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Abstract #4403

Self-validation for deep learning–based diffusion kurtosis imaging harmonization

Qiqi Tong1, Ting Gong1, Hongjian He1, Yi-Cheng Hsu2, and Jianhui Zhong1,3
1Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, 2MR Collaboration, Siemens Healthcare, Shanghai, China, 3Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Deep learning–based harmonization for diffusion imaging data with high efficiency and low cost is gaining popularity. However, the performance of the training-required network depends on the training data, which lack the diversity of the large sets of data in more substantial multicenter projects. We proposed a leave-one-tissue-out training strategy to evaluate the validity and reliability across scanners of a deep learning–based diffusion kurtosis imaging harmonization method. The results confirm that the deep learning–based network can still reconstruct the untrained tissue with validity, although the reliability would be higher when the tissue is trained.

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