  There has been much recent interest in adapting undersampled trajectories in
MRI based on training data. In this work, we propose a novel patient-adaptive
MRI sampling algorithm based on grouping scans within a training set.
Scan-adaptive sampling patterns are optimized together with an image
reconstruction network for the training scans. The training optimization
alternates between determining the best sampling pattern for each scan (based
on a greedy search or iterative coordinate descent (ICD)) and training a
reconstructor across the dataset. The eventual scan-adaptive sampling patterns
on the training set are used as labels to predict sampling design using nearest
neighbor search at test time. The proposed algorithm is applied to the fastMRI
knee multicoil dataset and demonstrates improved performance over several
baselines.
