Estimating uncertainty in deep learning MRI reconstruction using a pixel classification image reconstruction framework

Data-driven deep learning (DL) image reconstruction from undersampled data has become a mainstream research area in MR image reconstruction. The generalization of the model on unseen data and out of sample data distribution is still a concern for the adoption of the DL reconstruction. In this work, we present a method of risk assessment in DL MR image reconstruction by generating an uncertainty map along with the reconstructed image. The proposed method re-casts image reconstruction as a classification problem and the probability of each voxel intensity in the reconstructed image can be used to efficiently estimate its uncertainty.