Radial DCE-MRI is robust to motion. However, bulk motion or heavy breathing causes 1) irrecoverably deteriorated k-space lines acquired during motion events reducing image quality, 2) misaligned volumes in a dynamic sequence. In this work we propose to solve the first problem by fitting a Gaussian process to the k-space center of each spoke over time and using it to determine outlier spokes corrupted by motion. We solve the second problem by clustering the dynamic data to respective motionless phases before and after each motion event and registering volumes between phases for computationally efficient correction of motion with fewer registrations.
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