# Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers

arXiv: https://arxiv.org/abs/2609.39184 · HTML: https://mlsanity.com/paper/2609.39184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [End-to-end people detection in crowded scenes](https://mlsanity.com/paper/1506.04878/) (2015)
- [Model-Informed Machine Learning for Multi-component T2 Relaxometry](https://mlsanity.com/paper/2007.10225/) (2020)
- [Magic DIAMOND: Multi-Fascicle Diffusion Compartment Imaging with Tensor Distribution Modeling and Tensor-Valued Diffusion Encoding](https://mlsanity.com/paper/2004.07340/) (2020)
- [Multi-compartment diffusion-relaxation MR signal representation in the spherical 3D-SHORE basis](https://mlsanity.com/paper/2501.15689/) (2025)

## Abstract (arXiv)

Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods either resolve fiber orientations without quantifying microstructure, or quantify microstructure while assuming a fixed number of compartments and a single fiber direction. Nonparametric approaches that recover both require tensor-valued diffusion encoding and computationally expensive Monte-Carlo inversion of an ill-posed inverse Laplace transform. We propose to reframe this problem as an object detection-like task, adopting the Detection Transformer (DETR) architecture to jointly predict mean diffusivity (MD), fractional anisotropy (FA), main fiber direction, and signal fraction for a variable number of compartments per voxel from standard multi-shell diffusion MRI with linear encoding. Hungarian matching during training resolves permutation invariance across compartments. We introduce mean Average Precision as a reproducible benchmark metric. Evaluated on synthetic test data with up to five compartments per voxel, our model achieves R2=0.95 for MD, R2=0.88 for FA, and a median angular error of 4.2deg, with performance scaling naturally with compartmental signal fraction.