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Abstract #2785

Learned K-Space Partitioning for Improved Dual-Domain Self-Supervised Image Reconstruction

Brenden Kadota1,2, Charles Millard3, and Mark Chiew1,2
1Sunnybrook Research Institute, Toronto, ON, Canada, 2Medical Biophysics, University of Toronto, Toronto, ON, Canada, 3Wellcome Centre for Integrative Neuroimaging, Oxford, United Kingdom

Synopsis

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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Keywords