Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence
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.
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