Keywords: New Signal Preparation Schemes, Data Processing
Motivation: Multiple Echo Recombined Gradient Echo (MERGE) images are inherently complex-valued, and motion, field inhomogeneities, etc. could cause echo-to-echo background phase variations. Filter-based phase correction often results in signal cancellation.
Goal(s): To remove echo-to-echo phase variations for complex echo combination and improve the in-plane resolution and SNR of complex combined image
Approach: We used a deep-learning-based phase correction to improve complex echo combination and apply AIR Recon DL to further improve the in-plane resolution and SNR
Results: Deep learning based phase correction minimized signal cancellation and enabled robust complex echo combination With AIR Recon DL, MERGE images showed improved resolution and SNR.
Impact: With improved image quality, it could improve the visualization, segmentation and measurement of tissue of interest, improving diagnosis, treatment response monitoring, etc.
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