Learning non-rigid registration in k-space from highly-accelerated cardiac and respiratory MR data

Motivation: Time-resolved motion estimation from accelerated MR data enables high-quality imaging, intra-modality motion correction and real-time tracking during MR-guided radiotherapy. Conventionally, image registration is solved in the image domain and, therefore, remains susceptible to aliasing artifacts for highly-accelerated acquisitions. Goal(s): We aim to propose a robust non-rigid image registration framework from highly-accelerated data without additional information. Approach: We introduce a novel Local-All-Pass Attention Network (LAPANet) that performs accurate motion estimation directly from the acquired k-space. Results: LAPANet provides reliable estimates for fully-sampled and undersampled data, up to 104-fold for cardiac motion and 148-fold for respiratory motion, and outperforms established image-based registrations in different trajectories. Impact: Our framework can reliably estimate non-rigid motion from highly-accelerated data without a-priori information. This enables faster acquisition through integration into motion-compensated reconstructions, intra-modality motion correction for other imaging methods and real-time motion characterization and tracking for guided radiotherapy and interventions.