Uncertainty Estimation for Deep Learning-Based Enhancement of Undersampled Dual-Echo Steady-State Knee MRI

Deep learning (DL) based image enhancement of undersampled 3D dual-echo steady-state knee MRI can achieve faster computation times compared to compressed sensing reconstruction. However, it is hard to interpret how DL models work. This introduces the risk of DL-enhanced images containing inaccuracies without the user’s knowledge and thus confounding diagnosis. This work aimed to calculate pixel-wise uncertainty maps for DL-enhanced images by incorporating Monte Carlo Dropout into a 2D UNET to estimate epistemic uncertainty. Analysis showed that the DL-enhanced images achieved good image quality and the spatial uncertainty maps reflected errors, compared to reference images.