# Anatomy-to-tract mapping infers white matter pathways without diffusion streamline propagation

## TL;DR

This paper introduces a new method to map white matter pathways using anatomical MRI, avoiding the need for diffusion MRI data.

## Contribution

The novel contribution is a deep learning framework called ATM that generates white matter streamlines directly from T1-weighted MRI.

## Key findings

- ATM outperforms diffusion-based methods in reconstructing white matter bundles.
- The method handles complex fiber configurations like crossings and bends effectively.

## Abstract

Diffusion tractography, a cornerstone of white matter mapping, relies on point-to-point streamline propagation, a process often limited by the signal-to-noise ratio and spatioangular resolution of diffusion MRI (dMRI). Here, we present Anatomy-to-Tract Mapping (ATM), a framework that generates bundle-specific streamlines directly from T1-weighted MRI without requiring orientation field estimation, voxelwise segmentation, or streamline propagation. ATM leverages the high quality and minimal distortion of anatomical MRI and learns from paired T1w and tractogram data to synthesize anatomically plausible, subject-specific streamlines. This anatomy-driven approach addresses complex configurations such as crossing, kissing, and bending fibers, providing robust bundle reconstructions. Using the TractoInferno dataset with 30 white matter bundles, we evaluate ATM against diffusion-based methods, including MRtrix probabilistic tracking with BundleSeg and SCIL atlas warping. ATM demonstrates strong performance across multiple metrics, including bundle similarity, volume coverage, angular correlation, and geometric fidelity.

This work presents a deep learning model that predicts white matter tracts from anatomical MRI without diffusion data. It enables faster, accessible mapping of white-matter connections for research and clinical use.