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    Area of Science:

    • Neuroimaging
    • Biophysics
    • Artificial Intelligence

    Background:

    • Diffusion MRI Neurite Exchange Imaging (dNEI) models brain microstructure.
    • Current dNEI protocols require lengthy scan times, limiting clinical and research applications.
    • Developing efficient dNEI acquisition schemes is crucial for broader adoption.

    Purpose of the Study:

    • To develop and validate a reduced acquisition scheme for dNEI on the Connectome 2.0 scanner.
    • To significantly shorten scan duration while maintaining accuracy of microstructural parameter estimation.
    • To compare the optimized protocol against full acquisition and alternative reduction strategies.

    Main Methods:

    • A data-driven framework utilizing explainable artificial intelligence (XAI) and recursive feature elimination was employed.
    • An optimal 8-feature subset was identified from a 15-feature protocol.
    • In vivo validation and benchmarking against full acquisition and heuristic/theory-driven methods were performed.

    Main Results:

    • The reduced 8-feature protocol produced parameter estimates and cortical maps comparable to the full 15-feature protocol.
    • Low estimation errors were observed in synthetic data, with minimal impact on test-retest variability.
    • The optimized protocol showed superior robustness, reducing water exchange time estimation deviations by over two-fold compared to other reduction methods.

    Conclusions:

    • A hybrid optimization framework enables efficient dNEI imaging in 14 minutes with preserved parameter fidelity.
    • This optimized protocol facilitates broader application of exchange-sensitive diffusion MRI in neuroscience and clinical research.
    • The developed method offers a generalizable approach for designing efficient biophysical parameter mapping acquisition protocols.