Hou Baohua, Du Hongwei
College of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, Anhui, China.
College of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, Anhui, China.
Magn Reson Imaging. 2025 Aug 6;124:110465. doi: 10.1016/j.mri.2025.110465.
Magnetic Resonance Imaging (MRI) is widely utilized in medical imaging due to its high resolution and non-invasive nature. However, the prolonged acquisition time significantly limits its clinical applicability. Although traditional compressed sensing (CS) techniques can accelerate MRI acquisition, they often lead to degraded reconstruction quality under high undersampling rates. Deep learning-based methods, including CNN- and GAN-based approaches, have improved reconstruction performance, yet are limited by their local receptive fields, making it challenging to effectively capture long-range dependencies. Moreover, these models typically exhibit high computational complexity, which hinders their efficient deployment in practical scenarios. To address these challenges, we propose a lightweight Multi-scale Context-Aware Generative Adversarial Network (MCA-GAN), which enhances MRI reconstruction through dual-domain generators that collaboratively optimize both k-space and image-domain representations. MCA-GAN integrates several lightweight modules, including Depthwise Separable Local Attention (DWLA) for efficient local feature extraction, Adaptive Group Rearrangement Block (AGRB) for dynamic inter-group feature optimization, Multi-Scale Spatial Context Modulation Bridge (MSCMB) for multi-scale feature fusion in skip connections, and Channel-Spatial Multi-Scale Self-Attention (CSMS) for improved global context modeling. Extensive experiments conducted on the IXI, MICCAI 2013, and MRNet knee datasets demonstrate that MCA-GAN consistently outperforms existing methods in terms of PSNR and SSIM. Compared to SepGAN, the latest lightweight model, MCA-GAN achieves a 27.3% reduction in parameter size and a 19.6% reduction in computational complexity, while attaining the shortest reconstruction time among all compared methods. Furthermore, MCA-GAN exhibits robust performance across various undersampling masks and acceleration rates. Cross-dataset generalization experiments further confirm its ability to maintain competitive reconstruction quality, underscoring its strong generalization potential. Overall, MCA-GAN improves MRI reconstruction quality while significantly reducing computational cost through a lightweight architecture and multi-scale feature fusion, offering an efficient and accurate solution for accelerated MRI.
磁共振成像（MRI）因其高分辨率和非侵入性的特点而在医学成像中得到广泛应用。然而，较长的采集时间严重限制了其临床适用性。尽管传统的压缩感知（CS）技术可以加速MRI采集，但在高下采样率下，它们往往会导致重建质量下降。基于深度学习的方法，包括基于卷积神经网络（CNN）和生成对抗网络（GAN）的方法，虽然提高了重建性能，但受到局部感受野的限制，难以有效捕捉长距离依赖关系。此外，这些模型通常具有较高的计算复杂度，这阻碍了它们在实际场景中的高效部署。为了应对这些挑战，我们提出了一种轻量级的多尺度上下文感知生成对抗网络（MCA-GAN），它通过双域生成器协同优化k空间和图像域表示来增强MRI重建。MCA-GAN集成了几个轻量级模块，包括用于高效局部特征提取的深度可分离局部注意力（DWLA）、用于动态组间特征优化的自适应组重排块（AGRB）、用于跳跃连接中多尺度特征融合的多尺度空间上下文调制桥（MSCMB）以及用于改进全局上下文建模的通道-空间多尺度自注意力（CSMS）。在IXI、MICCAI 2013和MRNet膝关节数据集上进行的大量实验表明，MCA-GAN在峰值信噪比（PSNR）和结构相似性指数（SSIM）方面始终优于现有方法。与最新的轻量级模型SepGAN相比，MCA-GAN的参数大小减少了27.3%，计算复杂度降低了19.6%，同时在所有比较方法中重建时间最短。此外，MCA-GAN在各种下采样掩码和加速率下都表现出强大的性能。跨数据集泛化实验进一步证实了其保持有竞争力的重建质量的能力，突出了其强大的泛化潜力。总体而言，MCA-GAN通过轻量级架构和多尺度特征融合提高了MRI重建质量，同时显著降低了计算成本，为加速MRI提供了一种高效准确的解决方案。
