MCU-Net：一种用于加速磁共振图像重建的带门控空间注意力的多先验协同深度展开网络
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction
AI总结：
本文提出多先验协同深度展开网络MCU-Net，通过门控空间注意力模块融合低秩与稀疏子网络重建结果，在降低计算成本的同时提升MRI重建质量。
摘要：
深度展开网络（DUNs）在加速磁共振成像（MRI）方面已展现出显著潜力。然而，它们通常面临较高的计算成本和较慢的收敛速度。此外，它们在融合多个先验时难以充分利用互补性。在本研究中，我们提出了一种多先验协同的深度展开网络，称为MCU-Net，以解决这些局限性。我们的方法采用并行结构，由分别基于低秩和稀疏性的不同优化启发子网络组成。我们设计了一个门控空间注意力模块（GSAM），用于评估不同子网络生成的中间重建结果的相对置信度（RC）和整体置信度（OC）图。RC为每个子网络表现优异的图像区域分配更大的权重，从而实现精确的逐元素协同。我们设计了校正模块，以增强在OC值较低所指示的两个子网络性能均受限的区域中的有效性，从而无需额外的分支。GSAM中的门单元被设计用于在多次迭代中保留必要信息，提高所学置信度图的准确性，并增强对累积误差的鲁棒性。在多个数据集上的实验结果表明，与前沿方法相比，PSNR和SSIM结果显著提升，同时FLOPs相对较低。此外，所提出的策略可以方便地应用于各种DUN结构以提升其性能。
英文摘要：
Deep unfolding networks (DUNs) have demonstrated significant potential in accelerating magnetic resonance imaging (MRI). However, they often encounter high computational costs and slow convergence rates. Besides, they struggle to fully exploit the complementarity when incorporating multiple priors. In this study, we propose a multi-prior collaborative DUN, termed MCU-Net, to address these limitations. Our method features a parallel structure consisting of different optimization-inspired subnetworks based on low-rank and sparsity, respectively. We design a gates-controlled spatial attention module (GSAM), evaluating the relative confidence (RC) and overall confidence (OC) maps for intermediate reconstructions produced by different subnetworks. RC allocates greater weights to the image regions where each subnetwork excels, enabling precise element-wise collaboration. We design correction modules to enhance the effectiveness in regions where both subnetworks exhibit limited performance, as indicated by low OC values, thereby obviating the need for additional branches. The gate units within GSAMs are designed to preserve necessary information across multiple iterations, improving the accuracy of the learned confidence maps and enhancing robustness against accumulated errors. Experimental results on multiple datasets show significant improvements on PSNR and SSIM results with relatively low FLOPs compared to cutting-edge methods. Additionally, the proposed strategy can be conveniently applied to various DUN structures to enhance their performance.
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