  Deep learning (DL) models are capable of successfully exploiting latent
representations in MR data and have become state-of-the-art for accelerated MRI
reconstruction. However, undersampling the measurements in k-space as well as
the over- or under-parameterized and non-transparent nature of DL make these
models exposed to uncertainty. Consequently, uncertainty estimation has become
a major issue in DL MRI reconstruction. To estimate uncertainty, Monte Carlo
(MC) inference techniques have become a common practice where multiple
reconstructions are utilized to compute the variance in reconstruction as a
measurement of uncertainty. However, these methods demand high computational
costs as they require multiple inferences through the DL model. To this end, we
introduce a method to estimate uncertainty during MRI reconstruction using a
pixel classification framework. The proposed method, PixCUE (stands for Pixel
Classification Uncertainty Estimation) produces the reconstructed image along
with an uncertainty map during a single forward pass through the DL model. We
demonstrate that this approach generates uncertainty maps that highly correlate
with the reconstruction errors with respect to various MR imaging sequences and
under numerous adversarial conditions. We also show that the estimated
uncertainties are correlated to that of the conventional MC method. We further
provide an empirical relationship between the uncertainty estimations using
PixCUE and well-established reconstruction metrics such as NMSE, PSNR, and
SSIM. We conclude that PixCUE is capable of reliably estimating the uncertainty
in MRI reconstruction with a minimum additional computational cost.
