CT image super-resolution reconstruction based on multi-scale residual network

In order to apply the super-resolution reconstruction algorithm to the field of medical imaging and improve the resolution of various medical images, a multi scale residual network model applied to CT images is proposed for the problem of the singleness of the network structure and the resolution multiplier of the current mainstream algorithm. Firstly, the model framework is built by cascading multi-level residual blocks, and the convolution kernels of three scales are used in the residual block to extract the detailed features of low-resolution images. Then, the feature map is fused in one dimension for feature mapping and data dimensionality reduction, and the multi-scale feature information is imported into the next residual block. Finally, the residual map calculated by the network is merged with the low-resolution image to reconstruct the high-resolution image. The network is trained by CT images processed by multiple magnifications, and so that a model can support multiple resolution enhancements at the same time. The experimental results show that under the 2, 3, and 4 times magnification factors, the PSNR of the reconstructed CT image is 0.87, 0.83, 1.16 dB higher than the VDSR algorithm. Therefore, the model of this paper effectively improves the super-resolution reconstruction effect of CT images, restores its detailed features more sharply, and greatly improves the practicality of the algorithm.