Abstract
Physics-driven deep learning (PD-DL) techniques have recently emerged as a powerful means for improved computational imaging, including in MRI applications. These methods use the physics information by incorporating the known forward model for data fidelity, while performing regularization using neural networks. There has been substantial progress in the training of PD-DL reconstruction methods, ranging from simple supervised learning to more practical self-supervised learning and generative models that allow training without reference data. Similarly, efforts have been made to characterize the errors associated with PD-DL methods via uncertainty quantification, mostly focusing on generative models. In this work, we devise an uncertainty estimation process that primarily focuses on the data fidelity component of PD-DL by characterizing the cyclic consistency between different forward models. Subsequently, we use this uncertainty estimate to guide the training of the PD-DL method. Results show that the proposed uncertainty-guided PD-DL strategy improves reconstruction quality.