LAUSR

Compressed-sensing-based magnetic resonance imaging (CS-MRI) methods can significantly shorten scanning time while ensuring reconstructed image quality. Recently, deep learning methods, particularly generative adversarial networks (GAN), have been introduced into CS-MRI. However, these GAN-based methods suffer from their heavy learning parameters and ignore long-range dependency, which degrades the reconstructed image quality. Thus, the objective of this study is to design an efficient lightweight GAN to achieve more accurate MRI reconstruction. The proposed framework, named SepGAN, utilises depthwise separable convolution as the basic component to reduce the number of learning parameters. Two modules, the dilated depthwise separable convolution dense block and a squeeze-and-excitation lightweight self-attention module were proposed to extract the long-range dependency and improve the representational ability. The focal frequency loss was also involved in assisting the model to focus on high-frequency information. To evaluate the performance of the three proposed methods and SepGAN, two brain datasets were used in our experiment. From the results of our comparison analysis, SepGAN possesses the minimum number of parameters and multiply–accumulate operations (i.e., 7.32 M and 13.62G) and outperforms other methods in a variety of evaluation metrics, especially in Frechet inception distance, proving that reconstructed images of our method have better visual effects. For unseen pathological data, SepGAN can also perform effective reconstruction with explicit tumour textures and boundaries. The experimental results demonstrate that SepGAN can reconstruct high quality images with fewer parameters and exhibit remarkable generalisation ability.