  Reliable MRI is crucial for accurate interpretation in therapeutic and
diagnostic tasks. However, undersampling during MRI acquisition as well as the
overparameterized and non-transparent nature of deep learning (DL) leaves
substantial uncertainty about the accuracy of DL reconstruction. With this in
mind, this study aims to quantify the uncertainty in image recovery with DL
models. To this end, we first leverage variational autoencoders (VAEs) to
develop a probabilistic reconstruction scheme that maps out (low-quality) short
scans with aliasing artifacts to the diagnostic-quality ones. The VAE encodes
the acquisition uncertainty in a latent code and naturally offers a posterior
of the image from which one can generate pixel variance maps using Monte-Carlo
sampling. Accurately predicting risk requires knowledge of the bias as well,
for which we leverage Stein's Unbiased Risk Estimator (SURE) as a proxy for
mean-squared-error (MSE). Extensive empirical experiments are performed for
Knee MRI reconstruction under different training losses (adversarial and
pixel-wise) and unrolled recurrent network architectures. Our key observations
indicate that: 1) adversarial losses introduce more uncertainty; and 2)
recurrent unrolled nets reduce the prediction uncertainty and risk.
