ATM: Anatomy to Tract Mapping

Motivation: Conventional diffusion tractography relies on error-prone voxel-to-voxel tracing and typically demands diffusion MRI with high signal-to-noise ratio, spatial and angular resolution, which can be challenging to acquire. Goal(s): To generate bundle-specific streamlines from anatomical MRI. Approach: We present a deep learning framework for anatomy to tract mapping (ATM), allowing bundle-specific streamlines to be generated from anatomical MRI. ATM generates streamlines without resorting to voxel-to-voxel tracing, hence sidesteps challenges involved in tracing across complex configurations such as crossings, kissing, and bending and the bottlenecks where multiple bundles converge toward before re-emerging. Results: ATM effectively captures bundle shapes and generates bundle-specific streamlines from T1-weighted MRI. Impact: We demonstrate that tract streamlines can be estimated directly from anatomical MRI. This allows (1) tractography in the absence of diffusion MRI and (2) anatomy tractography to guide diffusion tractography.