Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging

Abstract

Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.