Keywords: Image Reconstruction, AI/ML Image Reconstruction
Motivation: This study aims to quantitatively validate a physics-guided stopping criterion for variational manifold learning applied to upper-airway sleep MRI.
Goal(s): We retrospectively analyze reconstruction sensitivity using a reference dynamic dataset and varied regularization constraints.
Approach: Given a target time resolution, healthy volunteer performing the Muller maneuver was imaged with 2D gradient-echo-based variable-density spirals, generating a reference image from a mild total-variation-regularized sparse-SENSE reconstruction. Retrospective undersampling was then applied to assess reconstruction sensitivity to various regularizers quantitatively.
Results: Proposed criterion effectively stabilizes adaptive learning by guiding it with validation loss, establishing a robust quantitative basis for stopping criteria in dynamic MRI reconstruction.
Impact: Enables robust sleep MRI methods for patient specific imaging diagnostics and therapeutic planning.
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