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Abstract #4346

High-quality Reconstruction of Volumetric Diffusion Kurtosis Metrics via Residual Learning Network and Perceptual Loss

Min Feng1, Qiqi Tong2, Yingying Li1, Bo Dong1, JIanhui Zhong1,3, and Hongjian He1
1Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, HANGZHOU, China, 2Research Center for Healthcare Data Science, Zhejiang Lab, HANGZHOU, China, 3Department of Imaging Sciences, University of Rochester, Rochester, NY, United States


This study proposed a novel 3D residual network to learn end-to-end reconstruction from as few as eight DWIs to volumetric DKI parameters. The weighted loss function combining perceptual loss is utilized, which helps the network capture in-depth feature of DKI parameters. The results show that our method achieves superior performance over state-of-the-art methods for providing accurate DKI parameters as well as preserves rich textural details and improves the visual quality of reconstructions.

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