ARXIV 1 Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction Roberto Souza, PhD, Mariana Bento, PhD, Nikita Nogovitsyn, MSc, MD, Kevin J. Chung, BSc, R. Marc Lebel, PhD, and Richard Frayne, PhD Abstract --The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two element network combinations were evaluated for the four possible image-k-space domain configurations: a) W-net II, b) W-net KK, c) W-net IK, and d) W-net KI were evaluated. Selected promising four element networks (WW-nets) were also examined. Two configurations of each network were compared: 1) Each coil channel processed independently, and 2) all channels processed simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil k-space datasets were used in the experiments. Normalized root mean squared error, peak signal to noise ratio, visual information fidelity and visual inspection were used to assess the reconstructed images against the fully sampled reference images. Our results indicated that networks that operate solely in the image domain are better suited when processing individual channels of multi-channel data independently. Dual domain methods are more advantageous when simultaneously reconstructing all channels of multi-channel data. Also, the appropriate cascade of U-nets compared favorably ( p 0 . Index T erms --Magnetic resonance imaging, compressed sensing, multi-channel (coil), image reconstruction, inverse problems, brain, machine learning M AGNETIC RESONANCE (MR) imaging is a sensitive diagnostic modality that allows specific, high-quality investigation of structure and function of the brain and body. One major drawback is the overall acquisition time to complete an MR imaging protocol, which can easily exceed 30 minutes per patient [1]. Lengthy MR examination times are costly ( 300 USD or more per examination); increase susceptibility to patient motion artifacts, which negatively impact image quality; further reduce patient throughput and contribute to repeated studies.