preprocess HERMES and HERCULES @@ -626,7 +626,7 @@

Can Gannet process HERMES/HERCULES

-

Can Gannet process phantom +

5. Can Gannet process phantom data?

Yes, Gannet can process and fit phantom edited data as described @@ -642,9 +642,9 @@

Output

-

Why is my spectrum upside down?

+

1. Why is my spectrum upside +down?

A screenshot example of upside-down MRS spectra

Gannet has a routine that automatically phases spectra so that they are not upside-down or otherwise out of phase. However, this routine can @@ -663,7 +663,7 @@

Errors -

You have issues with SPM MEX files on +

1. You have issues with SPM MEX files on macOS Catalina, Big Sur, Monterey, Ventura

@@ -679,8 +679,9 @@

You have issues with SPM MEX files on

-

MATLAB says my structural image does not -exist2. MATLAB says my structural image does +not exist

For example:

MATLAB says my structural image does not
-
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+
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

diff --git a/_site/gannetpreinitialise-settings.html b/_site/gannetpreinitialise-settings.html index e8b3d96..54c5ec9 100644 --- a/_site/gannetpreinitialise-settings.html +++ b/_site/gannetpreinitialise-settings.html @@ -450,7 +450,7 @@

GannetPreInitialise settings

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

diff --git a/_site/getting-started.html b/_site/getting-started.html index 81b9e00..a497e6f 100644 --- a/_site/getting-started.html +++ b/_site/getting-started.html @@ -532,7 +532,7 @@

Getting started

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

diff --git a/_site/index.html b/_site/index.html index 0501878..b6222b9 100644 --- a/_site/index.html +++ b/_site/index.html @@ -450,7 +450,7 @@

Gannet

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

@@ -552,13 +552,13 @@

DownloadAlternatively, Git users can clone the Gannet repository into a directory of their choice:

git clone https://github.com/markmikkelsen/Gannet.git
+class="sourceCode default">git clone https://github.com/markmikkelsen/Gannet.git

The development version of Gannet can be downloaded from the development branch on GitHub or by using the following git command if the repository was cloned:

git checkout dev
+class="sourceCode default">git checkout dev

Stable releases can be found here.

@@ -769,7 +769,7 @@

Acknowledgments -
---
title: "Gannet"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
output: html_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, child = "js/back-to-top.js"}
```

```{css, echo = FALSE}
/* This enables wrapping of code blocks */
pre.sourceCode code {
  white-space: pre-wrap;
}
```

<br>

<img id="img_50" src="images/logo+title.png" alt="The Gannet logo"/>

::: release
<h3>Current stable release: <a href="https://github.com/markmikkelsen/Gannet" target="_blank" class="badge badge-secondary">3.3.2</a></h3>
:::

## Overview

Gannet is a free, open-source MATLAB-based software toolkit for analyzing edited single-voxel ^1^H magnetic resonance spectroscopy (MRS) data. Its largely automated functions cover all the essential steps of modern MRS analysis:

-   Loading raw data
-   Substantial preprocessing
-   Signal modeling
-   Voxel co-registration with structural MR images
-   Concentration estimation based on tissue composition

Several existing software packages for MRS data analysis require substantial user input or offer a wide selection of processing options. In contrast, the philosophy behind Gannet is to provide users with a complete automated pipeline without the need for significant user input.

Additionally, as open-source software, advanced users have the ability to modify the underlying routines for ad hoc purposes.

## Installation

### Prerequisites

Gannet runs in <a href="https://mathworks.com/products/matlab.html" target="_blank">MATLAB</a>. For best performance, we recommend using the latest release if possible. Additionally, Gannet requires that the following MATLAB toolboxes are installed:

-   Image Processing
-   Optimization
-   Signal Processing
-   Statistics and Machine Learning

You can check which toolboxes you have installed by typing `ver` in the MATLAB command window. To install any missing toolboxes, please follow these <a href="https://mathworks.com/matlabcentral/answers/101885-how-do-i-install-additional-toolboxes-into-an-existing-installation-of-matlab" target="_blank">instructions</a>.

To run the voxel co-registration and structural image segmentation modules, <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank">SPM12</a> must be installed.

### Download

The simplest way to install Gannet is to download the latest source code as a ZIP file from the <a href="https://github.com/markmikkelsen/Gannet" target="_blank">GitHub repository</a> and move the `Gannet-main/` directory into your main MATLAB directory (or wherever else you wish).

Alternatively, Git users can clone the Gannet repository into a directory of their choice:

```{bash, eval = FALSE}
git clone https://github.com/markmikkelsen/Gannet.git
```

The development version of Gannet can be downloaded from the <a href="https://github.com/markmikkelsen/Gannet/tree/dev" target="_blank">development branch</a> on GitHub or by using the following git command if the repository was cloned:

```{bash, eval = FALSE}
git checkout dev
```

Stable releases can be found <a href="https://github.com/markmikkelsen/Gannet/releases" target="_blank">here</a>.

### Setup

Open the *Set Path* dialog box from the MATLAB menu (or run the command `pathtool` in the Command Window), click *Add with Subfolders...*, find the downloaded Gannet directory, and then select it. When done, press *Save* to permanently save the Gannet directory to MATLAB's default search path.

SPM12 can be installed in the same manner after it has been downloaded from the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank">SPM</a> website.

::: info
<i class="fa fa-info-circle" style="color: white"></i> It is highly recommended that you only add the main SPM12 directory (`spm12/`) to your search path instead of including all the subdirectories. This prevents function conflicts.
:::

::: warning
<i class="fa fa-exclamation-circle" style="color: white"></i> If you have <a href="https://github.com/schorschinho/osprey" target="_blank" style="color:white"><u>Osprey</u></a> also installed on your computer, please ensure that you either have all the Gannet and SPM12 directories at the top of your search path or have removed the Osprey directories from your search path. Gannet and Osprey share several functions that can lead to conflicts that result in Gannet not functioning correctly.
:::

## Compatibility

Gannet is currently being developed in MATLAB R2023b in macOS 14 Sonoma (Apple silicon). While reasonable effort is made to ensure legacy and cross-OS compatibility, an error-free user experience is not guaranteed.

## Supported file formats

At present, the following MRS data file formats are supported:

- DICOM (.dcm)
- GE P-file (.7)
- NIfTI-MRS (.nii[.gz])
- Philips .data/.list
- Philips .raw
- Philips .sdat/.spar
- Siemens DICOM (.ima)
- Siemens .rda
- Siemens TWIX (.dat)

For creating and co-registering voxel masks, structural images need to be in NIfTI format (DICOM files can also be used if processing GE P-files).

::: warning
<i class="fa fa-exclamation-circle" style="color: white"></i> Philips users: Do not use structural images exported using the *fsl-nifti* option as this creates problems with co-registration in Gannet.
:::

## Getting help

If you encounter any problems, please first check the Documentation or <a href="https://markmikkelsen.github.io/Gannet-docs/faq.html">FAQ</a> for a solution.

Otherwise, you can post your query on the <a href="https://forum.mrshub.org/c/mrs-software/gannet/9" target="_blank">Gannet forum</a> on <a href="https://mrshub.org/" target="_blank">MRSHub</a>.

The Gannet team can also be <a href="mailto:mam4041@med.cornell.edu?subject=[Gannet]%20<Please enter the subject of your query here>" target="_blank">contacted</a> directly. We will do our best to work with you to solve your issue.

## Versioning

Gannet uses a form of <a href="https://semver.org/" target="_blank">semantic versioning</a> in the style 'x.x.x' to mark code releases. Versioning is also conducted on a module-specific basis using the style 'YYMMDD'. That is, each Gannet module has its own release version. Users should note that module-specific versions sometimes are updated despite the semantic version number remaining unchanged (typically for minor updates/bug fixes).

## Developers

- Richard Edden (Johns Hopkins University) - creator
- Mark Mikkelsen (Weill Cornell Medicine) - lead developer
- Georg Oeltzschner (Johns Hopkins University) - contributor
- Muhammad Saleh (Children's Hospital of Philadelphia) - contributor
- C. John Evans (Cardiff University) - contributor
- Ashley Harris (University of Calgary) - contributor
- Nicolaas Puts (King's College London) - contributor

## License and citing Gannet

This software is licensed under an open-source <a href="https://markmikkelsen.github.io/Gannet-docs/gannet-license.html">BSD-3-Clause License</a>. Should you disseminate material that made use of Gannet, please cite the following publications, as appropriate:

- Edden RAE, Puts NAJ, Harris AD, Barker PB, Evans CJ. <a href="https://doi.org/10.1002/jmri.24478" target="_blank">Gannet: A batch-processing tool for the quantitative analysis of gamma-aminobutyric acid-edited MR spectroscopy spectra.</a> *Journal of Magnetic Resonance Imaging*. 2014;40(6):1445--1452

**If you perform frequency-and-phase correction (FPC) using:**

Robust spectral registration (`RobustSpecReg`):

- Mikkelsen M, Tapper S, Near J, Mostofsky SH, Puts NAJ, Edden RAE. <a href="https://doi.org/10.1002/nbm.4368" target="_blank">Correcting frequency and phase offsets in MRS data using robust spectral registration.</a> *NMR in Biomedicine*. 2020;33(10):e4368

multi-step FPC (`SpecRegHERMES`):

- Mikkelsen M, Saleh MG, Near J, et al. <a href="https://doi.org/10.1002/mrm.27027" target="_blank">Frequency and phase correction for multiplexed edited MRS of GABA and glutathione.</a> *Magnetic Resonance in Medicine*. 2018;80(1):21--28

or spectral registration (`SpecReg`):

- Near J, Edden R, Evans CJ, Paquin R, Harris A, Jezzard P. <a href="https://doi.org/10.1002/mrm.25094" target="_blank">Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.</a> *Magnetic Resonance in Medicine*. 2015;73(1):44--50

**If you perform voxel co-registration:**

- Please acknowledge the use of SPM12. You may also link/cite the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank">SPM12 website</a>.

**If you perform tissue segmentation:**

- Ashburner J, Friston KJ. <a href="https://doi.org/10.1016/j.neuroimage.2005.02.018" target="_blank">Unified segmentation.</a> *NeuroImage*. 2005;26(3):839--851

**If you report water-referenced, tissue-corrected metabolite measurements using:**

The Harris et al. method:

- Harris AD, Puts NAJ, Edden RAE. <a href="https://doi.org/10.1002/jmri.24903" target="_blank">Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations.</a> *Journal of Magnetic Resonance Imaging*. 2015;42(5):1431--1440

or the Gasparovic et al. method:

- Gasparovic C, Song T, Devier D, et al. <a href="https://doi.org/10.1002/mrm.20901" target="_blank">Use of tissue water as a concentration reference for proton spectroscopic imaging.</a> *Magnetic Resonance in Medicine*. 2006;55(6):1219--1226

## Acknowledgments

The development and dissemination of Gannet has been supported by the following NIH grants:

- R01 EB016089
- R01 EB023963
- P41 EB015909
- K99 EB028828
- R01 MH106564
- R21 MH098228
- R21 NS077300
- R01 MH096263

We wish to thank the following individuals for their direct or indirect contributions:

- Yair Altman (Undocumented Matlab)
- Peter Barker (Johns Hopkins University)
- Alex Craven (University of Bergen)
- Philipp Ehses (Max Planck Institute for Biological Cybernetics)
- Robin de Graaf (Yale School of Medicine)
- Xiangrui Li (Ohio State University)
- Jamie Near (McGill University)
- Ralph Noeske (GE Healthcare)
- Wouter Potters (UMC Amsterdam)
- Jan Simon (Heidelberg)




+
---
title: "Gannet"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
output: html_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, child = "js/back-to-top.js"}
```

```{css, echo = FALSE}
/* This enables wrapping of code blocks */
pre.sourceCode code {
  white-space: pre-wrap;
}
```

<br>

<img id="img_50" src="images/logo+title.png" alt="The Gannet logo"/>

::: release
<h3>Current stable release: <a href="https://github.com/markmikkelsen/Gannet" target="_blank" class="badge badge-secondary">3.3.2</a></h3>
:::

## Overview

Gannet is a free, open-source MATLAB-based software toolkit for analyzing edited single-voxel ^1^H magnetic resonance spectroscopy (MRS) data. Its largely automated functions cover all the essential steps of modern MRS analysis:

-   Loading raw data
-   Substantial preprocessing
-   Signal modeling
-   Voxel co-registration with structural MR images
-   Concentration estimation based on tissue composition

Several existing software packages for MRS data analysis require substantial user input or offer a wide selection of processing options. In contrast, the philosophy behind Gannet is to provide users with a complete automated pipeline without the need for significant user input.

Additionally, as open-source software, advanced users have the ability to modify the underlying routines for ad hoc purposes.

## Installation

### Prerequisites

Gannet runs in <a href="https://mathworks.com/products/matlab.html" target="_blank">MATLAB</a>. For best performance, we recommend using the latest release if possible. Additionally, Gannet requires that the following MATLAB toolboxes are installed:

-   Image Processing
-   Optimization
-   Signal Processing
-   Statistics and Machine Learning

You can check which toolboxes you have installed by typing `ver` in the MATLAB command window. To install any missing toolboxes, please follow these <a href="https://mathworks.com/matlabcentral/answers/101885-how-do-i-install-additional-toolboxes-into-an-existing-installation-of-matlab" target="_blank">instructions</a>.

To run the voxel co-registration and structural image segmentation modules, <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank">SPM12</a> must be installed.

### Download

The simplest way to install Gannet is to download the latest source code as a ZIP file from the <a href="https://github.com/markmikkelsen/Gannet" target="_blank">GitHub repository</a> and move the `Gannet-main/` directory into your main MATLAB directory (or wherever else you wish).

Alternatively, Git users can clone the Gannet repository into a directory of their choice:

```{verbatim, eval = FALSE}
git clone https://github.com/markmikkelsen/Gannet.git
```

The development version of Gannet can be downloaded from the <a href="https://github.com/markmikkelsen/Gannet/tree/dev" target="_blank">development branch</a> on GitHub or by using the following git command if the repository was cloned:

```{verbatim, eval = FALSE}
git checkout dev
```

Stable releases can be found <a href="https://github.com/markmikkelsen/Gannet/releases" target="_blank">here</a>.

### Setup

Open the *Set Path* dialog box from the MATLAB menu (or run the command `pathtool` in the Command Window), click *Add with Subfolders...*, find the downloaded Gannet directory, and then select it. When done, press *Save* to permanently save the Gannet directory to MATLAB's default search path.

SPM12 can be installed in the same manner after it has been downloaded from the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank">SPM</a> website.

