Keywords: Image Reconstruction, Diffusion Reconstruction, Joint-neighbourhood reconstruction, Deep learning reconstruction, Self-supervised learning
Motivation: Diffusion MRI (dMRI) at mesoscale-resolution is hindered low SNR. To reconstruct high SNR images from high-noise levels and improve reconstruction schemes for clinical and research practice, we explore different advanced techniques to pinpoint optimal strategies.
Goal(s): To investigate enhancing SNR reconstructions for mesoscale-resolution BUDA circular-EPI (BUDA-cEPI) dMRI.
Approach: We employed BUDA-S-LORAKS, joint nearest-neighbors (JNN) diffusion directions with BUDA-S-LORAKS, and zero-shot self-supervised (ZS-SS) unrolled deep-learning network to reconstruct multiple directions from BUDA-cEPI at 500µm-resolution.
Results: BUDA-cEPI reconstructions indicate that JNN BUDA-S-LORAKS and ZS-SS unrolled network with 1-NEX data achieve SNR levels comparable to standard method using 3-NEX data.
Impact: Our exploration may provide the advancement of superb and appropriate dMRI reconstruction for high-fidelity dMRI at mesoscale resolution on clinical scanners by addressing SNR challenges and preserving fine anatomical details critical for accurate diagnosis and analysis.
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