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CUDA-accelerated Python utilities for high-throughput PET/MR image reconstruction and analysis.

Project description

Documentation Status

NIPET is a Python sub-package of NiftyPET, offering high-throughput PET image reconstruction as well as image processing and analysis (nimpa: https://github.com/pjmark/NIMPA) for PET/MR imaging with high quantitative accuracy and precision. The software is written in CUDA C and embedded in Python C extensions.

The scientific aspects of this software are covered in two open-access publications:

Although, the two stand-alone and independent packages, nipet and nimpa, are dedicated to brain imaging, they can equally well be used for whole body imaging. Strong emphasis is put on the data, which are acquired using positron emission tomography (PET) and magnetic resonance (MR), especially the hybrid and simultaneous PET/MR scanners.

This software platform and Python name-space NiftyPET covers the entire processing pipeline, from the raw list-mode (LM) PET data through to the final image statistic of interest (e.g., regional SUV), including LM bootstrapping and multiple reconstructions to facilitate voxel-wise estimation of uncertainties.

In order to facilitate all the functionality, NiftyPET relies on third-party software for image conversion from DICOM to NIfTI (dcm2niix) and image registration (NiftyReg). The additional software is installed automatically to a user specified location.

Documentation with installation manual and tutorials: https://niftypet.readthedocs.io/

Quick install

conda create -n niftypet python=2.7 \
  conda-forge::nibabel conda-forge::pydicom ipykernel matplotlib \
  conda-forge::tqdm conda-forge::ipywidgets
git clone https://github.com/pjmark/NIMPA.git nimpa
git clone https://github.com/pjmark/NIPET.git nipet
cd nimpa
pip install --no-binary :all: --verbose .
cd ../nipet
pip install --no-binary :all: --verbose .

Author: Pawel J. Markiewicz @ University College London

Copyright 2018

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