::: info
<i class="fa fa-info-circle" style="color: white"></i> It is highly recommended that you only add the main SPM12 directory (`spm12/`) to your search path instead of including all the subdirectories. This prevents function conflicts.
:::

::: warning
<i class="fa fa-exclamation-circle" style="color: white"></i> If you have <a href="https://github.com/schorschinho/osprey" target="_blank" style="color:white"><u>Osprey</u></a> also installed on your computer, please ensure that you either have all the Gannet and SPM12 directories at the top of your search path or have removed the Osprey directories from your search path. Gannet and Osprey share several functions that can lead to conflicts that result in Gannet not functioning correctly.
:::

## Compatibility

Gannet is currently being developed in MATLAB R2023b in macOS 14 Sonoma (Apple silicon). While reasonable effort is made to ensure legacy and cross-OS compatibility, an error-free user experience is not guaranteed.

## Supported file formats

At present, the following MRS data file formats are supported:

- DICOM (.dcm)
- GE P-file (.7)
- NIfTI-MRS (.nii[.gz])
- Philips .data/.list
- Philips .raw
- Philips .sdat/.spar
- Siemens DICOM (.ima)
- Siemens .rda
- Siemens TWIX (.dat)

For creating and co-registering voxel masks, structural images need to be in NIfTI format (DICOM files can also be used if processing GE P-files).

::: warning
<i class="fa fa-exclamation-circle" style="color: white"></i> Philips users: Do not use structural images exported using the *fsl-nifti* option as this creates problems with co-registration in Gannet.
:::

## Getting help

If you encounter any problems, please first check the Documentation or <a href="https://markmikkelsen.github.io/Gannet-docs/faq.html">FAQ</a> for a solution.

Otherwise, you can post your query on the <a href="https://forum.mrshub.org/c/mrs-software/gannet/9" target="_blank">Gannet forum</a> on <a href="https://mrshub.org/" target="_blank">MRSHub</a>.

The Gannet team can also be <a href="mailto:mam4041@med.cornell.edu?subject=[Gannet]%20<Please enter the subject of your query here>" target="_blank">contacted</a> directly. We will do our best to work with you to solve your issue.

## Versioning

Gannet uses a form of <a href="https://semver.org/" target="_blank">semantic versioning</a> in the style 'x.x.x' to mark code releases. Versioning is also conducted on a module-specific basis using the style 'YYMMDD'. That is, each Gannet module has its own release version. Users should note that module-specific versions sometimes are updated despite the semantic version number remaining unchanged (typically for minor updates/bug fixes).

## Developers

- Richard Edden (Johns Hopkins University) - creator
- Mark Mikkelsen (Weill Cornell Medicine) - lead developer
- Georg Oeltzschner (Johns Hopkins University) - contributor
- Muhammad Saleh (Children's Hospital of Philadelphia) - contributor
- C. John Evans (Cardiff University) - contributor
- Ashley Harris (University of Calgary) - contributor
- Nicolaas Puts (King's College London) - contributor

## License and citing Gannet

This software is licensed under an open-source <a href="https://markmikkelsen.github.io/Gannet-docs/gannet-license.html">BSD-3-Clause License</a>. Should you disseminate material that made use of Gannet, please cite the following publications, as appropriate:

- Edden RAE, Puts NAJ, Harris AD, Barker PB, Evans CJ. <a href="https://doi.org/10.1002/jmri.24478" target="_blank">Gannet: A batch-processing tool for the quantitative analysis of gamma-aminobutyric acid-edited MR spectroscopy spectra.</a> *Journal of Magnetic Resonance Imaging*. 2014;40(6):1445--1452

**If you perform frequency-and-phase correction (FPC) using:**

Robust spectral registration (`RobustSpecReg`):

- Mikkelsen M, Tapper S, Near J, Mostofsky SH, Puts NAJ, Edden RAE. <a href="https://doi.org/10.1002/nbm.4368" target="_blank">Correcting frequency and phase offsets in MRS data using robust spectral registration.</a> *NMR in Biomedicine*. 2020;33(10):e4368

multi-step FPC (`SpecRegHERMES`):

- Mikkelsen M, Saleh MG, Near J, et al. <a href="https://doi.org/10.1002/mrm.27027" target="_blank">Frequency and phase correction for multiplexed edited MRS of GABA and glutathione.</a> *Magnetic Resonance in Medicine*. 2018;80(1):21--28

or spectral registration (`SpecReg`):

- Near J, Edden R, Evans CJ, Paquin R, Harris A, Jezzard P. <a href="https://doi.org/10.1002/mrm.25094" target="_blank">Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.</a> *Magnetic Resonance in Medicine*. 2015;73(1):44--50

**If you perform voxel co-registration:**

- Please acknowledge the use of SPM12. You may also link/cite the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank">SPM12 website</a>.

**If you perform tissue segmentation:**

- Ashburner J, Friston KJ. <a href="https://doi.org/10.1016/j.neuroimage.2005.02.018" target="_blank">Unified segmentation.</a> *NeuroImage*. 2005;26(3):839--851

**If you report water-referenced, tissue-corrected metabolite measurements using:**

The Harris et al. method:

- Harris AD, Puts NAJ, Edden RAE. <a href="https://doi.org/10.1002/jmri.24903" target="_blank">Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations.</a> *Journal of Magnetic Resonance Imaging*. 2015;42(5):1431--1440

or the Gasparovic et al. method:

- Gasparovic C, Song T, Devier D, et al. <a href="https://doi.org/10.1002/mrm.20901" target="_blank">Use of tissue water as a concentration reference for proton spectroscopic imaging.</a> *Magnetic Resonance in Medicine*. 2006;55(6):1219--1226

## Acknowledgments

The development and dissemination of Gannet has been supported by the following NIH grants:

- R01 EB016089
- R01 EB023963
- P41 EB015909
- K99 EB028828
- R01 MH106564
- R21 MH098228
- R21 NS077300
- R01 MH096263

We wish to thank the following individuals for their direct or indirect contributions:

- Yair Altman (Undocumented Matlab)
- Peter Barker (Johns Hopkins University)
- Alex Craven (University of Bergen)
- Philipp Ehses (Max Planck Institute for Biological Cybernetics)
- Robin de Graaf (Yale School of Medicine)
- Xiangrui Li (Ohio State University)
- Jamie Near (McGill University)
- Ralph Noeske (GE Healthcare)
- Wouter Potters (UMC Amsterdam)
- Jan Simon (Heidelberg)





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+
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diff --git a/_site/quantification-tissue-correction.html b/_site/quantification-tissue-correction.html index e7a0a52..d269436 100644 --- a/_site/quantification-tissue-correction.html +++ b/_site/quantification-tissue-correction.html @@ -453,7 +453,7 @@

Quantification & tissue correction

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

@@ -921,7 +921,7 @@

References -
---
title: "Quantification & tissue correction"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
bibliography: references.bib
csl: american-medical-association.csl
link-citations: yes
output:
  html_document:
    toc: TRUE
    toc_depth: 2
    toc_float:
      collapsed: FALSE
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, child = "js/back-to-top.js"}
```

```{css, echo = FALSE}
.info {
  margin-bottom: 20px;
}

table {
  margin: auto;
}

table thead th {
  border-bottom: 1px solid #ddd;
}

th, td {
  padding: 5px;
}

tfoot, tr:nth-child(even) {
  background: #eee;
}
```

<br>

::: info
<i class="fa fa-info-circle" style="color: white"></i>&nbsp; The formulas below apply to releases 3.1+ and may not be applicable to older releases.
:::

Gannet quantifies metabolite levels in different ways at various points in the pipeline depending on what reference signals are present and if structural image data are available. It is important to understand what the underlying quantification formulas are for the interpretation and reporting of results.

<u>A note on units of concentration</u>

There is a perennial issue surrounding how quantified in vivo MRS measurements should be expressed; that is, in what biochemical units (if any) they should be reported. In vivo MRS measurements can be, and have been, reported in molar, molal, and institutional units or as simple ratios. However, this makes interpreting and comparing such values, especially across published studies, highly challenging. While Gannet does apply a number of signal scaling factors to water-referenced metabolite measurements (that are required when reporting absolute concentrations), it is our philosophy that these measurements should be considered pseudo-absolute because accounting for all empirical signal scaling factors is impractical (even impossible) to permit truly absolute quantification. Thus, we denote our water-referenced metabolite measurements in institutional units (i.u.).

## GannetFit

`MRS_struct.out.vox1.(metab).ConcCr`, `MRS_struct.out.vox1.(metab).ConcCho`, and `MRS_struct.out.vox1.(metab).ConcNAA` are simple signal integral ratios of the metabolite of interest and the metabolite reference signals Cr, Cho, and NAA, respectively:

$$C = \frac{I_{M}}{I_{ref}}$$

If a water reference is provided, `MRS_struct.out.vox1.(metab).ConcIU` is also calculated. It is defined as the signal integral ratio of the metabolite of interest and water reference scaled by a number of global signal scaling factors:

$$
C_{corr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
C_{W}\cdot
W_{vis}\cdot
\frac{\exp\left(-\frac{TE_{W}}{T_{2W}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}
$$

where:

| <u>Parameter</u> | <u>Description</u> | <u>Default value</u> |
| :- | :-------- | :------ |
| $C_{corr}$ | Estimated metabolite concentration in i.u. | |
| $I_{M}$ | Metabolite signal integral | |
| $I_{W}$ | Water signal integral | |
| $H_{M}$ | Number of ^1^H protons that give rise to the metabolite signal | Metabolite dependent; see `GannetFit.m` for default values |
| $H_{W}$ | Number of ^1^H protons that give rise to the water signal | 2 |
| $MM$ | Correction factor for the contribution of the co-edited macromolecule signal in the metabolite signal | 0.45 for GABA editing and 1 for all other edited metabolites |
| $\kappa$ | Editing efficiency | Acquisition dependent; 0.5 for GABA editing |
| $C_{W}$ | Molal concentration of pure water | 55.51 mol/kg |
| $W_{vis}$ | Approximate relative MR visibility of water in brain tissue | 0.65 [@Ernst1993] |
| $TE_{W}$ | Echo time of the water reference acquisition | Acquisition dependent |
| $TR_{W}$ | Repetition time of the water reference acquisition | Acquisition dependent |
| $TE_{M}$ | Echo time of the metabolite acquisition | Acquisition dependent |
| $TR_{M}$ | Repetition time of the metabolite acquisition | Acquisition dependent |
| $T_{2W}$ | Average transverse relaxation time of water in GM and WM | 1.100 s [@Wansapura1999] |
| $T_{1W}$ | Average longitudinal relaxation time of water in GM and WM | 0.095 s [@Wansapura1999] |
| $T_{2M}$ | Transverse relaxation time of metabolite | Metabolite dependent; see `GannetFit.m` for default values |
| $T_{1M}$ | Longitudinal relaxation time of metabolite | Metabolite dependent; see `GannetFit.m` for default values |

## GannetSegment

When segmenting structural images to obtain voxel volume fractions of GM, WM, and CSF, and if a water reference is available, a CSF-only correction is applied to the `ConcIU` measurement.

`MRS_struct.out.vox1.(metab).ConcIU_CSFcorr`:

$$
C_{CSFcorr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
C_{W}\cdot
W_{vis}\cdot
\frac{\exp\left(-\frac{TE_{W}}{T_{2W}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}\cdot
\frac{1}{1-f_{CSF}}
$$

where $C_{CSFcorr}$ is the estimated metabolite concentration in i.u. corrected for CSF and $f_{CSF}$ is the voxel volume fraction of CSF.

## GannetQuantify

`GannetQuantify.m` goes a step further and corrects for partial volume effects that attenuate the observed water and metabolite signals. There are two approaches that are employed. The first is termed the Gasparovic et al. [@Gasparovic2006] method and the second is termed the Harris et al. [@Harris2015] method. Although similar, the difference between these approaches is that the Harris et al. method additionally accounts for intrinsic differences in metabolite concentrations in GM and WM. 

### <u>The Gasparovic et al. method</u>

`MRS_struct.out.vox1.(metab).ConcIU_TissCorr`:

$$
C_{TissCorr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
C_{W}\cdot
\frac{\sum_{i}^{GM,WM,CSF}f_{i}\beta_{i}\exp\left(-\frac{TE_{W}}{T_{2W,i}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W,i}}\right)\right]}
{(1-f_{CSF})\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}
$$

where:

| <u>Parameter</u> | <u>Description</u> | <u>Default value</u> |
| :- | :-------- | :------ |
| $C_{TissCorr}$ | Estimated metabolite concentration in i.u. corrected for partial volume effects of water | |
| $f_{i}$ | Voxel volume fraction of GM, WM, or CSF | |
| $\beta_i$ | Relative MR visibility of water in GM, WM, or CSF | 0.78, 0.65, and 0.97 [@Ernst1993] |
| $T_{2W,i}$ | Transverse relaxation time of water in GM, WM, or CSF | 0.110, 0.0792, and 0.503 s [@Wansapura1999; @Piechnik2009] |
| $T_{1W,i}$ | Longitudinal relaxation time of water in GM, WM, or CSF | 1.331, 0.832, and 3.817 s [@Wansapura1999; @Lu2005] |

### <u>The Harris et al. method</u>

`MRS_struct.out.vox1.(metab).ConcIU_AlphaTissCorr`:

$$
C_{AlphaCorr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
\frac{\sum_{i}^{GM,WM,CSF}f_{i}C_{W,i}\exp\left(-\frac{TE_{W}}{T_{2W,i}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W,i}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}\cdot
\frac{1}{f_{GM}+\alpha{f_{WM}}}
$$

where:

| <u>Parameter</u> | <u>Description</u> | <u>Default value</u> |
| :- | :-------- | :------ |
| $C_{AlphaCorr}$ | Estimated metabolite concentration in i.u. corrected for partial volume effects of water and metabolite | |
| $C_{W,i}$ | Molal concentration of water in GM, WM, or CSF | 43.30, 36.08, and 53.84 mol/kg [@Ernst1993; @Gasparovic2006] |
| $\alpha$ | Ratio of intrinsic WM:GM metabolite concentrations | Metabolite dependent; see `GannetQuantify.m` for default values |

A modification of the Harris et al. method is also calculated where `ConcIU_AlphaTissCorr` is further scaled by the average voxel GM and WM composition of all datasets in a group.

`MRS_struct.out.vox1.(metab).ConcIU_AlphaTissCorr_GrpNorm`:

$$
C_{AlphaCorrNorm} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
\frac{\sum_{i}^{GM,WM,CSF}f_{i}C_{W,i}\exp\left(-\frac{TE_{W}}{T_{2W,i}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W,i}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}\cdot
\frac{\mu_{GM}+\alpha{\mu_{WM}}}{(f_{GM}+\alpha{f_{WM}})(\mu_{GM}+{\mu_{WM}})}
$$

where:

| <u>Parameter</u> | <u>Description</u> |
| :- | :-------- |
| $C_{AlphaCorrNorm}$ | Estimated metabolite concentration in i.u. corrected for partial volume effects of water and metabolite and adjusted to the average voxel GM and WM composition across a group |
| ${\mu_{GM}}$ | Group-averaged voxel volume fraction of GM |
| ${\mu_{WM}}$ | Group-averaged voxel volume fraction of WM |

<br>

### References



+
---
title: "Quantification & tissue correction"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
bibliography: bibliography.bib
csl: american-medical-association.csl
link-citations: yes
output:
  html_document:
    toc: TRUE
    toc_depth: 2
    toc_float:
      collapsed: FALSE
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, child = "js/back-to-top.js"}
```

```{css, echo = FALSE}
.info {
  margin-bottom: 20px;
}

table {
  margin: auto;
}

table thead th {
  border-bottom: 1px solid #ddd;
}

th, td {
  padding: 5px;
}

tfoot, tr:nth-child(even) {
  background: #eee;
}
```

<br>

::: info
<i class="fa fa-info-circle" style="color: white"></i>&nbsp; The formulas below apply to releases 3.1+ and may not be applicable to older releases.
:::

Gannet quantifies metabolite levels in different ways at various points in the pipeline depending on what reference signals are present and if structural image data are available. It is important to understand what the underlying quantification formulas are for the interpretation and reporting of results.

