Deep‐Reorder: Refined Motion Correction for 3D Brain MRI via Iterative Data Regrouping

PURPOSE: Accurate and temporally high-resolution estimation of subject motion is paramount for effective motion-correction in MRI. This work proposes a novel approach for estimating subject motion within a 3D MRI scan with high temporal resolution. METHODS: Starting from an incoherent sampling scheme, images are reconstructed from temporally ordered k-space fractions via deep learning, enabling motion estimation via registration between each image. Assuming the bulk of the motion is removed using these initial estimates, the remaining misalignment between k-space fractions is expected to be minimally correlated in time. Based on this insight, an iterative strategy is proposed in which shots are arbitrarily regrouped into new k-space fractions, regardless of their original temporal ordering, for subsequent motion estimation and correction. This effectively increases the temporal resolution of the approach, enabling individual correction of all shots. RESULTS: When applied to 3D T1w TFE MRI of the brain, the proposed approach "Deep-Reorder" estimated a unique motion state for all shots, producing significantly better estimations of retrospective motion compared to estimation without iterative data regrouping. On prospectively motion-corrupted scans with intentional head movement by volunteers, the proposed approach consistently improved image quality, producing less noisy images with reduced motion artifacts. CONCLUSION: In conclusion, the proposed approach, which is free of external tracking devices or navigators, effectively estimated 3D motion and corrected motion artifacts with increased temporal resolution.