  The core problem of Magnetic Resonance Imaging (MRI) is the trade off between
acceleration and image quality. Image reconstruction and super-resolution are
two crucial techniques in Magnetic Resonance Imaging (MRI). Current methods are
designed to perform these tasks separately, ignoring the correlations between
them. In this work, we propose an end-to-end task transformer network
(T$^2$Net) for joint MRI reconstruction and super-resolution, which allows
representations and feature transmission to be shared between multiple task to
achieve higher-quality, super-resolved and motion-artifacts-free images from
highly undersampled and degenerated MRI data. Our framework combines both
reconstruction and super-resolution, divided into two sub-branches, whose
features are expressed as queries and keys. Specifically, we encourage joint
feature learning between the two tasks, thereby transferring accurate task
information. We first use two separate CNN branches to extract task-specific
features. Then, a task transformer module is designed to embed and synthesize
the relevance between the two tasks. Experimental results show that our
multi-task model significantly outperforms advanced sequential methods, both
quantitatively and qualitatively.
