Deep-learning-based flow-artifact correction for multi-shot multiple overlapping-echo detachment imaging (msh-MOLED)

Motivation: Multi-shot overlapping-echo detachment imaging (msh-MOLED), a msh-EPI-based quantitative MR sequence, quantifies tissue T2 rapidly without the need of separately acquiring images with different TEs, but its results could be contaminated by flow-induced inter-shot phase variations. Goal(s): To implement an instantaneous referenceless flow-artifact correction for msh-MOLED. Approach: Flow-related features were added to the training data, and the trained network fulfilled T2 mapping free from flow artifacts without dear computational costs or additional reference data. Results: After correction, the Pearson’s correlation coefficient/mean absolute error was changed from 0.6332/6.5328 (uncorrected) to 0.8808/2.7623 (corrected). Impact: The proposed correction could be used to retain the mapping accuracy of msh-MOLED regardless of shot numbers, or to refine the reference data in high-spatial-resolution diffusion mapping potentially.