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 Zhong^1,3, 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

Synopsis

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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