
microTorch: A Software Package for Fast and Flexible Self-Supervised Diffusion MRI Model Fitting
Abstract
The microTorch software package allows for the fast and flexible fitting of a wide range of multi-compartment microstructure models to diffusion magnetic resonance imaging (dMRI) data. It employs a self-supervised learning approach, which enables the speed gains of deep learning whilst minimising estimation bias induced by the training data distribution. Its modular architecture enables users to readily evaluate different combinations of compartment models, apply established multi-compartment models, and interchange neural network architectures without modifying the underlying framework. The package supports custom acquisition schemes and provides configurable training settings, allowing it to be tailored to specific datasets. microTorch is built using PyTorch and is freely available at https://github.com/snigdha-sen/microtorch.
© 2026 Snigdha Sen, Rajib Ahmed, Gerrit C. Arends, Nicola Casali, Alvaro Planchuelo Gomez, Xiaoxiang Chen, Marta Masramon, Christopher S. Parker, Marco Palombo, Chantal M. W. Tax, Eleftheria Panagiotaki, Paddy J. Slator, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.