  Decreasing magnetic resonance (MR) image acquisition times can potentially
reduce procedural cost and make MR examinations more accessible. Compressed
sensing (CS)-based image reconstruction methods, for example, decrease MR
acquisition time by reconstructing high-quality images from data that were
originally sampled at rates inferior to the Nyquist-Shannon sampling theorem.
In this work we propose a hybrid architecture that works both in the k-space
(or frequency-domain) and the image (or spatial) domains. Our network is
composed of a complex-valued residual U-net in the k-space domain, an inverse
Fast Fourier Transform (iFFT) operation, and a real-valued U-net in the image
domain. Our experiments demonstrated, using MR raw k-space data, that the
proposed hybrid approach can potentially improve CS reconstruction compared to
deep-learning networks that operate only in the image domain. In this study we
compare our method with four previously published deep neural networks and
examine their ability to reconstruct images that are subsequently used to
generate regional volume estimates. We evaluated undersampling ratios of 75%
and 80%. Our technique was ranked second in the quantitative analysis, but
qualitative analysis indicated that our reconstruction performed the best in
hard to reconstruct regions, such as the cerebellum. All images reconstructed
with our method were successfully post-processed, and showed good volumetry
agreement compared with the fully sampled reconstruction measures.
