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Abstract #3219

Tractography from T1-Weighted MRI Using Segmentation-Guided and Patch-Based Diffusion Model

Dian Sheng1, Junyi Wang1, Guoqian Xie2, Lauren J O’Donnell3, Le Zhang1, and Fan Zhang1
1University of Electronic Science and Technology of China, Chengdu, China, 2Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China, 3Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation: Generating tractography from diffusion MRI (dMRI) is valuable for studying brain connectivity, but is limited by its accessibility. Many existing neuroimaging datasets only include T1-weighted MRI, motivating the need for a dMRI-free approach.

Goal(s): This study aims to develop an efficient, accurate method to generate tractography directly from T1-weighted MRI, enabling broader applications in both clinical and research settings.

Approach: We designed a segmentation-guided, patch-based framework that focuses on anatomically relevant regions, facilitating high-fidelity Tract Orientation Map (TOM) generation.

Results: Our framework achieves structural accuracy comparable to dMRI-based methods, as demonstrated by quantitative and qualitative evaluations.

Impact: This study introduces a novel approach for generating tractography directly from T1-weighted MRI rapidly and accurately. This can be a useful tool to facilitate large-scale brain connectivity studies without the resource constraints associated with dMRI.

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Keywords