Metadata-Version: 1.2
Name: nipet
Version: 1.1.22
Summary: CUDA-accelerated Python utilities for high-throughput PET/MR image reconstruction and analysis.
Home-page: https://github.com/NiftyPET/NiftyPET
Author: Pawel J. Markiewicz
Author-email: p.markiewicz@ucl.ac.uk
License: Apache 2.0
Description: ===========================================================
        NIPET: high-throughput Neuro-Image PET reconstruction
        ===========================================================
        
        |Docs| |PyPI-Status| |PyPI-Downloads|
        
        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/NiftyPET/NIMPA) for PET/MR imaging with high quantitative accuracy and precision. The software is written in CUDA C and embedded in Python C extensions.
        
        .. _NiftyPET: https://github.com/NiftyPET/NiftyPET
        
        The scientific aspects of this software 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
        
        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
        ~~~~~~~~~~~~~
        
        Note that installation prompts for setting the path to `NiftyPET_tools` and
        hardware attenuation maps. This can be avoided by setting the environment
        variables `PATHTOOLS` and `HMUDIR`, respectively.
        
        .. code:: sh
        
            # optional (Linux syntax) to avoid prompts
            export PATHTOOLS=$HOME/NiftyPET_tools
            export HMUDIR=$HOME/mmr_hardwareumaps
            # cross-platform install
            conda create -n niftypet -c conda-forge python=2.7 \
              ipykernel matplotlib numpy scikit-image ipywidgets
            git clone https://github.com/NiftyPET/NIMPA.git nimpa
            git clone https://github.com/NiftyPET/NIPET.git nipet
            conda activate niftypet
            pip install --no-binary :all: --verbose -e ./nimpa
            pip install --no-binary :all: --verbose -e ./nipet
        
        Licence
        ~~~~~~~
        
        |Licence|
        
        - Author: `Pawel J. Markiewicz <https://github.com/pjmark>`__ @ University College London
        - `Contributors <https://github.com/NiftyPET/NIPET/graphs/contributors>`__:
        
          - `Casper O. da Costa-Luis <https://github.com/casperdcl>`__ @ King's College London
        
        Copyright 2018-19
        
        .. |Docs| image:: https://readthedocs.org/projects/niftypet/badge/?version=latest
           :target: https://niftypet.readthedocs.io/en/latest/?badge=latest
        .. |Licence| image:: https://img.shields.io/pypi/l/nipet.svg?label=licence
           :target: https://github.com/NiftyPET/NIPET/blob/master/LICENCE
        .. |PyPI-Downloads| image:: https://img.shields.io/pypi/dm/nipet.svg?label=PyPI%20downloads
           :target: https://pypi.org/project/nipet
        .. |PyPI-Status| image:: https://img.shields.io/pypi/v/nipet.svg?label=latest
           :target: https://pypi.org/project/nipet
        
Keywords: PET image reconstruction and analysis
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: C
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: <3.0.0