<u>A note on units of concentration</u>

There is a perennial issue surrounding how quantified in vivo MRS measurements should be expressed; that is, in what biochemical units (if any) they should be reported. In vivo MRS measurements can be, and have been, reported in molar, molal, and institutional units or as simple ratios. However, this makes interpreting and comparing such values, especially across published studies, highly challenging. While Gannet does apply a number of signal scaling factors to water-referenced metabolite measurements (that are required when reporting absolute concentrations), it is our philosophy that these measurements should be considered pseudo-absolute because accounting for all empirical signal scaling factors is impractical (even impossible) to permit truly absolute quantification. Thus, we denote our water-referenced metabolite measurements in institutional units (i.u.).

## GannetFit

`MRS_struct.out.vox1.(metab).ConcCr`, `MRS_struct.out.vox1.(metab).ConcCho`, and `MRS_struct.out.vox1.(metab).ConcNAA` are simple signal integral ratios of the metabolite of interest and the metabolite reference signals Cr, Cho, and NAA, respectively:

$$C = \frac{I_{M}}{I_{ref}}$$

If a water reference is provided, `MRS_struct.out.vox1.(metab).ConcIU` is also calculated. It is defined as the signal integral ratio of the metabolite of interest and water reference scaled by a number of global signal scaling factors:

$$
C_{corr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
C_{W}\cdot
W_{vis}\cdot
\frac{\exp\left(-\frac{TE_{W}}{T_{2W}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}
$$

where:

| <u>Parameter</u> | <u>Description</u> | <u>Default value</u> |
| :- | :-------- | :------ |
| $C_{corr}$ | Estimated metabolite concentration in i.u. | |
| $I_{M}$ | Metabolite signal integral | |
| $I_{W}$ | Water signal integral | |
| $H_{M}$ | Number of ^1^H protons that give rise to the metabolite signal | Metabolite dependent; see `GannetFit.m` for default values |
| $H_{W}$ | Number of ^1^H protons that give rise to the water signal | 2 |
| $MM$ | Correction factor for the contribution of the co-edited macromolecule signal in the metabolite signal | 0.45 for GABA editing and 1 for all other edited metabolites |
| $\kappa$ | Editing efficiency | Acquisition dependent; 0.5 for GABA editing |
| $C_{W}$ | Molal concentration of pure water | 55.51 mol/kg |
| $W_{vis}$ | Approximate relative MR visibility of water in brain tissue | 0.65 [@Ernst1993] |
| $TE_{W}$ | Echo time of the water reference acquisition | Acquisition dependent |
| $TR_{W}$ | Repetition time of the water reference acquisition | Acquisition dependent |
| $TE_{M}$ | Echo time of the metabolite acquisition | Acquisition dependent |
| $TR_{M}$ | Repetition time of the metabolite acquisition | Acquisition dependent |
| $T_{2W}$ | Average transverse relaxation time of water in GM and WM | 1.100 s [@Wansapura1999] |
| $T_{1W}$ | Average longitudinal relaxation time of water in GM and WM | 0.095 s [@Wansapura1999] |
| $T_{2M}$ | Transverse relaxation time of metabolite | Metabolite dependent; see `GannetFit.m` for default values |
| $T_{1M}$ | Longitudinal relaxation time of metabolite | Metabolite dependent; see `GannetFit.m` for default values |

## GannetSegment

When segmenting structural images to obtain voxel volume fractions of GM, WM, and CSF, and if a water reference is available, a CSF-only correction is applied to the `ConcIU` measurement.

`MRS_struct.out.vox1.(metab).ConcIU_CSFcorr`:

$$
C_{CSFcorr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
C_{W}\cdot
W_{vis}\cdot
\frac{\exp\left(-\frac{TE_{W}}{T_{2W}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}\cdot
\frac{1}{1-f_{CSF}}
$$

where $C_{CSFcorr}$ is the estimated metabolite concentration in i.u. corrected for CSF and $f_{CSF}$ is the voxel volume fraction of CSF.

## GannetQuantify

`GannetQuantify.m` goes a step further and corrects for partial volume effects that attenuate the observed water and metabolite signals. There are two approaches that are employed. The first is termed the Gasparovic et al. [@Gasparovic2006] method and the second is termed the Harris et al. [@Harris2015] method. Although similar, the difference between these approaches is that the Harris et al. method additionally accounts for intrinsic differences in metabolite concentrations in GM and WM. 

### <u>The Gasparovic et al. method</u>

`MRS_struct.out.vox1.(metab).ConcIU_TissCorr`:

$$
C_{TissCorr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
C_{W}\cdot
\frac{\sum_{i}^{GM,WM,CSF}f_{i}\beta_{i}\exp\left(-\frac{TE_{W}}{T_{2W,i}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W,i}}\right)\right]}
{(1-f_{CSF})\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}
$$

where:

| <u>Parameter</u> | <u>Description</u> | <u>Default value</u> |
| :- | :-------- | :------ |
| $C_{TissCorr}$ | Estimated metabolite concentration in i.u. corrected for partial volume effects of water | |
| $f_{i}$ | Voxel volume fraction of GM, WM, or CSF | |
| $\beta_i$ | Relative MR visibility of water in GM, WM, or CSF | 0.78, 0.65, and 0.97 [@Ernst1993] |
| $T_{2W,i}$ | Transverse relaxation time of water in GM, WM, or CSF | 0.110, 0.0792, and 0.503 s [@Wansapura1999; @Piechnik2009] |
| $T_{1W,i}$ | Longitudinal relaxation time of water in GM, WM, or CSF | 1.331, 0.832, and 3.817 s [@Wansapura1999; @Lu2005] |

### <u>The Harris et al. method</u>

`MRS_struct.out.vox1.(metab).ConcIU_AlphaTissCorr`:

$$
C_{AlphaCorr} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
\frac{\sum_{i}^{GM,WM,CSF}f_{i}C_{W,i}\exp\left(-\frac{TE_{W}}{T_{2W,i}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W,i}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}\cdot
\frac{1}{f_{GM}+\alpha{f_{WM}}}
$$

where:

| <u>Parameter</u> | <u>Description</u> | <u>Default value</u> |
| :- | :-------- | :------ |
| $C_{AlphaCorr}$ | Estimated metabolite concentration in i.u. corrected for partial volume effects of water and metabolite | |
| $C_{W,i}$ | Molal concentration of water in GM, WM, or CSF | 43.30, 36.08, and 53.84 mol/kg [@Ernst1993; @Gasparovic2006] |
| $\alpha$ | Ratio of intrinsic WM:GM metabolite concentrations | Metabolite dependent; see `GannetQuantify.m` for default values |

A modification of the Harris et al. method is also calculated where `ConcIU_AlphaTissCorr` is further scaled by the average voxel GM and WM composition of all datasets in a group.

`MRS_struct.out.vox1.(metab).ConcIU_AlphaTissCorr_GrpNorm`:

$$
C_{AlphaCorrNorm} =
\frac{I_{M}}{I_{W}}\cdot
\frac{H_{W}}{H_{M}}\cdot
\frac{MM}{\kappa}\cdot
\frac{\sum_{i}^{GM,WM,CSF}f_{i}C_{W,i}\exp\left(-\frac{TE_{W}}{T_{2W,i}}\right)\left[1-\exp\left(-\frac{TR_{W}}{T_{1W,i}}\right)\right]}
{\exp\left(-\frac{TE_{M}}{T_{2M}}\right)\left[1-\exp\left(-\frac{TR_{M}}{T_{1M}}\right)\right]}\cdot
\frac{\mu_{GM}+\alpha{\mu_{WM}}}{(f_{GM}+\alpha{f_{WM}})(\mu_{GM}+{\mu_{WM}})}
$$

where:

| <u>Parameter</u> | <u>Description</u> |
| :- | :-------- |
| $C_{AlphaCorrNorm}$ | Estimated metabolite concentration in i.u. corrected for partial volume effects of water and metabolite and adjusted to the average voxel GM and WM composition across a group |
| ${\mu_{GM}}$ | Group-averaged voxel volume fraction of GM |
| ${\mu_{WM}}$ | Group-averaged voxel volume fraction of WM |

<br>

### References




diff --git a/_site/reference.html b/_site/reference.html index 12d2173..cb9cdcd 100644 --- a/_site/reference.html +++ b/_site/reference.html @@ -554,7 +554,7 @@

Reference

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

@@ -983,7 +983,7 @@

References -
---
title: "Reference"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
output:
  html_document:
    toc: TRUE
    toc_depth: 2
    toc_float:
      collapsed: FALSE
bibliography: references.bib
csl: american-medical-association.csl
link-citations: yes
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, child = "js/back-to-top.js"}
```

<br>

::: info
<i class="fa fa-info-circle" style="color: white"></i>&nbsp; The variables listed on this page are applicable to releases 3.3.0+ and are not necessarily correct for older versions of Gannet.
:::

## Pre-initializing Gannet

<br>

**Parent function**

`GannetPreInitialise.m`

<br>

| <u>Acquisition parameters</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `target` | Sets the metabolite of interest that was edited in the MRS experiment; if HERMES or HERCULES was used, multiple metabolites should be selected |
| `seqorig` | Origin of the Philips MEGA-PRESS or GE HERMES sequences |

| <u>Analysis parameters</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `LB` | Sets the amount of exponential line-broadening to apply to the data (in Hz)                                           |
| `water_ECC` | Whether to perform an eddy-current correction [@Klose1990] on the water data                                          |
| `metab_ECC` | Whether to perform an eddy-current correction on the metabolite data                                                  |
| `water_removal` | Whether to remove the residual water signal from the difference spectra using an HSVD filter [@Barkhuijsen1987]       |
| `alignment` | Which method to use for shot-to-shot frequency-and-phase correction [@Evans2013; @Near2015; @Mikkelsen2018; @Mikkelsen2020] |
| `use_prealign_ref` | In some cases, using `RobustSpecReg` to align HERMES/HERCULES data can result in worse alignment compared to the pre-aligned data; setting this parameter to 1 will make `RobustSpecReg` use the averaged pre-aligned subspectra as references to align the averaged post-aligned subspectra, which may improve the final alignment; this has not be tested extensively |
| `fit_resid_water` | Whether to model the residual water signal in the difference spectrum; used to calculate a water suppression factor |
| `weighted_averaging` | Whether to average the data using weighted averaging; the default approach use is mean-squared error, but other approaches can be set in `SignalAveraging.m` |

| <u>Flags</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `HERMES`   | Set to `1` if the data were acquired using HERMES                                                                                         |
| `HERCULES` | Set to `1` if the data were acquired using HERCULES (`HERMES` must also be set to `1`)                                                    |
| `PRIAM`    | Set to `1` if the data were acquired using PRIAM                                                                                          |
| `phantom`  | Set to `1` if the data were acquired in a phantom                                                                                         |
| `join`     | Set to `1` to join multiple files (this can be batched across subjects)                                                                   |
| `mat`      | Set to `1` to save the output structure as a MAT-file                                                                                     |
| `csv`      | Set to `1` to export a CSV file containing useful data for statistical analysis (applies to `GannetFit.m`, `GannetSegment.m` and `GannetQuantify.m`) |
| `append`   | Set to `1` to append PDF outputs into one PDF (separately for each module) (requires `export_fig/` in the `Gannet/` directory to be added to the search path and <a href="https://www.ghostscript.com/" target="_blank">Ghostscript</a> to be installed on the user's machine (NB: macOS users should use <a href="https://pages.uoregon.edu/koch/" target="_blank">this version</a> or use <a href="https://formulae.brew.sh/formula/ghostscript" target="_blank">Homebrew</a> to install Ghostscript) |
| `hide`     | Set to `1` to not display output figures |

------------------------------------------------------------------------

## Loading & preprocessing

<br>

**Parent function**

`GannetLoad.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
```

Output:

<img src="images/reference/GannetLoad-example.png" alt="A screenshot example of GannetLoad output">

A) Edited difference spectra before and after frequency and phase alignment. Users should look out for poor alignment and signal distortions from lipid contamination or ghosting artifacts [@Kreis2004].

B) The observed frequency of either the residual water signal or 3 ppm Cr peak on a average-by-average basis. Users can use this panel to check for participant motion and/or scanner-related frequency drift. The solid black line is the nominal water or 3 ppm Cr frequency, while the dashed lines represent +/-1 standard deviation of population-level frequency offsets observed in the Big GABA study [@Mikkelsen2017]. In other words, frequency shifts beyond these limits indicates above-average offsets that should be of concern to data fidelity.

C) A spectrogram of the 3 ppm Cr signal average by average before and after frequency and phase alignment. These data complement the data shown in panel B by additionally showing spectral distortions in each average.

D) Textual details of the data and processing procedures. When "n/a - wgt. avg. used" is shown for "Rejects", this means the data were averaged using weighted averaging, which down-weights bad (i.e., "rejected") averages.

------------------------------------------------------------------------

## Signal fitting

<br>

**Parent function**

`GannetFit.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
```

Output:

<img src="images/reference/GannetFit-example.png" alt="A screenshot example of GannetFit output">

A) Edited difference spectrum (spectra) with fitted model function(s) overlaid. The fit residuals are also shown. NB: When modeling the GABA peak, Gannet applies weights to the residual Cho signal data points at 3.16 to 3.285 ppm. This improves fitting of the GABA peak when there are subtraction artifacts.

B) Reference spectra (water and Cr) with fitted model functions overlaid. The fit residuals are also shown. If no water reference data was given during data loading, only Cr is shown.

C) Textual results from GannetFit (full details can be found on the [Data quality metrics](https://markmikkelsen.github.io/Gannet-docs/data-quality-metrics.html) page). For the metabolites listed:
    - Area is the integrated signal (in arbitrary units) of the peaks shown
    - Linewidth is the full-width, half-maximum (FWHM) (in Hz)
    - SNR is defined as the signal model amplitude divided by twice the standard deviation of noise
    - Fit error is defined as the standard deviation of residuals divided by the signal model amplitude multiplied by 100
    - Quantification details can be found on the [Quantification & tissue correction](https://markmikkelsen.github.io/Gannet-docs/quantification-tissue-correction.html) page.

------------------------------------------------------------------------

## Voxel co-registration

<br>

**Parent function**

`GannetCoRegister.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
MRS_struct = GannetCoRegister(MRS_struct, {'S01_struc.nii'});
```

Output:

<img src="images/reference/GannetCoRegister-example.png" alt="A screenshot example of GannetCoRegister output">

A) Co-registration of the MRS voxel on the corresponding structural MR image. The three orthogonal slices are shown at the voxel center.

B) Textual details of the voxel geometry.

------------------------------------------------------------------------

## Tissue segmentation

<br>

**Parent function**

`GannetSegment.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
MRS_struct = GannetCoRegister(MRS_struct, {'S01_struc.nii'});
MRS_struct = GannetSegment(MRS_struct);
```

Output:

<img src="images/reference/GannetSegment-example.png" alt="A screenshot example of GannetSegment output">

A) Axial view of the probabilistic voxel tissue segmentation maps for grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

B) Textual details of the CSF-corrected metabolites values (see [Quantification & tissue correction](https://markmikkelsen.github.io/Gannet-docs/quantification-tissue-correction.html) for further details) and the voxel tissue volume fractions.

------------------------------------------------------------------------

## Quantification

<br>

**Parent function**

`GannetQuantify.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
MRS_struct = GannetCoRegister(MRS_struct, {'S01_struc.nii'});
MRS_struct = GannetSegment(MRS_struct);
MRS_struct = GannetQuantify(MRS_struct);
```

Output:

<img src="images/reference/GannetQuantify-example.png" alt="A screenshot example of GannetQuantify output">

A) Axial view of the probabilistic voxel tissue segmentation maps for grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

B)  Edited difference spectrum (spectra) with fitted model function(s) overlaid.

C) Textual details of tissue-corrected metabolite values (see [Quantification & tissue correction](https://markmikkelsen.github.io/Gannet-docs/quantification-tissue-correction.html) for further details).

<br>

### References




+
---
title: "Reference"
date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`"
bibliography: bibliography.bib
csl: american-medical-association.csl
link-citations: yes
output:
  html_document:
    toc: TRUE
    toc_depth: 2
    toc_float:
      collapsed: FALSE
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r, child = "js/back-to-top.js"}
```

<br>

::: info
<i class="fa fa-info-circle" style="color: white"></i>&nbsp; The variables listed on this page are applicable to releases 3.3.0+ and are not necessarily correct for older versions of Gannet.
:::

## Pre-initializing Gannet

<br>

**Parent function**

`GannetPreInitialise.m`

<br>

| <u>Acquisition parameters</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `target` | Sets the metabolite of interest that was edited in the MRS experiment; if HERMES or HERCULES was used, multiple metabolites should be selected |
| `seqorig` | Origin of the Philips MEGA-PRESS or GE HERMES sequences |

| <u>Analysis parameters</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `LB` | Sets the amount of exponential line-broadening to apply to the data (in Hz)                                           |
| `water_ECC` | Whether to perform an eddy-current correction [@Klose1990] on the water data                                          |
| `metab_ECC` | Whether to perform an eddy-current correction on the metabolite data                                                  |
| `water_removal` | Whether to remove the residual water signal from the difference spectra using an HSVD filter [@Barkhuijsen1987]       |
| `alignment` | Which method to use for shot-to-shot frequency-and-phase correction [@Evans2013; @Near2015; @Mikkelsen2018; @Mikkelsen2020] |
| `use_prealign_ref` | In some cases, using `RobustSpecReg` to align HERMES/HERCULES data can result in worse alignment compared to the pre-aligned data; setting this parameter to 1 will make `RobustSpecReg` use the averaged pre-aligned subspectra as references to align the averaged post-aligned subspectra, which may improve the final alignment; this has not be tested extensively |
| `fit_resid_water` | Whether to model the residual water signal in the difference spectrum; used to calculate a water suppression factor |
| `weighted_averaging` | Whether to average the data using weighted averaging; the default approach use is mean-squared error, but other approaches can be set in `SignalAveraging.m` |

| <u>Flags</u> | <u>Description</u> |
|:------------------|:----------------------------------------------------|
| `HERMES`   | Set to `1` if the data were acquired using HERMES                                                                                         |
| `HERCULES` | Set to `1` if the data were acquired using HERCULES (`HERMES` must also be set to `1`)                                                    |
| `PRIAM`    | Set to `1` if the data were acquired using PRIAM                                                                                          |
| `phantom`  | Set to `1` if the data were acquired in a phantom                                                                                         |
| `join`     | Set to `1` to join multiple files (this can be batched across subjects)                                                                   |
| `mat`      | Set to `1` to save the output structure as a MAT-file                                                                                     |
| `csv`      | Set to `1` to export a CSV file containing useful data for statistical analysis (applies to `GannetFit.m`, `GannetSegment.m` and `GannetQuantify.m`) |
| `append`   | Set to `1` to append PDF outputs into one PDF (separately for each module) (requires `export_fig/` in the `Gannet/` directory to be added to the search path and <a href="https://www.ghostscript.com/" target="_blank">Ghostscript</a> to be installed on the user's machine (NB: macOS users should use <a href="https://pages.uoregon.edu/koch/" target="_blank">this version</a> or use <a href="https://formulae.brew.sh/formula/ghostscript" target="_blank">Homebrew</a> to install Ghostscript) |
| `hide`     | Set to `1` to not display output figures |

------------------------------------------------------------------------

## Loading & preprocessing

<br>

**Parent function**

`GannetLoad.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
```

Output:

<img src="images/reference/GannetLoad-example.png" alt="A screenshot example of GannetLoad output">

A) Edited difference spectra before and after frequency and phase alignment. Users should look out for poor alignment and signal distortions from lipid contamination or ghosting artifacts [@Kreis2004].

B) The observed frequency of either the residual water signal or 3 ppm Cr peak on a average-by-average basis. Users can use this panel to check for participant motion and/or scanner-related frequency drift. The solid black line is the nominal water or 3 ppm Cr frequency, while the dashed lines represent +/-1 standard deviation of population-level frequency offsets observed in the Big GABA study [@Mikkelsen2017]. In other words, frequency shifts beyond these limits indicates above-average offsets that should be of concern to data fidelity.

C) A spectrogram of the 3 ppm Cr signal average by average before and after frequency and phase alignment. These data complement the data shown in panel B by additionally showing spectral distortions in each average.

D) Textual details of the data and processing procedures. When "n/a - wgt. avg. used" is shown for "Rejects", this means the data were averaged using weighted averaging, which down-weights bad (i.e., "rejected") averages.

------------------------------------------------------------------------

## Signal fitting

<br>

**Parent function**

`GannetFit.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
```

Output:

<img src="images/reference/GannetFit-example.png" alt="A screenshot example of GannetFit output">

A) Edited difference spectrum (spectra) with fitted model function(s) overlaid. The fit residuals are also shown. NB: When modeling the GABA peak, Gannet applies weights to the residual Cho signal data points at 3.16 to 3.285 ppm. This improves fitting of the GABA peak when there are subtraction artifacts.

B) Reference spectra (water and Cr) with fitted model functions overlaid. The fit residuals are also shown. If no water reference data was given during data loading, only Cr is shown.

C) Textual results from GannetFit (full details can be found on the [Data quality metrics](https://markmikkelsen.github.io/Gannet-docs/data-quality-metrics.html) page). For the metabolites listed:
    - Area is the integrated signal (in arbitrary units) of the peaks shown
    - Linewidth is the full-width, half-maximum (FWHM) (in Hz)
    - SNR is defined as the signal model amplitude divided by twice the standard deviation of noise
    - Fit error is defined as the standard deviation of residuals divided by the signal model amplitude multiplied by 100
    - Quantification details can be found on the [Quantification & tissue correction](https://markmikkelsen.github.io/Gannet-docs/quantification-tissue-correction.html) page.

------------------------------------------------------------------------

## Voxel co-registration

<br>

**Parent function**

`GannetCoRegister.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
MRS_struct = GannetCoRegister(MRS_struct, {'S01_struc.nii'});
```

Output:

<img src="images/reference/GannetCoRegister-example.png" alt="A screenshot example of GannetCoRegister output">

A) Co-registration of the MRS voxel on the corresponding structural MR image. The three orthogonal slices are shown at the voxel center.

B) Textual details of the voxel geometry.

------------------------------------------------------------------------

## Tissue segmentation

<br>

**Parent function**

`GannetSegment.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
MRS_struct = GannetCoRegister(MRS_struct, {'S01_struc.nii'});
MRS_struct = GannetSegment(MRS_struct);
```

Output:

<img src="images/reference/GannetSegment-example.png" alt="A screenshot example of GannetSegment output">

A) Axial view of the probabilistic voxel tissue segmentation maps for grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

B) Textual details of the CSF-corrected metabolites values (see [Quantification & tissue correction](https://markmikkelsen.github.io/Gannet-docs/quantification-tissue-correction.html) for further details) and the voxel tissue volume fractions.

------------------------------------------------------------------------

## Quantification

<br>

**Parent function**

`GannetQuantify.m`

<br>

**Example usage:**

