Robust Compressed Sensing MRI with Deep Generative Priors

Compressed sensing [23, 15] has enabled reductions to the number of measurements needed for successful reconstruction in a variety of imaging inverse problems. In particular, it has led to shorter scan times for magnetic resonance imaging (MRI) [58, 86], and most MRI vendors have released products leveraging this framework to accelerate clinical workflows. Despite their successes, sparsity-based methods are limited by the achievable acceleration rates, as the sparsity assumptions are either hand-crafted or are limited to simple learned sparse codes [68, 69]. More recently, deep learning techniques have been used as powerful data-driven reconstruction methods for inverse problems [47, 64]. There are two broad families of deep learning inversion techniques [64]: end-to-end supervised and distribution-learning approaches. End-to-end supervised techniques use a training set of measured images and deploy convolutional neural networks (CNNs) and other architectures to learn the inverse mapping from measurements to image. Network architectures that include both CNN blocks and the imaging forward model have grown in popularity, as they combine deep learning with the compressed sensing optimization framework, see e.g.