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Abstract #3599

Uncertainty Quantification for Deep MRI

Vineet Edupuganti1, Morteza Mardani1, Joseph Cheng1, Shreyas Vasanawala2, and John Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Reliable MRI reconstruction is crucial for accurate diagnosis. However, high resolution imaging leaves substantial uncertainty about the authenticity of the recovered pixels especially when using overparameterized deep learning. Leveraging variational autoencoders (VAEs), this study proposes a Bayesian imaging algorithm that distills the uncertainty in a low-dimensional latent code. One can then simply draw independent samples from the decoder to procure pixel variance maps along with the image. To further quantify the prediction risk of unseen images, we adopt Stein's Unbiased Risk Estimator (SURE), which we find correlates well with the true risk.

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