Li Xia, Zhang Hui, Yang Hao, Li Tie-Qiang
College of Information Engineering, China Jiliang University, Hangzhou 310018, China.
Department of Clinical Science, Intervention, and Technology, Karolinska Institute, 14186 Stockholm, Sweden.
Sensors (Basel). 2023 Sep 6;23(18):7685. doi: 10.3390/s23187685.
Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN's average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.
压缩感知（CS）磁共振成像（MRI）在提高时间效率方面已显示出巨大潜力。深度学习技术，特别是生成对抗网络（GAN），已成为快速CS-MRI重建的有力工具。然而，随着深度学习重建模型复杂性的增加，这可能导致重建时间延长和收敛方面的挑战。在本研究中，我们提出了一种基于GAN的新型模型，该模型在不增加模型复杂性的情况下提供卓越性能。我们基于U-net架构构建的生成器模块纳入了扩张残差（DR）网络，从而在不增加参数或计算负载的情况下扩大了网络的感受野。在降采样路径的每一步，这个改进的生成器模块都包括一个DR网络，其扩张率根据网络层的深度进行调整。此外，我们引入了一种通道注意力机制（CAM）来区分通道并减少背景噪声，从而专注于关键信息。该机制巧妙地结合了全局最大池化和平均池化方法来优化通道注意力。我们使用人脑的公共领域MRI数据集对设计的模型进行了全面实验。消融研究证实了网络中修改模块的有效性。即使在10倍CS加速下，纳入DR网络和CAM平均分别将重建图像的峰值信噪比（PSNR）提高了约1.2 dB和0.8 dB。与其他相关模型相比，我们提出的模型表现出卓越性能，不仅实现了出色的稳定性，而且在PSNR和结构相似性（SSIM）方面优于大多数比较网络。与U-net相比，DR-CAM-GAN在SSIM和PSNR方面的平均增益分别为14%和15%。其均方误差（MSE）降低了两到七倍。该模型为提高CS-MRI重建的效率和质量提供了一条有前景的途径。
