Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data.

ABSTRACT: Advanced contemporary diffusion models for tissue microstructure often require diffusion MRI (DMRI) data with sufficiently dense sampling in the diffusion wavevector space for reliable model fitting, which might not always be feasible in practice. A potential remedy to this problem is by using deep learning techniques to predict high-quality diffusion microstructural indices from sparsely sampled data. However, existing methods are either agnostic to the data geometry in the diffusion wavevector space ( q -space) or limited to leveraging information from only local neighborhoods in the physical coordinate space ( x -space). Here, we propose a hybrid graph transformer (HGT) to explicitly consider the q -space geometric structure with a graph neural network (GNN) and make full use of spatial

SUBMITTER: Chen G 

PROVIDER: S-EPMC10141974 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature