Fidelity imposed network edit (FINE) for solving illposed image reconstruction Abstract   Deep learning (DL) is increasingly used to solve illposed inverse problems in medical imaging, such as reconstruction from noisy andor incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DLbased methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DLbased image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pretrained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and undersampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.

Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks Image reconstruction from undersampled kspace data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deeplearningbased image reconstruction methods for MRI. Two types of deeplearningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.