  The goal of MRI reconstruction is to restore a high fidelity image from
partially observed measurements. This partial view naturally induces
reconstruction uncertainty that can only be reduced by acquiring additional
measurements. In this paper, we present a novel method for MRI reconstruction
that, at inference time, dynamically selects the measurements to take and
iteratively refines the prediction in order to best reduce the reconstruction
error and, thus, its uncertainty. We validate our method on a large scale knee
MRI dataset, as well as on ImageNet. Results show that (1) our system
successfully outperforms active acquisition baselines; (2) our uncertainty
estimates correlate with error maps; and (3) our ResNet-based architecture
surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The
proposed method not only shows high-quality reconstructions but also paves the
road towards more applicable solutions for accelerating MRI.
