Title: Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction Show Chinese title

Title: 双域级联的U-网络用于多通道磁共振图像重建

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 < 0.01) to the previously published, state-of-the-art Deep Cascade model in in three out of four experiments. Show Chinese abstract

Abstract: U-net是一种已被用于解决多个逆问题的深度学习网络模型。在本研究中，评估了两个元件U-net在k空间（K）和图像（I）域中操作的串联，称为W-net，用于多通道磁共振（MR）图像重建。对四种可能的图像-k空间域配置中的两个元件网络组合进行了评估：a) W-net II，b) W-net KK，c) W-net IK，d) W-net KI。还检查了选定的有前景的四个元件网络（WW-nets）。对每个网络的两种配置进行了比较：1) 每个线圈通道独立处理，2) 所有通道同时处理。实验中使用了111个体积、T1加权、12通道线圈k空间数据集。使用归一化均方根误差、峰值信噪比、视觉信息保真度和视觉检查来评估重建图像与完全采样参考图像的对比。我们的结果表明，仅在图像域中运行的网络在独立处理多通道数据的各个通道时更为合适。当同时重建多通道数据的所有通道时，双域方法更具优势。此外，在四个实验中的三个中，适当的U-net级联相比之前发表的最先进的Deep Cascade模型表现更优（p < 0.01）。