NiftyPET: High-throughput image reconstruction and analysis

Documentation: https://niftypet.readthedocs.io/

NiftyPET is a software platform and a Python namespace package encompassing sub-packages for high-throughput PET image reconstruction, manipulation, processing and analysis with high quantitative accuracy and precision. See below for the description of the above image, reconstructed using NiftyPET [*].

NiftyPET includes two packages:

nimpa: https://github.com/pjmark/NIMPA (neuro-image manipulation, processing and analysis)

nipet: https://github.com/pjmark/NIPET (quantitative PET neuro-image reconstruction)

The core routines are written in CUDA C and embedded in Python C extensions. The scientific aspects of this software platform are covered in two open-access publications:

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis Neuroinformatics (2018) 16:95. https://doi.org/10.1007/s12021-017-9352-y
Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis Physics in Medicine & Biology (2016). https://doi.org/10.1088/0031-9155/61/13/N322

[*]

The above dynamic transaxial and coronal images show the activity of ¹⁸F-florbetapir during the one-hour dynamic acquisition. Note that the signal in the brain white matter dominates over the signal in the grey matter towards the end of the acquisition, which is a typical presentation of a negative amyloid beta (Abeta) scan.

Acknowledgements

This project is being developed at University College London (UCL). Initially, it was supported and funded by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom (UK). Currently, the project is being further developed under the following funding streams:

The Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
The Dementias Platform UK MR-PET Partnership, supported by the Medical Research Council (MRC) in the UK.

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K20 and Titan X Pascal GPUs used for this research and work.