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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