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

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

MRIQC extracts no-reference image quality metrics from structural (T1w and T2w), functional and diffusion MRI data.

This package provides the version schemes used for packaging software from the NiPreps organization.

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

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.

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

Nipype provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages.

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).

Convert data from DICOM and organise the resulting NIfTI files into BIDS.

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

NIPY provides a platform-independent Python environment for the analysis of functional brain imaging data.

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

TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional MRI data.

The Insight Toolkit (ITK) is a toolkit for N-dimensional scientific image processing, segmentation, and registration. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

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

This package provides processing pipelines for structural MRI.

dcm2niix is designed to convert neuroimaging data from the DICOM format to the NIfTI format. dcm2niix is also able to generate a BIDS JSON format sidecar which includes relevant information for brain scientists in a vendor agnostic and human readable form.

MRtrix3 provides a large suite of tools for image processing, analysis and visualisation, with a focus on the analysis of white matter using diffusion-weighted MRI.

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.

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.

This package provides programs to perform rigid, affine and non-linear registration of 2D and 3D images stored as NIfTI or Analyze formats.

Nitime contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

SDCFlows (Susceptibility Distortion Correction workFlows) is a Python library of NiPype-based workflows to preprocess B0 mapping data, estimate the corresponding fieldmap and finally correct for susceptibility distortions. Susceptibility-derived distortions are typically displayed by images acquired with EPI MR schemes.