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Abstract #0333

Exploring SNR-Enhanced Reconstruction Techniques for Mesoscale Resolution BUDA-cEPI Diffusion MRI

Atita Suwannasak1, Congyu Liao2,3, Kawin Setsompop2,3, Itthi Chatnuntawech4, and Uten Yarach1
1Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand, 2Department of Radiology, Stanford University, Stanford, CA, United States, 3Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 4National Nanotechnology Center (NANOTEC), Pathum Thani, Thailand

Synopsis

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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Keywords