Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: K-space partitioning schemes for self-supervised learning via data undersampling (SSDU) remain unoptimized, with current partitioning typically performed heuristically based on the original sampling distribution.
Goal(s): To improve self-supervised image reconstruction by learning optimal k-space partitioning.
Approach: We modify SSDU by using a dual domain loss and learning a secondary sampling probability distribution to partition k-space into two sets: a data consistency and loss.
Results: Dual domain and learned partitioning produces higher quality images than k-space only self-supervised learning.
Impact: Learned k-space partitioning enhances reconstruction quality better utilizing acquired data for reconstructions. Learned k-space partitioning provides a framework for optimizing self-supervised partitioning to diverse k-space trajectories which previously were hand-picked or sub-optimal.
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