Self-supervised variational manifold learning: application to dynamic MRI of airway collapse in obstructive sleep apnea.

Motivation: Recent manifold-based models use unsupervised generative Variational smoothness regularization on manifold framework for improved recovery. This unsupervised method lacks a well-defined automated early stopping criterion and rely on subjective qualitative assessment only. Goal(s): We aim to adapt a physics-guided early stopping criterion to V-SToRM framework leveraging non-cartesian multi-slice acquisition. Approach: We developed a self-supervised variational manifold recovery method where we modified the original variational manifold scheme to integrate an early stopping criterion. Results: With early-stopping criterion enforced, we observe a faithful reconstruction of spatiotemporal dynamics at epoch 45 and images without blocky/noise amplification artifacts at different temporal phases with suppressed temporal blurring artifacts. Impact: While preserving the integrity of the ongoing joint learning of latent variables and generator weights, the adoption of early-stopping strategy in this context streamlines the computational complexity and consequently, rendering faithful and reproducible faster reconstructions.