Reduced Noise and Motion Artifacts for MUSE Reconstruction using Deep Learning-based Phase Correction

Motivation: Filter-based phase estimation requires tuning and is subject to the tradeoff between signal bias and vulnerability against phase inhomogeneity. DL-based phase correction has been shown to effectively remove both high- and low-frequency phase while minimizing signal bias. Goal(s): To evaluate a DL-based phase correction method that improves the robustness of motion-induced phase estimation and its impact on noise and motion artifacts in MUSE reconstruction. Approach: Volunteer brain and abdomen data were acquired with a MUSE sequence and reconstruction was performed offline. Results: Compared to filter-based phase estimation, DL-based phase correction results in reduced noise and motion artifacts in MUSE reconstructed images. Impact: MUSE enables high resolution DWI over a large FOV with reduced geometric distortion, but is very sensitive to shot-to-shot differences in motion-induced phase. DL-based phase correction can improve robustness in MUSE reconstruction, especially in anatomical regions with significant motion.