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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