NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data

Deep learning (DL) models are currently employed to reconstruct high quality MRI image from undersampled k-space measurements. Yet, uncertainty quantification in images reconstructed by such models is critical for reliable clinical decision making. We propose NPBREC, a non-parametric Bayesian approach for uncertainty estimation in DL-based MRI reconstruction. We demonstrated the added-value of our Bayesian registration framework on the fastmri multi-coil brain MRI dataset, compared to the baseline E2E-VarNet trained with and without inference-time dropout for uncertainty quantification. Our NPBREC approach demonstrated both improved reconstruction accuracy and a better correlation between reconstruction errors and uncertainty measures.