# Hacking axisymmetric DKI into DIPY

CFMM project for western brainhack 2026.

## Description:
Diffusion Weighted MRI (dMRI) is a cool technology that has improved our understanding of brain microstructure and disease. In research, there are multiple approaches to model microstructure from dMRI data. For example, Diffusion Kurtosis Imaging (DKI) calculates quantitative metrics that potentially explain the brain's complex microstructural configuration. Here at Western, members of the CFMM have developed improvements for DKI making it more robust to noise while also reducing their acquisition time in the MRI scanner. However, these implementations were done on a closed platform (Matlab). This project aims to disseminate open science practices and research done here at Western, integrating these developments into the open source DIPY ecosystem (Python). We hope to give back powerful tools to the neuroscience community to tackle complex questions with dMRI.

**Skills for project:** Familiarity with matlab, python, git, dMRI... But not really, everyone is welcome. If you are interested in any of these topics you can join. Skill level doesn't matter.

## Additional details

### Relevant paper
Jake Hamilton, Kathy Xu, Nicole Geremia, Vania F. Prado, Marco A.M. Prado, Arthur Brown, Corey A. Baron; Robust frequency-dependent diffusional kurtosis computation using an efficient direction scheme, axisymmetric modelling, and spatial regularization. Imaging Neuroscience 2024; 2 imag–2–00055. doi: https://doi.org/10.1162/imag_a_00055

### Relevant links:

- matMRI (matlab package where hte method was originally implemented in function nii2kurt.m): https://gitlab.com/cfmm/matlab/matmri

- Dipy. Python ecosystem for models fitting and general proccessing of dMRI data. https://dipy.org/

- Dipy devolpers documentation https://docs.dipy.org/stable/devel/index.html#development