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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