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

Med Image Comput Comput Assist Interv. 2022 Sep;13431:113-122. doi: 10.1007/978-3-031-16431-6_11. Epub 2022 Sep 15.ABSTRACTAdvanced 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 information with a novel residual dense transformer (RDT). The RDT consists of multiple densely connected transformer layers and a residual connection to facilitate model training. Extensive experiments on the data from the Human Connectome Project (HCP) demonstrate that our method significantly improves the quality of microstructural estimations over existing state-of-the-art methods.PMID:37126477 | PMC:PMC10141974 | DOI:10.1007/978-3-031-16431-6_11

Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 1, 2023 Category: Radiology Authors: Geng Chen Haotian Jiang Jiannan Liu Jiquan Ma Hui Cui Yong Xia Pew-Thian Yap Source Type: research