A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction
Decreasing magnetic resonance (MR) image acquisition times can make MR examinations more accessible. Compressed sensing (CS)-based image reconstruction methods, decrease MR acquisition time by reconstructing high-quality images from data that were originally sampled at rates inferior to the Nyquist-Shannon sampling theorem. Iterative algorithms are the standard approach to solving ill-posed, CS inverse problems. These solutions are slow, thus, preventing near-real time image reconstruction. Recently, deep-learning methods have been used to solve the CS MR reconstruction problem. These proposed methods have the advantage of being able to quickly reconstruct images in a single pass using an appropriately trained network. A variety of different network architectures have been proposed to tackle the CS reconstruction problem. A drawback of these architectures is that they typically only work on image domain. In this work we propose a hybrid architecture that works both in k-space (frequency) and image domains. Our network is composed of a complex-valued residual U-net in the k-space domain, an iFFT operation, and a real-valued U-net in the image domain. Our experiments demonstrated, 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. Images reconstructed with our method were successfully post-processed, and showed good volumetry agreement compared with the fully sampled reconstruction measures.
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