Attention Hybrid Variational Net for Accelerated MRI Reconstruction
Guoyao Shen Boran Hao Mengyu Li Chad W. Farris Ioannis Ch. Paschalidis Stephan W. Anderson Xin Zhang
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.