Combining Prospective and Retrospective Motion Correction, Using the Scout Accelerated Motion Estimation and Reduction ( <scp>SAMER</scp> ) Framework, for Rapid and Motion‐Robust <scp>2D TSE</scp> Imaging

ABSTRACT Purpose Motion artifacts remain a major challenge in applying multi‐shot 2D imaging to motion prone patient populations. Through‐plane motion is especially problematic, where the lack of encoding cannot be easily recovered, even using deep‐learning (DL)‐regularized reconstruction. We demonstrate the benefits of combining prospective and retrospective motion correction, where the Scout Accelerated Motion Estimation and Reduction (SAMER) technique is utilized for on‐the‐fly motion estimation with field‐of‐view (FoV) updates along with retrospective correction of potential residual motion. Methods Four prospective motion correction (pMoCo) strategies were implemented within a custom 2D turbo‐spin‐echo (TSE) SAMER enabled sequence. They were evaluated in vivo across representative subject motion, with associated simulations to characterize artifacts and the correction performance. In addition, motion trajectories measured during inpatient clinical exams were used to further demonstrate the robustness of the combined motion correction approach. Results Prospectively applying FoV updates significantly improved the image quality of SAMER reconstructions. Simulated artifact patterns were shown to closely match those observed in vivo, and across 274 simulations using clinical motion trajectories, the combined approach reduced NRMSE in 90% of moderate‐to‐severe motion cases and significantly decreased the overall reconstruction error. Conclusion Utilizing on‐the‐fly SAMER motion estimates, a combined prospective and retrospective motion correction approach was demonstrated for 2D TSE imaging. The proposed method improved image quality in several representative in vivo motion experiments and across simulations of a wide range of clinical inpatient motion conditions. In addition, simulated artifact patterns were shown to closely match those observed in vivo. This capability should enable on‐the‐fly prediction of motion artifacts for efficient/intelligent acquisition strategies for the most challenging motion scenarios.