A Python implementation of the moving average principal components analysis methods for functional MRI data translated from the MATLAB-based GIFT package.

This package provides an implementation of TRX, a tractography file format designed to facilitate dataset exchange, interoperability, and state-of-the-art analyses, acting as a community-driven replacement for the myriad existing file formats.

Gifticlib is a a library for reading and writing files in GIfTI format. GIfTI is a standard for Geometry Data Format for Exchange of Surface-Based Brain Mapping Data.

The BIDS Validator is a web application, command-line utility, and Javascript/Typescript library for assessing compliance with the BIDS standard.

AFNI, Analysis of Functional NeuroImages is a suite of programs for looking at and analyzing MRI brain images at all stages of analysis (planning, setting up acquisition, preprocessing, analysis, quality control and statistical analysis).

Surfa is a collection of Python utilities for medical image analysis and mesh-based surface processing. It provides tools that operate on 3D image arrays and triangular meshes with consideration of their representation in a world (or scanner) coordinate system. While broad in scope, surfa is developed with particular emphasis on neuroimaging applications.

pybids provides a set of tools for working with BIDS datasets. The BIDS standard aims at organizing and describing neuroimaging data in a uniform way in order to facilitate data sharing within the scientific community.

heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.

This package provides DICOM to NIfTI conversion with the added ability to extract and summarize meta data from the source DICOM files. The meta data can be injected it into a NIfTI header extension or written out as a JSON formatted text file.

This package provides programs to perform EM based segmentation of images in nifti or analyse format.

The PETPVC toolbox comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data. Eight core PVC techniques are available, and those core methods can be combined to create a total of 22 different PVC techniques.

DIPY is the paragon 3D/4D+ medical imaging library in Python. It contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

This package provides Python tools for working with the BIDS schema.

The indexed_gzip project is a Python extension which aims to provide a drop-in replacement for the built-in Python gzip.GzipFile class, the IndexedGzipFile. indexed_gzip was written to allow fast random access of compressed NIFTI image files (for which GZIP is the de-facto compression standard), but will work with any GZIP file.

The fsleyes-widgets package contains a collection of GUI widgets and utilities, based on wxPython, which are used by fsleyes-props and FSLeyes.

Nilearn enables approachable and versatile analyses of brain volumes and surfaces. It provides statistical and machine-learning tools, with instructive documentation & open community.

Nifti_clib is a set of I/O libraries for reading and writing files in the nifti-1, nifti-2, and (to some degree) cifti file formats. These are binary file formats for storing medical image data, e.g. MRI and fMRI brain images.

migas (mee-gahs) is a Python client to facilitate communication with a migas server.

NiFreeze is a flexible framework for volume-to-volume motion estimation and correction in d/fMRI and PET, and eddy-current-derived distortion estimation in dMRI.

CiftiLib is a C++ library for CIFTI-2 file reading/writing. It additionally supports CIFTI-1 files, and supports both on-disk and in-memory access. It also provides C++ code for reading and writing generic NIfTI-1 and NIfTI-2 files.

CIFTI (Connectivity Informatics Technology Initiative) standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties.

See http://www.nitrc.org/projects/cifti/ for more information.

niworkflows is capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM).

The nipype1-workflows repository contains legacy workflows from Nipype 1.x, showcasing nearly a decade of development in neuroimaging data processing and analysis.

Framework to represent structured directories in python as FileTree objects. File trees can be read in from simple text files describing the directory structure. This is particularly useful for pipelines with large number of input, output, and intermediate files. It can also be used to visualise the data in structured directories using FSLeyes or file-tree on the command line.

The etelemetry Python client facilitates communication with the etelemetry server, providing version information and checking for critical bugs in projects. The client allows you to retrieve project details and compare versions to identify and warn about problematic versions.