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    Area of Science:

    • Neuroimaging
    • Biophysics
    • Medical Physics

    Background:

    • Diffusion kurtosis imaging (DKI) is crucial for neuroscience and clinical applications.
    • Noise significantly impacts the reliable estimation of DKI tensors, particularly the kurtosis tensor (KT).

    Purpose of the Study:

    • To develop a joint denoising and estimation framework for improved DKI tensor estimation.
    • To integrate multiple prior information sources to enhance DKI tensor accuracy.

    Main Methods:

    • Proposed a framework integrating nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR), and noise characteristics.
    • Employed the first-moment noise-corrected fitting model (M1NCM) to address noise bias.
    • Validated the method (M1NCM-NSS-LSS-PR) against existing estimators and state-of-the-art techniques.

    Main Results:

    • The proposed M1NCM-NSS-LSS-PR method demonstrated superior performance in simulations and in-vivo dMRI datasets.
    • The framework effectively reduced noise fluctuations in DKI tensors, especially KT.
    • The method showed robustness to varying numbers of diffusion directions in in-vivo experiments.

    Conclusions:

    • The integrated denoising and estimation framework significantly improves DKI tensor estimation accuracy.
    • The proposed method offers a robust solution for noise reduction in diffusion MRI.
    • This advancement has implications for more reliable neuroscientific and clinical applications of DKI.