```{octave, eval = FALSE}
MRS_struct = GannetLoad({'S01_GABA_68_act.sdat'}, {'S01_GABA_68_ref.sdat'});
MRS_struct = GannetFit(MRS_struct);
MRS_struct = GannetCoRegister(MRS_struct, {'S01_struc.nii'});
MRS_struct = GannetSegment(MRS_struct);
MRS_struct = GannetQuantify(MRS_struct);
```

Output:

<img src="images/reference/GannetQuantify-example.png" alt="A screenshot example of GannetQuantify output">

A) Axial view of the probabilistic voxel tissue segmentation maps for grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

B)  Edited difference spectrum (spectra) with fitted model function(s) overlaid.

C) Textual details of tissue-corrected metabolite values (see [Quantification & tissue correction](https://markmikkelsen.github.io/Gannet-docs/quantification-tissue-correction.html) for further details).

<br>

### References





diff --git a/_site/release-notes.html b/_site/release-notes.html index 0ce4048..ab58856 100644 --- a/_site/release-notes.html +++ b/_site/release-notes.html @@ -431,7 +431,7 @@

Release notes

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

diff --git a/_site/signal-modeling.html b/_site/signal-modeling.html index 0e56a78..3ba89a1 100644 --- a/_site/signal-modeling.html +++ b/_site/signal-modeling.html @@ -453,7 +453,7 @@

Signal modeling

-

Last updated: January 24, 2024

+

Last updated: February 05, 2024

@@ -782,7 +782,7 @@

References -
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+
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diff --git a/references.bib b/bibliography.bib old mode 100755 new mode 100644 similarity index 91% rename from references.bib rename to bibliography.bib index d289d02..c0d7b1e --- a/references.bib +++ b/bibliography.bib @@ -1,36 +1,71 @@ -@article{Barkhuijsen1987, -abstract = {The authors are concerned with a new method of fitting a physical model function to a magnetic resonance signal, directly in the time domain. Their primary aim is analysis of the signal in quantitative terms, i.e., describing the signal in terms of physically meaningful parameters with their statistical errors. Before explaining the new method they make some remarks about the place of time-domain model fitting in spectral analysis}, -author = {Barkhuijsen, H. and de Beer, R. and van Ormondt, D.}, -doi = {10.1016/0022-2364(87)90023-0}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Barkhuijsen, de Beer, van Ormondt/Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals.pdf:pdf}, -isbn = {0022-2364}, -issn = {00222364}, -journal = {Journal of Magnetic Resonance}, +@article{Mikkelsen2018, +author = {Mikkelsen, Mark and Saleh, Muhammad G. and Near, Jamie and Chan, Kimberly L. and Gong, Tao and Harris, Ashley D. and Oeltzschner, Georg and Puts, Nicolaas A.J. and Cecil, Kim M. and Wilkinson, Iain D. and Edden, Richard A.E.}, +doi = {10.1002/mrm.27027}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Frequency and phase correction for multiplexed edited MRS of GABA and glutathione.pdf:pdf}, +issn = {07403194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {frequency,gaba,gsh,her-,mes,mrs,phase correction,spectral registration}, month = {jul}, -number = {3}, -pages = {553--557}, -title = {{Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals}}, -url = {http://linkinghub.elsevier.com/retrieve/pii/0022236487900230 https://linkinghub.elsevier.com/retrieve/pii/0022236487900230}, -volume = {73}, -year = {1987} +number = {1}, +pages = {21--28}, +title = {{Frequency and phase correction for multiplexed edited MRS of GABA and glutathione}}, +url = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.27027}, +volume = {80}, +year = {2018} } -@article{Edden2012, -abstract = {PURPOSE: To develop an experimental approach for determining in vivo transverse relaxation rates (T(2)) of metabolites that are detected by spectral editing without using simulations, and to demonstrate this approach to measure the T(2) of $\gamma$-aminobutyric acid (GABA).\n\nMATERIALS AND METHODS: The proposed method first determines the TE-dependence of the edited signals using measurements in a pure phantom solution (10 mM $\gamma$-aminobutyric acid; GABA); the phantom T(2) is also determined. Once the editing echo time (TE)-modulation pattern is known, it can then be used to determine T(2) in vivo. The method was applied to measure GABA T(2) in the occipital lobe of five healthy adult subjects at 3T, using a J-difference editing method. Unwanted macromolecular contributions to the GABA signal were also measured.\n\nRESULTS: The in vivo T(2) of edited GABA signal was 88 ± 12 ms; this preliminary result is somewhat shorter than other metabolite T(2) values in the literature at this field strength.\n\nCONCLUSION: Spectral editing methods are now widely used to detect low concentration metabolites, such as GABA, but to date no edited acquisition methods have been proposed for the measurement of transverse relaxation times (T(2)). The method described has been successfully applied to measuring the T(2) of GABA.}, -author = {Edden, Richard A.E. and Intrapiromkul, Jarunee and Zhu, He and Cheng, Ying and Barker, Peter B}, -doi = {10.1002/jmri.22865}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Edden et al/Measuring T2 in vivo with J-difference editing Application to GABA at 3 Tesla.pdf:pdf}, -isbn = {1522-2586 (Electronic)\r1053-1807 (Linking)}, -issn = {10531807}, +@article{Mescher1998, +abstract = {Water suppression is typically performed in vivo by exciting the longitudinal magnetization in combination with dephasing, or by using frequency-selective coherence generation. MEGA, a frequency-selective refocusing technique, can be placed into any pulse sequence element designed to generate a Hahn spin-echo or stimulated echo, to dephase transverse water coherences with minimal spectral distortions. Water suppression performance was verified in vivo using stimulated echo acquisition mode (STEAM) localization, which provided water suppression comparable with that achieved with four selective pulses in 3,1-DRYSTEAM. The advantage of the proposed method was exploited for editing J-coupled resonances. Using a double-banded pulse that selectively inverts a J-coupling partner and simultaneously suppresses water, efficient metabolite editing was achieved in the point resolved spectroscopy (PRESS) and STEAM sequences in which MEGA was incorporated. To illustrate the efficiency of the method, the detection of gamma-aminobutyric acid (GABA) was demonstrated, with minimal contributions from macromolecules and overlying singlet peaks at 4 T. The estimated occipital GABA concentration was consistent with previous reports, suggesting that editing for GABA is efficient when based on MEGA at high field strengths.}, +author = {Mescher, M and Merkle, H and Kirsch, J and Garwood, M and Gruetter, R}, +doi = {10.1002/(SICI)1099-1492(199810)11:6<266::AID-NBM530>3.0.CO;2-J}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mescher et al/Simultaneous in vivo spectral editing and water suppression.pdf:pdf}, +isbn = {0952-3480 (Print)}, +issn = {0952-3480}, +journal = {NMR in Biomedicine}, +keywords = {Frequency selective water suppression,GABA,Human brain,In vivo 1H MRS}, +month = {oct}, +number = {6}, +pages = {266--272}, +pmid = {9802468}, +title = {{Simultaneous in vivo spectral editing and water suppression}}, +url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1099-1492(199810)11:6%3C266::AID-NBM530%3E3.0.CO;2-J}, +volume = {11}, +year = {1998} +} +@article{Lu2005, +abstract = {PURPOSE: To establish image parameters for some routine clinical brain MRI pulse sequences at 3.0 T with the goal of maintaining, as much as possible, the well-characterized 1.5-T image contrast characteristics for daily clinical diagnosis, while benefiting from the increased signal to noise at higher field. MATERIALS AND METHODS: A total of 10 healthy subjects were scanned on 1.5-T and 3.0-T systems for T(1) and T(2) relaxation time measurements of major gray and white matter structures. The relaxation times were subsequently used to determine 3.0-T acquisition parameters for spin-echo (SE), T(1)-weighted, fast spin echo (FSE) or turbo spin echo (TSE), T(2)-weighted, and fluid-attenuated inversion recovery (FLAIR) pulse sequences that give image characteristics comparable to 1.5 T, to facilitate routine clinical diagnostics. Application of the routine clinical sequences was performed in 10 subjects, five normal subjects and five patients with various pathologies. RESULTS: T(1) and T(2) relaxation times were, respectively, 14% to 30% longer and 12% to 19% shorter at 3.0 T when compared to the values at 1.5 T, depending on the region evaluated. When using appropriate parameters, routine clinical images acquired at 3.0 T showed similar image characteristics to those obtained at 1.5 T, but with higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), which can be used to reduce the number of averages and scan times. Recommended imaging parameters for these sequences are provided. CONCLUSION: When parameters are adjusted for changes in relaxation rates, routine clinical scans at 3.0 T can provide similar image appearance as 1.5 T, but with superior image quality and/or increased speed.}, +author = {Lu, Hanzhang and Nagae-Poetscher, Lidia M. and Golay, Xavier and Lin, Doris and Pomper, Martin and van Zijl, Peter C.M.}, +doi = {10.1002/jmri.20356}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Lu et al/Routine clinical brain MRI sequences for use at 3.0 Tesla.pdf:pdf}, +isbn = {1522-2586}, +issn = {1053-1807}, journal = {Journal of Magnetic Resonance Imaging}, -keywords = {GABA,T 2,brain,edited MR spectroscopy,transverse relaxation}, -month = {jan}, +keywords = {Brain,Clinical MRI,FLAIR,High field,T1,T2}, +month = {jul}, number = {1}, -pages = {229--234}, -pmid = {22045601}, -title = {{Measuring T2 in vivo with J-difference editing: Application to GABA at 3 Tesla}}, -url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3377980&tool=pmcentrez&rendertype=abstract http://doi.wiley.com/10.1002/jmri.22865}, -volume = {35}, -year = {2012} +pages = {13--22}, +pmid = {15971174}, +title = {{Routine clinical brain MRI sequences for use at 3.0 Tesla}}, +url = {http://doi.wiley.com/10.1002/jmri.20356}, +volume = {22}, +year = {2005} +} +@article{Piechnik2009, +abstract = {Cerebrospinal fluid (CSF) provides hydraulic suspension for the brain. The general concept of bulk CSF production, circulation, and reabsorption is well established, but the mechanisms of momentary CSF volume variation corresponding to vasoreactive changes are far less understood. Nine individuals were studied in a 3T MR scanner with a protocol that included visual stimulation using a 10-Hz reversing checkerboard and administration of a 5% CO(2) mix in air. We acquired PRESS-localized spin-echoes (TR = 12 sec, TE = 26 ms to 1.5 sec) from an 8-mL voxel located in the visual cortex. Echo amplitudes were fitted to a two-compartmental model of relaxation to estimate the partial volume of CSF and the T(2) relaxation times of the tissues. CSF signal contributed 10.7 +/- 3% of the total, with T(2,csf) = 503.0 +/- 64.3 [ms], T(2,brain) = 61.0 +/- 2 [ms]. The relaxation time of tissue increased during physiological stimulation, while the fraction of signal contributed by CSF decreased significantly by 5-6% with visual stimulation (P < 0.03) and by 3% under CO(2) inhalation (P < 0.08). The CSF signal fraction is shown to represent well the volume changes under viable physiological scenarios. In conclusion, CSF plays a significant role in buffering the changes in cerebral blood volume, especially during rapid functional stimuli.}, +author = {Piechnik, S.K. and Evans, J. and Bary, L.H. and Wise, R.G. and Jezzard, P.}, +doi = {10.1002/mrm.21897}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Piechnik et al/Functional changes in CSF volume estimated using measurement of water T2 relaxation.pdf:pdf}, +isbn = {1522-2594 (Electronic)}, +issn = {07403194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {CO2 reactivity,Cerebral blood flow,Cerebrospinal fluid,Functional responses,Magnetic resonance imaging,Magnetic resonance spectroscopy,Vasodilatation}, +month = {mar}, +number = {3}, +pages = {579--586}, +pmid = {19132756}, +title = {{Functional changes in CSF volume estimated using measurement of water T2 relaxation}}, +url = {http://doi.wiley.com/10.1002/mrm.21897}, +volume = {61}, +year = {2009} } @article{Ernst1993, abstract = {A method is presented to determine the compartmentation of a localized region in the human brain in terms of CSF, tissue water, and an NMR-invisible rest, using a PRESS or STEAM sequence. Discrimination between CSF and tissue water is based on differences in their T2 relaxation times. The NMR-invisible compartment is assessed using an external standard. The composition of three regions in the human brain is determined. The CSF content of specific regions can be used to quantify cortical atrophy. The method provides a means for measuring the water content of brain tissue in vivo with a precision of 1.5%. After appropriate corrections, the results are in close agreement with biochemical values. The method has major applications in localized quantitative spectroscopy. The compartmentation model can be used to correct for the CSF content of the selected volume and to properly define and interconvert all major concentration units.}, @@ -47,41 +82,6 @@ @article{Ernst1993 volume = {102}, year = {1993} } -@article{Evans2013, -abstract = {PURPOSE: To compare the repeatability of $\gamma$-aminobutyric acid (GABA) measurements using J-difference editing, before and after spectral realignment-a technique which has previously been demonstrated to improve the quality of J-difference GABA spectra.\n\nMATERIALS AND METHODS: We performed in vivo measurements in three brain regions (occipital, sensorimotor, and dorsolateral prefrontal cortex [DLPFC]), and analyzed these using alternative alignment approaches to evaluate the impact of alignment on repeatability: "Independent alignment" (aligning each subspectrum independently) and "Pairwise alignment" (aligning each on and off subspectrum as a pair) were compared.\n\nRESULTS: Pairwise alignment improved the group mean coefficient of variation in all regions; 0.4% in occipital, 1.1% in sensorimotor, and 1.1% in DLPFC. Independent alignment resulted in subtraction artifacts in the majority of cases, and increased the coefficient of variation in the DLPFC by 9.4%. Simulations demonstrate that the GABA quantification error in datasets with high B0 drift, is 4.5% without alignment, but <1% with optimal alignment.\n\nCONCLUSION: Pairwise alignment improves the repeatability of GABA spectroscopy data. However, independently aligning all on and off subspectra can lead to artifacts and worse repeatability when compared with nonaligned data.}, -author = {Evans, C. John and Puts, Nicolaas A.J. and Robson, Si{\^{a}}n E. and Boy, Frederic and McGonigle, David J. and Sumner, Petroc and Singh, Krish D. and Edden, Richard A.E.}, -doi = {10.1002/jmri.23923}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Evans et al/Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing.pdf:pdf}, -issn = {10531807}, -journal = {Journal of Magnetic Resonance Imaging}, -keywords = {GABA,MRS,frequency alignment,repeatability,subtraction artifact}, -month = {oct}, -number = {4}, -pages = {970--975}, -pmid = {23188759}, -title = {{Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/23188759 http://doi.wiley.com/10.1002/jmri.23923 https://onlinelibrary.wiley.com/doi/10.1002/jmri.23923}, -volume = {38}, -year = {2013} -} -@article{Gasparovic2006, -abstract = {A strategy for using tissue water as a concentration standard in (1)H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with FAST multispectral segmentation are reported: GM [NAc] = 17.16 +/- 1.19 mM; WM [NAc] = 14.26 +/- 1.38 mM; GM [Ch] = 3.27 +/- 0.47 mM; WM [Ch] = 2.65 +/- 0.25 mM; GM [Cr] = 13.98 +/- 1.20 mM; and WM [Cr] = 7.10 +/- 0.67 mM.