  The application of compressed sensing (CS)-enabled data reconstruction for
accelerating magnetic resonance imaging (MRI) remains a challenging problem.
This is due to the fact that the information lost in k-space from the
acceleration mask makes it difficult to reconstruct an image similar to the
quality of a fully sampled image. Multiple deep learning-based structures have
been proposed for MRI reconstruction using CS, both in the k-space and image
domains as well as using unrolled optimization methods. However, the drawback
of these structures is that they are not fully utilizing the information from
both domains (k-space and image). Herein, we propose a deep learning-based
attention hybrid variational network that performs learning in both the k-space
and image domain. We evaluate our method on a well-known open-source MRI
dataset and a clinical MRI dataset of patients diagnosed with strokes from our
institution to demonstrate the performance of our network. In addition to
quantitative evaluation, we undertook a blinded comparison of image quality
across networks performed by a subspecialty trained radiologist. Overall, we
demonstrate that our network achieves a superior performance among others under
multiple reconstruction tasks.
