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Abstract #2595

Sensitivity analysis of self-supervised variational manifold learning based accelerated dynamic upper-airway collapse MRI

Md Shahin Ali1, Wahidul Alam1, Mathews Jacob2, and Sajan Goud Lingala1,3
1Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States, 2Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 3Department of Radiology, University of Iowa, Iowa City, IA, United States

Synopsis

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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Keywords