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Contributed Talk and Q&A
Workshop: Workshop on Deep Learning and Inverse Problems

Risk Quantification in Deep MRI Reconstruction

Vineet Edupuganti


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.

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