}, -author = {Gasparovic, Charles and Song, Tao and Devier, Deidre and Bockholt, H Jeremy and Caprihan, Arvind and Mullins, Paul G and Posse, Stefan and Jung, Rex E and Morrison, Leslie A}, -doi = {10.1002/mrm.20901}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Gasparovic et al/Use of tissue water as a concentration reference for proton spectroscopic imaging.pdf:pdf}, -isbn = {0740-3194 (Print)\n0740-3194 (Linking)}, -issn = {0740-3194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {1H-MRS,Relaxation times,Spectroscopic imaging,Tissue water,Voxel}, -month = {jun}, -number = {6}, -pages = {1219--1226}, -pmid = {16688703}, -title = {{Use of tissue water as a concentration reference for proton spectroscopic imaging}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/16688703 http://doi.wiley.com/10.1002/mrm.20901}, -volume = {55}, -year = {2006} -} @article{Harris2017, author = {Harris, Ashley D and Saleh, Muhammad G and Edden, Richard A.E.}, doi = {10.1002/mrm.26619}, @@ -97,101 +97,22 @@ @article{Harris2017 volume = {77}, year = {2017} } -@article{Harris2015, -author = {Harris, Ashley D. and Puts, Nicolaas A.J. and Edden, Richard A.E.}, -doi = {10.1002/jmri.24903}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations.pdf:pdf;:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations(2).pdf:pdf}, -issn = {10531807}, -journal = {Journal of Magnetic Resonance Imaging}, -month = {nov}, -number = {5}, -pages = {1431--1440}, -title = {{Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations}}, -url = {http://doi.wiley.com/10.1002/jmri.24903}, -volume = {42}, -year = {2015} -} -@article{Klose1990, -abstract = {Spatially localized methods in spectroscopy often operate with magnetic field gradients for volume selection. The eddy currents induced by these gradients produce time-dependent shifts of the resonance frequency in the selected volume, which results in a distortion of the spectrum after Fourier transformation. In whole-body systems the complete compensation of eddy currents is a difficult procedure. To avoid this, a correction method is proposed for proton spectroscopy, which uses the signal of prominent water protons as a reference for the water-suppressed signal. The correction is performed in the time domain, dividing the water-suppressed signal by the phase factor of the water signal for each data point. The corrected spectra have a good resolution as shown by phantom measurements and brain and muscle spectra of volunteers.}, -author = {Klose, Uwe}, -doi = {10.1002/mrm.1910140104}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Klose/In vivo proton spectroscopy in presence of eddy currents.pdf:pdf}, +@article{Near2015, +abstract = {PURPOSE: Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data.\n\nMETHODS: We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as "spectral registration." Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels.\n\nRESULTS: Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation.\n\nCONCLUSIONS: Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile. Magn Reson Med, 2014. {\textcopyright} 2014 Wiley Periodicals, Inc.}, +author = {Near, Jamie and Edden, Richard and Evans, C John and Paquin, Rapha{\"{e}}l and Harris, Ashley and Jezzard, Peter}, +doi = {10.1002/mrm.25094}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Near et al/Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.pdf:pdf}, issn = {07403194}, journal = {Magnetic Resonance in Medicine}, -keywords = {Extracellular Space,Fourier Analysis,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: methods,Protons,Time Factors}, -month = {apr}, -number = {1}, -pages = {26--30}, -pmid = {2161984}, -title = {{In vivo proton spectroscopy in presence of eddy currents}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/2161984 http://doi.wiley.com/10.1002/mrm.1910140104}, -volume = {14}, -year = {1990} -} -@article{Lu2005, -abstract = {PURPOSE: To establish image parameters for some routine clinical brain MRI pulse sequences at 3.0 T with the goal of maintaining, as much as possible, the well-characterized 1.5-T image contrast characteristics for daily clinical diagnosis, while benefiting from the increased signal to noise at higher field. MATERIALS AND METHODS: A total of 10 healthy subjects were scanned on 1.5-T and 3.0-T systems for T(1) and T(2) relaxation time measurements of major gray and white matter structures. The relaxation times were subsequently used to determine 3.0-T acquisition parameters for spin-echo (SE), T(1)-weighted, fast spin echo (FSE) or turbo spin echo (TSE), T(2)-weighted, and fluid-attenuated inversion recovery (FLAIR) pulse sequences that give image characteristics comparable to 1.5 T, to facilitate routine clinical diagnostics. Application of the routine clinical sequences was performed in 10 subjects, five normal subjects and five patients with various pathologies. RESULTS: T(1) and T(2) relaxation times were, respectively, 14% to 30% longer and 12% to 19% shorter at 3.0 T when compared to the values at 1.5 T, depending on the region evaluated. When using appropriate parameters, routine clinical images acquired at 3.0 T showed similar image characteristics to those obtained at 1.5 T, but with higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), which can be used to reduce the number of averages and scan times. Recommended imaging parameters for these sequences are provided. CONCLUSION: When parameters are adjusted for changes in relaxation rates, routine clinical scans at 3.0 T can provide similar image appearance as 1.5 T, but with superior image quality and/or increased speed.}, -author = {Lu, Hanzhang and Nagae-Poetscher, Lidia M. and Golay, Xavier and Lin, Doris and Pomper, Martin and van Zijl, Peter C.M.}, -doi = {10.1002/jmri.20356}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Lu et al/Routine clinical brain MRI sequences for use at 3.0 Tesla.pdf:pdf}, -isbn = {1522-2586}, -issn = {1053-1807}, -journal = {Journal of Magnetic Resonance Imaging}, -keywords = {Brain,Clinical MRI,FLAIR,High field,T1,T2}, -month = {jul}, +keywords = {B0 drift,Frequency drift,Magnetic resonance spectroscopy,Motion correction,Phase drift}, +month = {jan}, number = {1}, -pages = {13--22}, -pmid = {15971174}, -title = {{Routine clinical brain MRI sequences for use at 3.0 Tesla}}, -url = {http://doi.wiley.com/10.1002/jmri.20356}, -volume = {22}, -year = {2005} -} -@article{Mescher1998, -abstract = {Water suppression is typically performed in vivo by exciting the longitudinal magnetization in combination with dephasing, or by using frequency-selective coherence generation. MEGA, a frequency-selective refocusing technique, can be placed into any pulse sequence element designed to generate a Hahn spin-echo or stimulated echo, to dephase transverse water coherences with minimal spectral distortions. Water suppression performance was verified in vivo using stimulated echo acquisition mode (STEAM) localization, which provided water suppression comparable with that achieved with four selective pulses in 3,1-DRYSTEAM. The advantage of the proposed method was exploited for editing J-coupled resonances. Using a double-banded pulse that selectively inverts a J-coupling partner and simultaneously suppresses water, efficient metabolite editing was achieved in the point resolved spectroscopy (PRESS) and STEAM sequences in which MEGA was incorporated. To illustrate the efficiency of the method, the detection of gamma-aminobutyric acid (GABA) was demonstrated, with minimal contributions from macromolecules and overlying singlet peaks at 4 T. The estimated occipital GABA concentration was consistent with previous reports, suggesting that editing for GABA is efficient when based on MEGA at high field strengths.}, -author = {Mescher, M and Merkle, H and Kirsch, J and Garwood, M and Gruetter, R}, -doi = {10.1002/(SICI)1099-1492(199810)11:6<266::AID-NBM530>3.0.CO;2-J}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mescher et al/Simultaneous in vivo spectral editing and water suppression.pdf:pdf}, -isbn = {0952-3480 (Print)}, -issn = {0952-3480}, -journal = {NMR in Biomedicine}, -keywords = {Frequency selective water suppression,GABA,Human brain,In vivo 1H MRS}, -month = {oct}, -number = {6}, -pages = {266--272}, -pmid = {9802468}, -title = {{Simultaneous in vivo spectral editing and water suppression}}, -url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/(SICI)1099-1492(199810)11:6%3C266::AID-NBM530%3E3.0.CO;2-J}, -volume = {11}, -year = {1998} -} -@article{Mikkelsen2017, -author = {Mikkelsen, Mark and Barker, Peter B and Bhattacharyya, Pallab K and Brix, Maiken K and Buur, Pieter F. and Cecil, Kim M and Chan, Kimberly L and Chen, David Y.-T. and Craven, Alexander R and Cuypers, Koen and Dacko, Michael and Duncan, Niall W and Dydak, Ulrike and Edmondson, David A and Ende, Gabriele and Ersland, Lars and Gao, Fei and Greenhouse, Ian and Harris, Ashley D and He, Naying and Heba, Stefanie and Hoggard, Nigel and Hsu, Tun-wei and Jansen, Jacobus F.A. and Kangarlu, Alayar and Lange, Thomas and Lebel, R Marc and Li, Yan and Lin, Chien-yuan E and Liou, Jy-kang and Lirng, Jiing-Feng and Liu, Feng and Ma, Ruoyun and Maes, Celine and Moreno-Ortega, Marta and Murray, Scott O and Noah, Sean and Noeske, Ralph and Noseworthy, Michael D and Oeltzschner, Georg and Prisciandaro, James J. and Puts, Nicolaas A.J. and Roberts, Timothy P.L. and Sack, Markus and Sailasuta, Napapon and Saleh, Muhammad G and Schallmo, Michael-paul and Simard, Nicholas and Swinnen, Stephan P. and Tegenthoff, Martin and Truong, Peter and Wang, Guangbin and Wilkinson, Iain D and Wittsack, Hans-J{\"{o}}rg and Xu, Hongmin and Yan, Fuhua and Zhang, Chencheng and Zipunnikov, Vadim and Z{\"{o}}llner, Helge J. and Edden, Richard A.E.}, -doi = {10.1016/j.neuroimage.2017.07.021}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Big GABA Edited MR spectroscopy at 24 research sites.pdf:pdf}, -issn = {10538119}, -journal = {NeuroImage}, -month = {oct}, -pages = {32--45}, -publisher = {Elsevier Inc.}, -title = {{Big GABA: Edited MR spectroscopy at 24 research sites}}, -url = {https://doi.org/10.1016/j.neuroimage.2017.07.021 https://linkinghub.elsevier.com/retrieve/pii/S105381191730589X}, -volume = {159}, -year = {2017} -} -@article{Mikkelsen2020, -author = {Mikkelsen, Mark and Tapper, Sofie and Near, Jamie and Mostofsky, Stewart H. and Puts, Nicolaas A. J. and Edden, Richard A. E.}, -doi = {10.1002/nbm.4368}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Correcting frequency and phase offsets in MRS data using robust spectral registration.pdf:pdf}, -issn = {0952-3480}, -journal = {NMR in Biomedicine}, -month = {oct}, -number = {10}, -pages = {e4368}, -pmid = {32656879}, -title = {{Correcting frequency and phase offsets in MRS data using robust spectral registration}}, -url = {https://onlinelibrary.wiley.com/doi/10.1002/nbm.4368}, -volume = {33}, -year = {2020} +pages = {44--50}, +pmid = {24436292}, +title = {{Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/24436292 http://doi.wiley.com/10.1002/mrm.25094}, +volume = {73}, +year = {2015} } @article{Mullins2014, abstract = {There is increasing interest in the use of edited proton magnetic resonance spectroscopy for the detection of GABA in the human brain. At a recent meeting held at Cardiff University, a number of spectroscopy groups met to discuss the acquisition, analysis and interpretation of GABA-edited MR spectra. This paper aims to set out the issues discussed at this meeting, reporting areas of consensus around parameters and procedures in the field and highlighting those areas where differences remain. It is hoped that this paper can fulfill two needs, providing a summary of the current 'state-of-the-art' in the field of GABA-edited MRS at 3T using MEGA-PRESS and a basic guide to help researchers new to the field to avoid some of the pitfalls inherent in the acquisition and processing of edited MRS for GABA.}, @@ -210,41 +131,6 @@ @article{Mullins2014 volume = {86}, year = {2014} } -@article{Near2015, -abstract = {PURPOSE: Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data.\n\nMETHODS: We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as "spectral registration." Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels.\n\nRESULTS: Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation.\n\nCONCLUSIONS: Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile. Magn Reson Med, 2014. {\textcopyright} 2014 Wiley Periodicals, Inc.}, -author = {Near, Jamie and Edden, Richard and Evans, C John and Paquin, Rapha{\"{e}}l and Harris, Ashley and Jezzard, Peter}, -doi = {10.1002/mrm.25094}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Near et al/Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.pdf:pdf}, -issn = {07403194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {B0 drift,Frequency drift,Magnetic resonance spectroscopy,Motion correction,Phase drift}, -month = {jan}, -number = {1}, -pages = {44--50}, -pmid = {24436292}, -title = {{Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/24436292 http://doi.wiley.com/10.1002/mrm.25094}, -volume = {73}, -year = {2015} -} -@article{Piechnik2009, -abstract = {Cerebrospinal fluid (CSF) provides hydraulic suspension for the brain. The general concept of bulk CSF production, circulation, and reabsorption is well established, but the mechanisms of momentary CSF volume variation corresponding to vasoreactive changes are far less understood. Nine individuals were studied in a 3T MR scanner with a protocol that included visual stimulation using a 10-Hz reversing checkerboard and administration of a 5% CO(2) mix in air. We acquired PRESS-localized spin-echoes (TR = 12 sec, TE = 26 ms to 1.5 sec) from an 8-mL voxel located in the visual cortex. Echo amplitudes were fitted to a two-compartmental model of relaxation to estimate the partial volume of CSF and the T(2) relaxation times of the tissues. CSF signal contributed 10.7 +/- 3% of the total, with T(2,csf) = 503.0 +/- 64.3 [ms], T(2,brain) = 61.0 +/- 2 [ms]. The relaxation time of tissue increased during physiological stimulation, while the fraction of signal contributed by CSF decreased significantly by 5-6% with visual stimulation (P < 0.03) and by 3% under CO(2) inhalation (P < 0.08). The CSF signal fraction is shown to represent well the volume changes under viable physiological scenarios. In conclusion, CSF plays a significant role in buffering the changes in cerebral blood volume, especially during rapid functional stimuli.}, -author = {Piechnik, S.K. and Evans, J. and Bary, L.H. and Wise, R.G. and Jezzard, P.}, -doi = {10.1002/mrm.21897}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Piechnik et al/Functional changes in CSF volume estimated using measurement of water T2 relaxation.pdf:pdf}, -isbn = {1522-2594 (Electronic)}, -issn = {07403194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {CO2 reactivity,Cerebral blood flow,Cerebrospinal fluid,Functional responses,Magnetic resonance imaging,Magnetic resonance spectroscopy,Vasodilatation}, -month = {mar}, -number = {3}, -pages = {579--586}, -pmid = {19132756}, -title = {{Functional changes in CSF volume estimated using measurement of water T2 relaxation}}, -url = {http://doi.wiley.com/10.1002/mrm.21897}, -volume = {61}, -year = {2009} -} @article{Puts2013, abstract = {PURPOSE: To measure the in vivo longitudinal relaxation time T1 of GABA at 3 Tesla (T).