This repository is a snapshot of processing code used for a publication, which will be linked here as soon as its released. This code is not maintained, its use is purely to provide the current state of data analysis for reproducibility.
This code consists of two python modules that are used by four data analysis jupyter notebooks.
- hypermri: This module is found in hypermri-publication-version and can be installed as described below. Its use is for loading, analyzing and processing preclinical MRI and MRS data from a Bruker MRI operated by Paravision 7.
- MagriProc.py: This module is used for loading, analyzing and processing NMR data from a Spinsolve tabletop spectrometer by Magritek.
All these notebooks should be opened using jupyter lab version 7.
-
Example_T1_T2_analysis.ipynb
This notebook shows exemplary how T1 and T2 values were analyzed for this study.
-
Example_Polarization_level_calculation.ipynb
This notebook shows how the polarization level was calculated.
-
Example_Perfusion_Measurement.ipynb
This notebook shows how in vivo measurements using a bssfp sequence were analyzed for perfusion experiments.
-
Example_Metabolism_Measurement.ipynb
This notebook shows how in vivo measurements using a bssfp sequence were analyzed for tumor metabolism measurements.
- Clone this repository, i.e. using Windows Powershell or Mac Terminal:
$ git clone https://github.com/QuE-MRT/2023_TUMKRI_NVIS_PHIPvsDNPInVivoStudy
- Navigate to the folder containing the hypermri package, create an environment and install the package
$ cd hypermri_publication_version
$ conda conda create -n hypermri_env python==3.10.10
$ conda activate hypermri_env
(hypermri_env)$ pip install -r dev-requirements.txt
(hypermri_env)$ pip install -e ".[dev]"
- Change to the main folder and open jupyter lab (version>=3.5.3):
(hypermri_env)$ cd ..
(hypermri_env)$ jupyter lab
- Test data is available upon request and should be placed in the same directory as the notebooks.
This is a static repository and contributing is not possible.
This repository is licensed under Apache 2.0.
However code from two external repositories (pyNMR by Benno Meier and brukerMRI by Joerg Döpfert) is integrated into some functions. Therefore a folder with their respective Licenses is included here as well and their code is cited within the packages.