Re-UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction

In recent years, the growing awareness of public health has brought attention to low-dose computed tomography (LDCT) scans. However, the CT image generated in this way contains a lot of noise or artifacts, which make increasing researchers to investigate methods to enhance image quality. The advancement of deep learning technology has provided researchers with novel approaches to enhance the quality of LDCT images. In the past, numerous studies based on convolutional neural networks (CNN) have yielded remarkable results in LDCT image reconstruction. Nonetheless, they all tend to continue to design new networks based on the fixed network architecture of UNet shape, which also leads to more and more complex networks. In this paper, we proposed a novel network model with a reverse U-shape architecture for the noise reduction in the LDCT image reconstruction task. In the model, we further designed a novel multi-scale feature extractor and edge enhancement module that yields a positive impact on CT images to exhibit strong structural characteristics. Evaluated on a public dataset, the experimental results demonstrate that the proposed model outperforms the compared algorithms based on traditional U-shaped architecture in terms of preserving texture details and reducing noise, as demonstrated by achieving the highest PSNR, SSIM and RMSE value. This study may shed light on the reverse U-shaped network architecture for CT image reconstruction, and could investigate the potential on other medical image processing.