\n\nMATERIALS AND METHODS: J-difference edited single-voxel MR spectroscopy was used to isolate $\gamma$-aminobutyric acid (GABA) signals. An increased echo time (80 ms) acquisition was used, accommodating the longer, more selective editing pulses required for symmetric editing-based suppression of co-edited macromolecular signal. Acquiring edited GABA measurements at a range of relaxation times in 10 healthy participants, a saturation-recovery equation was used to model the integrated data.\n\nRESULTS: The longitudinal relaxation time of GABA was measured as T(1,GABA) = 1.31 ± 0.16 s.\n\nCONCLUSION: The method described has been successfully applied to measure the T1 of GABA in vivo at 3T.}, author = {Puts, Nicolaas A.J. and Barker, Peter B and Edden, Richard A.E.}, @@ -263,6 +149,40 @@ @article{Puts2013 volume = {37}, year = {2013} } +@article{Klose1990, +abstract = {Spatially localized methods in spectroscopy often operate with magnetic field gradients for volume selection. The eddy currents induced by these gradients produce time-dependent shifts of the resonance frequency in the selected volume, which results in a distortion of the spectrum after Fourier transformation. In whole-body systems the complete compensation of eddy currents is a difficult procedure. To avoid this, a correction method is proposed for proton spectroscopy, which uses the signal of prominent water protons as a reference for the water-suppressed signal. The correction is performed in the time domain, dividing the water-suppressed signal by the phase factor of the water signal for each data point. The corrected spectra have a good resolution as shown by phantom measurements and brain and muscle spectra of volunteers.}, +author = {Klose, Uwe}, +doi = {10.1002/mrm.1910140104}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Klose/In vivo proton spectroscopy in presence of eddy currents.pdf:pdf}, +issn = {07403194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {Extracellular Space,Fourier Analysis,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: methods,Protons,Time Factors}, +month = {apr}, +number = {1}, +pages = {26--30}, +pmid = {2161984}, +title = {{In vivo proton spectroscopy in presence of eddy currents}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/2161984 http://doi.wiley.com/10.1002/mrm.1910140104}, +volume = {14}, +year = {1990} +} +@article{Kreis2004, +abstract = {In spite of the facts that magnetic resonance spectroscopy (MRS) is applied as clinical tool in non-specialized institutions and that semi-automatic acquisition and processing tools can be used to produce quantitative information from MRS exams without expert information, issues of spectral quality and quality assessment are neglected in the literature of MR spectroscopy. Even worse, there is no consensus among experts on concepts or detailed criteria of quality assessment for MR spectra. Furthermore, artifacts are not at all conspicuous in MRS and can easily be taken for true, interpretable features. This article aims to increase interest in issues of spectral quality and quality assessment, to start a larger debate on generally accepted criteria that spectra must fulfil to be clinically and scientifically acceptable, and to provide a sample gallery of artifacts, which can be used to raise awareness for potential pitfalls in MRS.}, +author = {Kreis, Roland}, +doi = {10.1002/nbm.891}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Kreis/Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts.pdf:pdf}, +issn = {0952-3480}, +journal = {NMR in Biomedicine}, +keywords = {Algorithms,Artifacts,Biomedical,Biomedical: methods,Clinical Medicine,Clinical Medicine: methods,Computer-Assisted,Computer-Assisted: methods,Diagnosis,Equipment Failure,Equipment Failure Analysis,Equipment Failure Analysis: methods,Health Care,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: instrumentation,Magnetic Resonance Spectroscopy: methods,Protons,Quality Assurance,Reproducibility of Results,Sensitivity and Specificity,Technology Assessment}, +month = {oct}, +number = {6}, +pages = {361--381}, +pmid = {15468083}, +title = {{Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/15468083 http://doi.wiley.com/10.1002/nbm.891 https://onlinelibrary.wiley.com/doi/10.1002/nbm.891}, +volume = {17}, +year = {2004} +} @article{Wansapura1999, abstract = {Relaxation time measurements at 3.0 T are reported for both gray and white matter in normal human brain. Measurements were made using a 3.0 T Bruker Biospec magnetic resonance imaging (MRI) scanner in normal adults with no clinical evidence of neurological disease. Nineteen subjects, 8 female and 11 male, were studied for T1 and T2 measurements, and 7 males were studied for T*2. Measurements were made using a saturation recovery method for T1, a multiple spin-echo experiment for T2, and a fast low-angle shot (FLASH) sequence with 14 different echo times for T*2. Results of the measurements are summarized as follows. Average T1 values measured for gray matter and white matter were 1331 and 832 msec, respectively. Average T2 values measured for gray matter and white matter were 80 and 110 msec, respectively. The average T*2 values for occipital and frontal gray matter were 41.6 and 51.8 msec, respectively. Average T*2 values for occipital and frontal white matter were 48.4 and 44.7 msec, respectively. ANOVA tests of the measurements revealed that for both gray and white matter there were no significant differences in T1 from one location in the brain to another. T2 in occipital gray matter was significantly higher (0.0001 < P < .0375) than the rest of the gray matter, while T2 in frontal white matter was significantly lower (P < 0.0001). Statistical analysis of cerebral hemispheric differences in relaxation time measurements showed no significant differences in T1 values from the left hemisphere compared with the right, except in insular gray matter, where this difference was significant at P = 0.0320. No significant difference in T2 values existed between the left and right cerebral hemispheres. Significant differences were apparent between male and female relaxation time measurements in brain. J. Magn. Reson. Imaging 1999;9:531–538. {\textcopyright} 1999 Wiley-Liss, Inc.}, author = {Wansapura, Janaka P and Holland, Scott K and Dunn, R Scott and Ball, William S}, @@ -281,6 +201,40 @@ @article{Wansapura1999 volume = {9}, year = {1999} } +@article{Barkhuijsen1987, +abstract = {The authors are concerned with a new method of fitting a physical model function to a magnetic resonance signal, directly in the time domain. Their primary aim is analysis of the signal in quantitative terms, i.e., describing the signal in terms of physically meaningful parameters with their statistical errors. Before explaining the new method they make some remarks about the place of time-domain model fitting in spectral analysis}, +author = {Barkhuijsen, H. and de Beer, R. and van Ormondt, D.}, +doi = {10.1016/0022-2364(87)90023-0}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Barkhuijsen, de Beer, van Ormondt/Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals.pdf:pdf}, +isbn = {0022-2364}, +issn = {00222364}, +journal = {Journal of Magnetic Resonance}, +month = {jul}, +number = {3}, +pages = {553--557}, +title = {{Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals}}, +url = {http://linkinghub.elsevier.com/retrieve/pii/0022236487900230 https://linkinghub.elsevier.com/retrieve/pii/0022236487900230}, +volume = {73}, +year = {1987} +} +@article{Edden2012, +abstract = {PURPOSE: To develop an experimental approach for determining in vivo transverse relaxation rates (T(2)) of metabolites that are detected by spectral editing without using simulations, and to demonstrate this approach to measure the T(2) of $\gamma$-aminobutyric acid (GABA).\n\nMATERIALS AND METHODS: The proposed method first determines the TE-dependence of the edited signals using measurements in a pure phantom solution (10 mM $\gamma$-aminobutyric acid; GABA); the phantom T(2) is also determined. Once the editing echo time (TE)-modulation pattern is known, it can then be used to determine T(2) in vivo. The method was applied to measure GABA T(2) in the occipital lobe of five healthy adult subjects at 3T, using a J-difference editing method. Unwanted macromolecular contributions to the GABA signal were also measured.\n\nRESULTS: The in vivo T(2) of edited GABA signal was 88 ± 12 ms; this preliminary result is somewhat shorter than other metabolite T(2) values in the literature at this field strength.\n\nCONCLUSION: Spectral editing methods are now widely used to detect low concentration metabolites, such as GABA, but to date no edited acquisition methods have been proposed for the measurement of transverse relaxation times (T(2)). The method described has been successfully applied to measuring the T(2) of GABA.}, +author = {Edden, Richard A.E. and Intrapiromkul, Jarunee and Zhu, He and Cheng, Ying and Barker, Peter B}, +doi = {10.1002/jmri.22865}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Edden et al/Measuring T2 in vivo with J-difference editing Application to GABA at 3 Tesla.pdf:pdf}, +isbn = {1522-2586 (Electronic)\r1053-1807 (Linking)}, +issn = {10531807}, +journal = {Journal of Magnetic Resonance Imaging}, +keywords = {GABA,T 2,brain,edited MR spectroscopy,transverse relaxation}, +month = {jan}, +number = {1}, +pages = {229--234}, +pmid = {22045601}, +title = {{Measuring T2 in vivo with J-difference editing: Application to GABA at 3 Tesla}}, +url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3377980&tool=pmcentrez&rendertype=abstract http://doi.wiley.com/10.1002/jmri.22865}, +volume = {35}, +year = {2012} +} @article{An2013, abstract = {Purpose: To propose using the generalized least square (GLS) algorithm for combining multichannel single-voxel magnetic resonance spectroscopy (MRS) signals. Materials and Methods: Phantom and in vivo brain MRS experiments on a 7 T scanner equipped with a 32-channel receiver coil, as well as Monte Carlo simulations, were performed to compare the coefficient of variation (CV) of the GLS method with those of two recently reported spectral combination methods. Results: Compared to the two existing methods, the GLS method significantly reduced CV values for the simulation, phantom, and in vivo experiments. Conclusion: The GLS method can lead to improved precision of peak quantification. J. Magn. Reson. Imaging 2013;37:14451450. (c) 2012 Wiley Periodicals, Inc.}, archivePrefix = {arXiv}, @@ -302,35 +256,95 @@ @article{An2013 volume = {37}, year = {2013} } -@article{Kreis2004, -abstract = {In spite of the facts that magnetic resonance spectroscopy (MRS) is applied as clinical tool in non-specialized institutions and that semi-automatic acquisition and processing tools can be used to produce quantitative information from MRS exams without expert information, issues of spectral quality and quality assessment are neglected in the literature of MR spectroscopy. Even worse, there is no consensus among experts on concepts or detailed criteria of quality assessment for MR spectra. Furthermore, artifacts are not at all conspicuous in MRS and can easily be taken for true, interpretable features. This article aims to increase interest in issues of spectral quality and quality assessment, to start a larger debate on generally accepted criteria that spectra must fulfil to be clinically and scientifically acceptable, and to provide a sample gallery of artifacts, which can be used to raise awareness for potential pitfalls in MRS.}, -author = {Kreis, Roland}, -doi = {10.1002/nbm.891}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Kreis/Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts.pdf:pdf}, +@article{Gasparovic2006, +abstract = {A strategy for using tissue water as a concentration standard in (1)H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with FAST multispectral segmentation are reported: GM [NAc] = 17.16 +/- 1.19 mM; WM [NAc] = 14.26 +/- 1.38 mM; GM [Ch] = 3.27 +/- 0.47 mM; WM [Ch] = 2.65 +/- 0.25 mM; GM [Cr] = 13.98 +/- 1.20 mM; and WM [Cr] = 7.10 +/- 0.67 mM.}, +author = {Gasparovic, Charles and Song, Tao and Devier, Deidre and Bockholt, H Jeremy and Caprihan, Arvind and Mullins, Paul G and Posse, Stefan and Jung, Rex E and Morrison, Leslie A}, +doi = {10.1002/mrm.20901}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Gasparovic et al/Use of tissue water as a concentration reference for proton spectroscopic imaging.pdf:pdf}, +isbn = {0740-3194 (Print)\n0740-3194 (Linking)}, +issn = {0740-3194}, +journal = {Magnetic Resonance in Medicine}, +keywords = {1H-MRS,Relaxation times,Spectroscopic imaging,Tissue water,Voxel}, +month = {jun}, +number = {6}, +pages = {1219--1226}, +pmid = {16688703}, +title = {{Use of tissue water as a concentration reference for proton spectroscopic imaging}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/16688703 http://doi.wiley.com/10.1002/mrm.20901}, +volume = {55}, +year = {2006} +} +@article{Evans2013, +abstract = {PURPOSE: To compare the repeatability of $\gamma$-aminobutyric acid (GABA) measurements using J-difference editing, before and after spectral realignment-a technique which has previously been demonstrated to improve the quality of J-difference GABA spectra.\n\nMATERIALS AND METHODS: We performed in vivo measurements in three brain regions (occipital, sensorimotor, and dorsolateral prefrontal cortex [DLPFC]), and analyzed these using alternative alignment approaches to evaluate the impact of alignment on repeatability: "Independent alignment" (aligning each subspectrum independently) and "Pairwise alignment" (aligning each on and off subspectrum as a pair) were compared.\n\nRESULTS: Pairwise alignment improved the group mean coefficient of variation in all regions; 0.4% in occipital, 1.1% in sensorimotor, and 1.1% in DLPFC. Independent alignment resulted in subtraction artifacts in the majority of cases, and increased the coefficient of variation in the DLPFC by 9.4%. Simulations demonstrate that the GABA quantification error in datasets with high B0 drift, is 4.5% without alignment, but <1% with optimal alignment.\n\nCONCLUSION: Pairwise alignment improves the repeatability of GABA spectroscopy data. However, independently aligning all on and off subspectra can lead to artifacts and worse repeatability when compared with nonaligned data.}, +author = {Evans, C. John and Puts, Nicolaas A.J. and Robson, Si{\^{a}}n E. and Boy, Frederic and McGonigle, David J. and Sumner, Petroc and Singh, Krish D. and Edden, Richard A.E.