Abstract #0801

Robust diffusion parametric mapping of motion-corrupted data using a deep-learning-based method

Ting Gong¹, Hongjian He¹, Zhiwei Li², Qiqi Tong¹, Zhichao Lin², Yi Sun³, Feng Yu², and Jianhui Zhong^1,4

¹Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China, ²Department of Instrument Science & Technology, Zhejiang University, Hangzhou, China, ³MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China, ⁴Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Motion occurring during the acquisition of diffusion-weighted image volumes is inevitable. Deficient accuracy of volumetric realignment and within-volume movements cause the quality of diffusion model reconstruction to deteriorate, particularly for uncooperative subjects. Taking advantage of the strong inference ability of neural networks, we reconstructed diffusion parametric maps with remaining volumes after the motion-corrupted data removed. Compared to conventional model fitting, our method is minimally sensitive to motion effects and generates results comparable to the gold standard, with as few as eight volumes retained from the motion-contaminated data. This method shows great potential in exploiting some valuable but motion-corrupted DWI data.

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