Super resolution reconstruction of CT images based on multi-scale attention mechanism

Abstract

CT diagnosis has been widely used in clinic because of its special diagnostic value. The image resolution of CT imaging system is constrained by X-ray focus size, detector element spacing, reconstruction algorithm and other factors, which makes the generated CT image have some problems, such as low contrast, insufficient high-frequency information, poor perceptual quality and so on. To solve the above problems, a super-resolution reconstruction method of CT image based on multi-scale attention mechanism is proposed. First, use a 3 × 3 and a 1 × 1 convolution layer extracting shallow features. In order to better extract the high-frequency features of CT images and improve the image contrast, a multi-scale attention module is designed to adaptively detect the information of different scales, improve the expression ability of features, integrate the channel attention mechanism and spatial attention mechanism, and pay more attention to important information, retain more valuable information. Finally, sub-pixel convolution is used to improve the resolution of CT image and reconstruct high-resolution CT image. The experimental results show that this method can effectively improve the CT image contrast and suppress the noise. The peak signal-to-noise ratio and structural similarity of the reconstructed CT image are better than the comparison method, and has a good subjective visual effect.