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Abstract #2452

A unified framework for estimating diffusion kurtosis tensors with multiple prior information

Li Guo1,2,3, Lyu Jian2,3, Yingjie Mei4, Mingyong Gao1, Yanqiu Feng2,3, and Xinyuan Zhang2,3,5
1Department of MRI, The First People’s Hospital of Foshan (Affiliated Foshan Hospital of Sun Yat-sen University), Foshan, China, 2School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 3Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, 4Philips Healthcare, Guangzhou, China, 5Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China

Accurate tensor estimation for DKI is usually challenged by noise. The noncentral Chi distribution noise would introduce bias in the estimated DKI tensors. Although several noise-corrected models are statistically unbiased, the DKI tensors generated by these estimators have large variances. In addition, severe noise easily causes the estimated kurtosis values outside a physically acceptable range. The goal of this work is to propose a unified framework that integrates multiple prior information including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of DKI model, and noise characteristic of magnitude diffusion images for improved DKI tensor estimation.

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