Li, X.; Zhang, H.; Yang, H.; Li, T.-Q. CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism. Sensors2023, 23, 7685.
Li, X.; Zhang, H.; Yang, H.; Li, T.-Q. CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism. Sensors 2023, 23, 7685.
Li, X.; Zhang, H.; Yang, H.; Li, T.-Q. CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism. Sensors2023, 23, 7685.
Li, X.; Zhang, H.; Yang, H.; Li, T.-Q. CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism. Sensors 2023, 23, 7685.
Abstract
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 recon-struction 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 escalating model complexity. 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 fo-cusing 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 af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.
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