Abstract #1141

AID-DTI: fast and high-fidelity diffusion tensor imaging with detail-preserving model-based deep learning

Wenxin Fan^1,2, Cheng Li¹, Jing Yang^3,4, Juan Zou^5,6, Hairong Zheng¹, and Shanshan Wang^1,7,8

¹Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²University of Chinese Academy of Science, Beijing, China, ³Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁴University of Chinese Academy of Science, Beijing, China,, Beijing, China, ⁵School of Physics and Optoelectronics, Xiangtan University, Xiangtan, China, ⁶Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁷Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China, ⁸Peng Cheng Laboratory, Shenzhen, China

Synopsis

Keywords: DWI/DTI/DKI, Diffusion Tensor Imaging, Deep Learning

Motivation: Existing methods tend to suffer from Rician noise, leading to detail loss during the reconstruction of DTI-derived parametric maps. This issue becomes particularly pronounced when sparsely sampled q-space data are used.

Goal(s): Our goal was to facilitate fast and high-fidelity estimation of DTI metrics.

Approach: We propose a novel SVD-based regularizer, which can effectively preserve fine details while suppressing noise during network training.

Results: Experimental results consistently demonstrate that the proposed method estimates DTI parameter maps with finer details, outperforming current state-of-the-art methods.

Impact: The proposed method may facilitate fast and high-fidelity DTI with a newly designed SVD-based regularizer, and it has a potential to become a practical tool in clinical and neuroscientific applications.

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