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Abstract #3826

Spatial-Angular Representation Learning for High-Fidelity Super-Resolution in Diffusion MRI

Ruoyou Wu1,2,3, Jian Cheng4, Cheng Li1, Juan Zou5, Wenxin Fan1,3, Yong Liang2, and Shanshan Wang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, 2Pengcheng Laboratory, Shenzhen 518055, China, 3University of Chinese Academy of Sciences, Beijing 100049, China, 4School of Computer Science and Engineering, Beihang University, Beijing 100191, China, 5School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Due to scanning limitations and system noise, the spatial and angular resolution of diffusion magnetic resonance imaging (dMRI) data is typically low, which limits the accuracy of quantitative parameter estimation in regions with fine anatomical details.

Goal(s): Our goal is to enhance both the spatial and angular resolution of dMRI data simultaneously to improve the accuracy of quantitative parameter estimation.

Approach: Using implicit neural representations and spherical harmonics to simultaneously enhance both spatial and angular resolution, delivering precise diffusion direction information at a high spatial resolution.

Results: Experimental results on the publicly available HCP dataset validate the effectiveness of our method.

Impact: Simultaneously enhancing the spatial and angular resolution of dMRI data can effectively reduce acquisition time, improve the accuracy of quantitative parameter estimation, and enhance clinical diagnostic efficiency.

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Keywords