}, +doi = {10.1002/jmri.23923}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Evans et al/Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing.pdf:pdf}, +issn = {10531807}, +journal = {Journal of Magnetic Resonance Imaging}, +keywords = {GABA,MRS,frequency alignment,repeatability,subtraction artifact}, +month = {oct}, +number = {4}, +pages = {970--975}, +pmid = {23188759}, +title = {{Subtraction artifacts and frequency (Mis-)alignment in J-difference GABA editing}}, +url = {http://www.ncbi.nlm.nih.gov/pubmed/23188759 http://doi.wiley.com/10.1002/jmri.23923 https://onlinelibrary.wiley.com/doi/10.1002/jmri.23923}, +volume = {38}, +year = {2013} +} +@article{Hui2021a, +author = {Hui, Steve C.N. and Mikkelsen, Mark and Z{\"{o}}llner, Helge J. and Ahluwalia, Vishwadeep and Alcauter, Sarael and Baltusis, Laima and Barany, Deborah A and Barlow, Laura R and Becker, Robert and Berman, Jeffrey I and Berrington, Adam and Bhattacharyya, Pallab K and Blicher, Jakob Udby and Bogner, Wolfgang and Brown, Mark S and Calhoun, Vince D and Castillo, Ryan and Cecil, Kim M and Choi, Yeo Bi and Chu, Winnie C.W. and Clarke, William T and Craven, Alexander R and Cuypers, Koen and Dacko, Michael and de la Fuente-Sandoval, Camilo and Desmond, Patricia and Domagalik, Aleksandra and Dumont, Julien and Duncan, Niall W and Dydak, Ulrike and Dyke, Katherine and Edmondson, David A and Ende, Gabriele and Ersland, Lars and Evans, C John and Fermin, Alan S.R. and Ferretti, Antonio and Fillmer, Ariane and Gong, Tao and Greenhouse, Ian and Grist, James T and Gu, Meng and Harris, Ashley D and Hat, Katarzyna and Heba, Stefanie and Heckova, Eva and Hegarty, John P. and Heise, Kirstin-friederike and Honda, Shiori and Jacobson, Aaron and Jansen, Jacobus F.A. and Jenkins, Christopher W and Johnston, Stephen J and Juchem, Christoph and Kangarlu, Alayar and Kerr, Adam B and Landheer, Karl and Lange, Thomas and Lee, Phil and Levendovszky, Swati Rane and Limperopoulos, Catherine and Liu, Feng and Lloyd, William and Lythgoe, David J and Machizawa, Maro G and MacMillan, Erin L. and Maddock, Richard J and Manzhurtsev, Andrei V and Martinez-Gudino, Mar{\'{i}}a L. and Miller, Jack J and Mirzakhanian, Heline and Moreno-Ortega, Marta and Mullins, Paul G and Nakajima, Shinichiro and Near, Jamie and Noeske, Ralph and Nordh{\o}y, Wibeke and Oeltzschner, Georg and Osorio-Duran, Raul and Otaduy, Maria C.G. and Pasaye, Erick H and Peeters, Ronald and Peltier, Scott J and Pilatus, Ulrich and Polomac, Nenad and Porges, Eric C and Pradhan, Subechhya and Prisciandaro, James Joseph and Puts, Nicolaas A and Rae, Caroline D and Reyes-Madrigal, Francisco and Roberts, Timothy P.L. and Robertson, Caroline E and Rosenberg, Jens T and Rotaru, Diana-georgiana and {O'Gorman Tuura}, Ruth L and Saleh, Muhammad G and Sandberg, Kristian and Sangill, Ryan and Schembri, Keith and Schrantee, Anouk and Semenova, Natalia A and Singel, Debra and Sitnikov, Rouslan and Smith, Jolinda and Song, Yulu and Stark, Craig and Stoffers, Diederick and Swinnen, Stephan P. and Tain, Rongwen and Tanase, Costin and Tapper, Sofie and Tegenthoff, Martin and Thiel, Thomas and Thioux, Marc and Truong, Peter and van Dijk, Pim and Vella, Nolan and Vidyasagar, Rishma and Vovk, Andrej and Wang, Guangbin and Westlye, Lars T and Wilbur, Timothy K and Willoughby, William R and Wilson, Martin and Wittsack, Hans-J{\"{o}}rg and Woods, Adam J and Wu, Yen-Chien and Xu, Junqian and Lopez, Maria Yanez and Yeung, David K.W. and Zhao, Qun and Zhou, Xiaopeng and Zupan, Gasper and Edden, Richard A.E.}, +doi = {10.1016/j.neuroimage.2021.118430}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Hui et al/Frequency drift in MR spectroscopy at 3T.pdf:pdf}, +issn = {10538119}, +journal = {NeuroImage}, +month = {nov}, +number = {21}, +pages = {118430}, +title = {{Frequency drift in MR spectroscopy at 3T}}, +url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921007059}, +volume = {241}, +year = {2021} +} +@article{Harris2015, +author = {Harris, Ashley D. and Puts, Nicolaas A.J. and Edden, Richard A.E.}, +doi = {10.1002/jmri.24903}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations.pdf:pdf;:Users/mmikkel5/Documents/Mendeley Desktop/Harris, Puts, Edden/Tissue correction for GABA-edited MRS Considerations of voxel composition, tissue segmentation, and tissue relaxations(2).pdf:pdf}, +issn = {10531807}, +journal = {Journal of Magnetic Resonance Imaging}, +month = {nov}, +number = {5}, +pages = {1431--1440}, +title = {{Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations}}, +url = {http://doi.wiley.com/10.1002/jmri.24903}, +volume = {42}, +year = {2015} +} +@article{Mikkelsen2017, +author = {Mikkelsen, Mark and Barker, Peter B and Bhattacharyya, Pallab K and Brix, Maiken K and Buur, Pieter F. and Cecil, Kim M and Chan, Kimberly L and Chen, David Y.-T. and Craven, Alexander R and Cuypers, Koen and Dacko, Michael and Duncan, Niall W and Dydak, Ulrike and Edmondson, David A and Ende, Gabriele and Ersland, Lars and Gao, Fei and Greenhouse, Ian and Harris, Ashley D and He, Naying and Heba, Stefanie and Hoggard, Nigel and Hsu, Tun-wei and Jansen, Jacobus F.A. and Kangarlu, Alayar and Lange, Thomas and Lebel, R Marc and Li, Yan and Lin, Chien-yuan E and Liou, Jy-kang and Lirng, Jiing-Feng and Liu, Feng and Ma, Ruoyun and Maes, Celine and Moreno-Ortega, Marta and Murray, Scott O and Noah, Sean and Noeske, Ralph and Noseworthy, Michael D and Oeltzschner, Georg and Prisciandaro, James J. and Puts, Nicolaas A.J. and Roberts, Timothy P.L. and Sack, Markus and Sailasuta, Napapon and Saleh, Muhammad G and Schallmo, Michael-paul and Simard, Nicholas and Swinnen, Stephan P. and Tegenthoff, Martin and Truong, Peter and Wang, Guangbin and Wilkinson, Iain D and Wittsack, Hans-J{\"{o}}rg and Xu, Hongmin and Yan, Fuhua and Zhang, Chencheng and Zipunnikov, Vadim and Z{\"{o}}llner, Helge J. and Edden, Richard A.E.}, +doi = {10.1016/j.neuroimage.2017.07.021}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Big GABA Edited MR spectroscopy at 24 research sites.pdf:pdf}, +issn = {10538119}, +journal = {NeuroImage}, +month = {oct}, +pages = {32--45}, +publisher = {Elsevier Inc.}, +title = {{Big GABA: Edited MR spectroscopy at 24 research sites}}, +url = {https://doi.org/10.1016/j.neuroimage.2017.07.021 https://linkinghub.elsevier.com/retrieve/pii/S105381191730589X}, +volume = {159}, +year = {2017} +} +@article{Mikkelsen2020, +author = {Mikkelsen, Mark and Tapper, Sofie and Near, Jamie and Mostofsky, Stewart H. and Puts, Nicolaas A. J. and Edden, Richard A. E.}, +doi = {10.1002/nbm.4368}, +file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Correcting frequency and phase offsets in MRS data using robust spectral registration.pdf:pdf}, issn = {0952-3480}, journal = {NMR in Biomedicine}, -keywords = {Algorithms,Artifacts,Biomedical,Biomedical: methods,Clinical Medicine,Clinical Medicine: methods,Computer-Assisted,Computer-Assisted: methods,Diagnosis,Equipment Failure,Equipment Failure Analysis,Equipment Failure Analysis: methods,Health Care,Humans,Magnetic Resonance Spectroscopy,Magnetic Resonance Spectroscopy: instrumentation,Magnetic Resonance Spectroscopy: methods,Protons,Quality Assurance,Reproducibility of Results,Sensitivity and Specificity,Technology Assessment}, month = {oct}, -number = {6}, -pages = {361--381}, -pmid = {15468083}, -title = {{Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts}}, -url = {http://www.ncbi.nlm.nih.gov/pubmed/15468083 http://doi.wiley.com/10.1002/nbm.891 https://onlinelibrary.wiley.com/doi/10.1002/nbm.891}, -volume = {17}, -year = {2004} -} -@article{Mikkelsen2018, -author = {Mikkelsen, Mark and Saleh, Muhammad G. and Near, Jamie and Chan, Kimberly L. and Gong, Tao and Harris, Ashley D. and Oeltzschner, Georg and Puts, Nicolaas A.J. and Cecil, Kim M. and Wilkinson, Iain D. and Edden, Richard A.E.}, -doi = {10.1002/mrm.27027}, -file = {:Users/mmikkel5/Documents/Mendeley Desktop/Mikkelsen et al/Frequency and phase correction for multiplexed edited MRS of GABA and glutathione.pdf:pdf}, -issn = {07403194}, -journal = {Magnetic Resonance in Medicine}, -keywords = {frequency,gaba,gsh,her-,mes,mrs,phase correction,spectral registration}, -month = {jul}, -number = {1}, -pages = {21--28}, -title = {{Frequency and phase correction for multiplexed edited MRS of GABA and glutathione}}, -url = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.27027}, -volume = {80}, -year = {2018} +number = {10}, +pages = {e4368}, +pmid = {32656879}, +title = {{Correcting frequency and phase offsets in MRS data using robust spectral registration}}, +url = {https://onlinelibrary.wiley.com/doi/10.1002/nbm.4368}, +volume = {33}, +year = {2020} } diff --git a/data-quality-metrics.Rmd b/data-quality-metrics.Rmd index b37d0dd..78e819c 100644 --- a/data-quality-metrics.Rmd +++ b/data-quality-metrics.Rmd @@ -1,7 +1,7 @@ --- title: "Data quality metrics" date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`" -bibliography: references.bib +bibliography: bibliography.bib csl: american-medical-association.csl link-citations: yes output: @@ -35,11 +35,11 @@ Listed below are the data quality metrics that Gannet computes during data proce ## Linewidth -Linewidth is calculated as the full-width half-maximum (FWHM) (in Hz) of fitted signals. When reporting linewidths of datasets, you may use the FWHM of Cr, NAA, or the water reference (if provided). +Linewidth is calculated as the full-width half-maximum (FWHM) (in Hz) of fitted model signals. When reporting linewidths of datasets, you may choose to use the FWHM of Cr, NAA, or the water reference (if a water reference is provided). ## Signal-to-noise ratio (SNR) -The SNR of fitted signals is calculated as the amplitude of the given modeled signal divided by twice the standard deviation of the noise signal. To estimate noise, Gannet takes two independent segments of the OFF or DIFF spectrum (as appropriate to the modeled signal of interest) between 8–9 ppm and 9–10 ppm, and detrends them using a second-order polynomial function. The standard deviation of each detrended noise segment is then calculated. Detrending is required to remove baseline artifacts (often related to the water signal). The smaller of the two standard deviations is then used as the estimate of noise, which is then multiplied by 2. +The SNR of fitted model signals is calculated as the amplitude of the given modeled signal divided by twice the standard deviation of the noise signal. To estimate the noise signal, Gannet takes two independent segments of the OFF or DIFF spectrum (as appropriate to the modeled signal of interest) between 8–9 ppm and 9–10 ppm, and detrends them using a second-order polynomial function. The standard deviation of each detrended noise segment is then calculated. Detrending is performed to remove baseline artifacts (often related to the residual water signal). The smaller of the two standard deviations is then used as the estimate of noise, which is then multiplied by 2. Formulaically, this is defined as: @@ -56,7 +56,18 @@ where: ## Frequency offsets (frequency drift/motion) -[TEXT] +To estimate the degree of frequency offsets that result from scanner-related frequency drift [@Hui2021a] and participant motion [@Evans2013], Gannet calculates the average frequency offset $\overline{\Delta\delta_{0}}$ [@Mikkelsen2017]. This is calculated as the mean (over the course of the acquisition) difference between the observed frequency of the residual water signal in the pre-frequency-corrected subspectra and the nominal water frequency $\delta_{0}$ at 4.68 ppm (4.8 ppm for room-temperature phantoms), or the nominal Cr frequency at 3.02 ppm for HERMES acquisitions. It should be noted that using the mean of offset differences does not fully characterize frequency offsets but is a useful heuristic. + +$$ +\overline{\Delta\delta_{0}} = \frac{1}{m}\sum{\widehat{\delta_{0,m}} - \delta_{0}} +$$ +where: + +| Parameter | Definition | +| :- | :--------- | +| $m$ | Each individual subspectrum index number | +| $\widehat{\delta_{0,m}}$ | Observed water or Cr frequency in each individual subspectrum | +| $\delta_{0}$ | Nominal water or Cr frequency | ## Fit error @@ -89,5 +100,9 @@ $$ [TEXT] +
+ +### References + diff --git a/faq.Rmd b/faq.Rmd index fb195cd..424d2b6 100755 --- a/faq.Rmd +++ b/faq.Rmd @@ -33,29 +33,29 @@ If your issue has not been addressed or is not found in this FAQ, please report ## General -#### **Can Gannet process non-edited data?** +#### **1. Can Gannet process non-edited data?** No, Gannet is designed to process edited single-voxel data only. However, the standalone tool [CoRegStandAlone](https://markmikkelsen.github.io/Gannet-docs/additional-tools.html#coregstandalone) can be used with non-edited single-voxel data to co-register voxels to structural images and segment the images to obtain voxel tissue fractions. -#### **Can Gannet process MRSI data?** +#### **2. Can Gannet process MRSI data?** No. -#### **Can Gannet process GSH-edited data?** +#### **3. Can Gannet process GSH-edited data?** Yes, Gannet can process GABA-, GSH-, Lac-, and EtOH-edited MEGA-PRESS data, as well as GABA-/GSH-, Lac-/GSH-, and EtOH-/GABA-/GSH-edited HERMES and HERCULES data. (Fitting of HERCULES phantom data is not supported, however.) Simply set the appropriate `target` option(s) in `GannetPreInitialise.m`. -#### **Can Gannet process HERMES/HERCULES data?** +#### **4. Can Gannet process HERMES/HERCULES data?** `GannetLoad.m` can load and preprocess HERMES and HERCULES data, but only HERMES data can be modeled in `GannetFit.m`. HERCULES data can be fully processed and fitted using the Osprey software package. -#### **Can Gannet process phantom data?** +#### **5. Can Gannet process phantom data?** Yes, Gannet can process and fit phantom edited data as described above. Make sure to set `phantom` to `1` under `Flags` in `GannetPreInitialise.m`. If you want to fit your data, run `GannetFitPhantom(MRS_struct);` instead of `GannetFit(MRS_struct);`. (Fitting of HERCULES phantom data is not supported.) ## Output -#### **Why is my spectrum upside down?** +#### **1. Why is my spectrum upside down?** A screenshot example of upside-down MRS spectra @@ -67,7 +67,7 @@ MRS_struct.fids.data = -MRS_struct.fids.data; ## Errors -#### **You have issues with SPM MEX files on macOS Catalina, Big Sur, Monterey, Ventura** +#### **1. You have issues with SPM MEX files on macOS Catalina, Big Sur, Monterey, Ventura** Specifically: @@ -83,7 +83,7 @@ Code signature not valid for use in process using Library Validation: library lo Please visit the SPM Wikibooks page for troubleshooting. -#### **MATLAB says my structural image does not exist** +#### **2. MATLAB says my structural image does not exist** For example: diff --git a/index.Rmd b/index.Rmd index 9461749..a5a5e08 100755 --- a/index.Rmd +++ b/index.Rmd @@ -61,13 +61,13 @@ The simplest way to install Gannet is to download the latest source code as a ZI Alternatively, Git users can clone the Gannet repository into a directory of their choice: -```{bash, eval = FALSE} +```{verbatim, eval = FALSE} git clone https://github.com/markmikkelsen/Gannet.git ``` The development version of Gannet can be downloaded from the development branch on GitHub or by using the following git command if the repository was cloned: -```{bash, eval = FALSE} +```{verbatim, eval = FALSE} git checkout dev ``` diff --git a/preprocessing.Rmd b/preprocessing.Rmd index e795af3..29a7f16 100644 --- a/preprocessing.Rmd +++ b/preprocessing.Rmd @@ -1,7 +1,7 @@ --- title: "Preprocessing" date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`" -bibliography: references.bib +bibliography: bibliography.bib csl: american-medical-association.csl link-citations: yes output: diff --git a/quantification-tissue-correction.Rmd b/quantification-tissue-correction.Rmd index 26bbfca..99ec471 100755 --- a/quantification-tissue-correction.Rmd +++ b/quantification-tissue-correction.Rmd @@ -1,7 +1,7 @@ --- title: "Quantification & tissue correction" date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`" -bibliography: references.bib +bibliography: bibliography.bib csl: american-medical-association.csl link-citations: yes output: diff --git a/reference.Rmd b/reference.Rmd index 9bd88dd..c637977 100755 --- a/reference.Rmd +++ b/reference.Rmd @@ -1,15 +1,15 @@ --- title: "Reference" date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`" +bibliography: bibliography.bib +csl: american-medical-association.csl +link-citations: yes output: html_document: toc: TRUE toc_depth: 2 toc_float: collapsed: FALSE -bibliography: references.bib -csl: american-medical-association.csl -link-citations: yes --- ```{r setup, include = FALSE} diff --git a/signal-modeling.Rmd b/signal-modeling.Rmd index cd0186c..0998e31 100644 --- a/signal-modeling.Rmd +++ b/signal-modeling.Rmd @@ -1,7 +1,7 @@ --- title: "Signal modeling" date: "Last updated: `r format(Sys.time(), '%B %d, %Y')`" -bibliography: references.bib +bibliography: bibliography.bib csl: american-medical-association.csl link-citations: yes output: