{"id":24547062,"url":"https://github.com/niftypet/nipet","last_synced_at":"2025-09-10T01:33:59.084Z","repository":{"id":46342457,"uuid":"110014332","full_name":"NiftyPET/NIPET","owner":"NiftyPET","description":"High-throughput PET image reconstruction with high quantitative accuracy and precision","archived":false,"fork":false,"pushed_at":"2025-07-02T17:08:07.000Z","size":1400,"stargazers_count":31,"open_issues_count":23,"forks_count":11,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-08-24T04:13:11.333Z","etag":null,"topics":["analysis","cuda","gpu","image-reconstruction","medical-imaging","mlem","pet","processing","python"],"latest_commit_sha":null,"homepage":"","language":"Cuda","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NiftyPET.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2017-11-08T18:27:31.000Z","updated_at":"2025-02-25T05:08:01.000Z","dependencies_parsed_at":"2023-12-17T22:42:18.878Z","dependency_job_id":"205e7ccc-bd7a-4114-a437-49d7b0c45e22","html_url":"https://github.com/NiftyPET/NIPET","commit_stats":{"total_commits":406,"total_committers":7,"mean_commits":58.0,"dds":0.5,"last_synced_commit":"e39dcaa2040ab3ed3b8cb85b7baf788f956bfcf2"},"previous_names":[],"tags_count":18,"template":false,"template_full_name":null,"purl":"pkg:github/NiftyPET/NIPET","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiftyPET%2FNIPET","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiftyPET%2FNIPET/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiftyPET%2FNIPET/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiftyPET%2FNIPET/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NiftyPET","download_url":"https://codeload.github.com/NiftyPET/NIPET/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiftyPET%2FNIPET/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274396495,"owners_count":25277393,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-09T02:00:10.223Z","response_time":80,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","cuda","gpu","image-reconstruction","medical-imaging","mlem","pet","processing","python"],"created_at":"2025-01-22T22:18:20.490Z","updated_at":"2025-09-10T01:33:59.041Z","avatar_url":"https://github.com/NiftyPET.png","language":"Cuda","funding_links":[],"categories":[],"sub_categories":[],"readme":"===========================================================\nNIPET: high-throughput Neuro-Image PET reconstruction\n===========================================================\n\n|Docs| |Version| |Downloads| |Py-Versions| |DOI| |Licence| |Tests|\n\nNIPET 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.\n\n.. _NiftyPET: https://github.com/NiftyPET/NiftyPET\n\nThe scientific aspects of this software are covered in two open-access publications:\n\n* *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\n\n* *Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis* Physics in Medicine \u0026 Biology (2016). https://doi.org/10.1088/0031-9155/61/13/N322\n\nAlthough, 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.\n\nThis 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.\n\nIn 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.\n\n**Documentation with installation manual and tutorials**: https://niftypet.readthedocs.io/\n\nQuick Install\n~~~~~~~~~~~~~\n\nNote that installation prompts for setting the path to ``NiftyPET_tools`` and\nhardware attenuation maps. This can be avoided by setting the environment\nvariables ``PATHTOOLS`` and ``HMUDIR``, respectively.\nIt's also recommended (but not required) to use `conda`.\n\n.. code:: sh\n\n    # optional (Linux syntax) to avoid prompts\n    export PATHTOOLS=$HOME/NiftyPET_tools\n    export HMUDIR=$HOME/mmr_hardwareumaps\n    # cross-platform install\n    conda install -c conda-forge python=3 \\\n      ipykernel numpy scipy scikit-image matplotlib ipywidgets dipy nibabel pydicom\n    pip install dcm2niix\n    pip install \"nipet\u003e=2\"\n\nExternal CMake Projects\n~~~~~~~~~~~~~~~~~~~~~~~\n\nThe raw C/CUDA libraries may be included in external projects using ``cmake``.\nSimply build the project and use ``find_package(NiftyPETnipet)``.\n\n.. code:: sh\n\n    # print installation directory (after `pip install nipet`)...\n    python -c \"from niftypet.nipet import cmake_prefix; print(cmake_prefix)\"\n\n    # ... or build \u0026 install directly with cmake\n    mkdir build \u0026\u0026 cd build\n    cmake ../niftypet \u0026\u0026 cmake --build . \u0026\u0026 cmake --install . --prefix /my/install/dir\n\nAt this point any external project may include NIPET as follows\n(Once setting ``-DCMAKE_PREFIX_DIR=\u003cinstallation prefix from above\u003e``):\n\n.. code:: cmake\n\n    cmake_minimum_required(VERSION 3.3 FATAL_ERROR)\n    project(myproj)\n    find_package(NiftyPETnipet COMPONENTS mmr_auxe mmr_lmproc petprj nifty_scatter REQUIRED)\n    add_executable(myexe ...)\n    target_link_libraries(myexe PRIVATE\n      NiftyPET::mmr_auxe NiftyPET::mmr_lmproc NiftyPET::petprj NiftyPET::nifty_scatter)\n\nLicence\n~~~~~~~\n\n|Licence| |DOI|\n\nCopyright 2018-21\n\n- `Pawel J. Markiewicz \u003chttps://github.com/pjmark\u003e`__ @ University College London\n- `Casper O. da Costa-Luis \u003chttps://github.com/casperdcl\u003e`__ @ King's College London\n- `Contributors \u003chttps://github.com/NiftyPET/NIPET/graphs/contributors\u003e`__\n\n.. |Docs| image:: https://readthedocs.org/projects/niftypet/badge/?version=latest\n   :target: https://niftypet.readthedocs.io/en/latest/?badge=latest\n.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4417679.svg\n   :target: https://doi.org/10.5281/zenodo.4417679\n.. |Licence| image:: https://img.shields.io/pypi/l/nipet.svg?label=licence\n   :target: https://github.com/NiftyPET/NIPET/blob/master/LICENCE\n.. |Tests| image:: https://img.shields.io/github/workflow/status/NiftyPET/NIPET/Test?logo=GitHub\n   :target: https://github.com/NiftyPET/NIPET/actions\n.. |Downloads| image:: https://img.shields.io/pypi/dm/nipet.svg?logo=pypi\u0026logoColor=white\u0026label=PyPI%20downloads\n   :target: https://pypi.org/project/nipet\n.. |Version| image:: https://img.shields.io/pypi/v/nipet.svg?logo=python\u0026logoColor=white\n   :target: https://github.com/NiftyPET/NIPET/releases\n.. |Py-Versions| image:: https://img.shields.io/pypi/pyversions/nipet.svg?logo=python\u0026logoColor=white\n   :target: https://pypi.org/project/nipet\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniftypet%2Fnipet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fniftypet%2Fnipet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniftypet%2Fnipet/lists"}