DIPY is the paragon 3D/4D+ medical imaging library in Python. 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.

[ICLR2023] Official repository of DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models

Preprocessing of diffusion MRI

The open source toolbox for reproducible diffusion MRI-based microstructure estimation

White matter tractography clustering and more...

dMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Generate subject connectomes from raw BIDS data & perform inter-subject connection density normalisation, using the MRtrix3 software package.

A hands-free DTI, DKI, FBI and FBWM preprocessing pipeline. Information on algorithms and preprocessing steps are available at https://www.biorxiv.org/content/10.1101/2021.10.20.465189v1 A video tutorial on PyDesigner and its usage is now available at https://www.youtube.com/watch?v=mChQFuQqX3k

Massively parallel Monte Carlo diffusion MR simulator written in Python.

The reference implementation for the Non Local Spatial and Angular Matching (NLSAM) denoising algorithm for diffusion MRI

This repository contains the code regarding our publication: Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling and evaluation in pancreatic cancer patients

Package facilitating the tracking of sub-cortical U-Fibers

Simulate realistic diffusion-weighted MR data

FOD-Net: A Deep Learning Method for Fiber Orientation Distribution Angular Super Resolution

Anomaly detection framework for Tractometry

Open-source eddy-current and head-motion correction for dMRI.

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

Quantitative MRI toolbox in Python

TractLearn is a Manifold Learning Toolbox for precision medicine. The first application is for Diffusion-Weighted MRI quantitative analysis.