Abstract: Magnetic resonance imaging serves as an important tool for clinical diagnosis and lesion detection. Convolutional neural network-based magnetic resonance image reconstruction has achieved significant progress in both speed and accuracy. However, existing models mostly depend on single-domain feature extraction and remain limited to local receptive fields. This leads to slightly lower reconstruction quality for complex anatomical structures. To address these issues, this paper proposes an improved hybrid attention-enhanced dual-domain multimodal reconstruction network (AMC-Net) based on a parallel framework. The network constructs the MMFF multimodal feature fusion module. This module matches and fuses shared features between multimodal inputs. It supplements missing structural features in the initial input and reduces artifactual interference. The method builds a two-branch PIRN reconstruction subnetwork based on the iterative shrinkage thresholding algorithm. The subnetwork employs multi-layer iteration and dual-domain information interaction for progressive reconstruction. It introduces an improved attention-based convolution mechanism to achieve global feature learning. Additionally, the approach integrates synergistic channel-space attention and self-attention mechanisms. These mechanisms refine reconstruction results by enhancing detailed features and suppressing artifacts. They improve the restoration quality of tiny structures and enhance overall visual clarity. AMC-Net outperforms mainstream methods on static brain MRI reconstruction using IXI and BraTS2018 datasets, adapting well to various sampling conditions. With 5× random sampling, it achieves an average PSNR of 42.66 and SSIM above 0.97, delivering clear and detailed images.

摘要： 磁共振成像是临床辅助诊断、病变检测的重要手段。基于卷积神经网络的磁共振图像重建在速度和精准度上取得较大进展。但现有模型多依赖单一域的特征提取，且受限于局部感受野，导致复杂解剖结构的重建质量略低。为解决这些问题，基于并行框架提出一种改进的混合注意力增强的双域多模态重建网络（AMC-Net）。该网络构造MMFF多模态特征融合模块，匹配并融合多模态输入之间的共享特征，补充初始输入中缺失的结构特征，尽量减轻初始输入的伪影干扰。基于迭代收缩阈值算法构建双分支的PIRN重建子网络，采用多层迭代和双域信息交互数据流实现渐进式重建，并引入注意力机制改进的卷积实现全局特征学习。此外，协同通道-空间注意力和自注意力机制以精细化重建结果，突出细节特征并抑制伪影，从而提升微小结构的恢复质量和整体视觉效果。实验结果表明，AMC-Net在静态脑部MRI数据集IXI和BraTS2018上的重建效果优于主流算法，能适应多种采样条件。在5倍随机采样下，模型平均PSNR达到42.66且SSIM超过0.97，生成具备高清晰度和细节还原的重建图像。