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Self-Navigated 3D Diffusion MRI Using an Optimized CAIPI Sampling and Structured Low-Rank Reconstruction Estimated

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    This study introduces a self-navigated method for 3D multi-slab diffusion MRI, improving scan efficiency. The technique corrects phase variations without extra navigators, reducing scan time and enhancing signal-to-noise ratio (SNR) efficiency.

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    Area of Science:

    • Magnetic Resonance Imaging
    • Diffusion MRI
    • Medical Imaging

    Background:

    • 3D multi-slab diffusion MRI offers high SNR efficiency but faces challenges with shot-to-shot phase variations due to motion in multi-shot acquisitions.
    • Conventional navigator acquisitions correct these phase variations but increase scan time and specific absorption rate (SAR).

    Purpose of the Study:

    • To develop an efficient, self-navigated method for correcting phase variations in 3D multi-slab diffusion MRI, eliminating the need for explicit navigator acquisitions.
    • To optimize multi-shot sampling for self-navigation while maintaining reconstruction quality.

    Main Methods:

    • Designed shot sampling to intersect the central kz=0 plane for self-navigation.
    • Utilized kz=0 intersections from all shots for a structured low-rank constrained reconstruction of 2D phase maps.
    • Applied reconstructed phase maps to correct shot-to-shot phase inconsistencies in the final 3D reconstruction.

    Main Results:

    • Achieved comparable image quality to conventional navigated 3D multi-slab imaging.
    • Reduced scan time by 31.7% and improved SNR efficiency by 15.5%.
    • Demonstrated comparable quality for Diffusion Tensor Imaging (DTI) and white matter tractography.

    Conclusions:

    • The proposed self-navigated method effectively corrects phase variations in 3D multi-slab diffusion MRI.
    • This approach significantly enhances scan efficiency and SNR performance compared to conventional navigated methods.
    • It offers a promising alternative for high-quality diffusion MRI with reduced acquisition time.