Risk Quantification in Deep MRI Reconstruction

Reliable medical image recovery is crucial for accurate patient diagnoses, but little prior work has centered on quantifying uncertainty when using non-transparent deep learning approaches to reconstruct high-quality images from limited measured data. In this study, we develop methods to address these concerns, utilizing a VAE as a probabilistic recovery algorithm for pediatric knee MR imaging. Through our use of SURE, which examines the end-to-end network Jacobian, we demonstrate a new and rigorous metric for assessing risk in medical image recovery that applies universally across model architectures.