Keywords: Tractography, Tractography & Fibre Modelling
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords