Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition

Low-dose computed tomography (LDCT) technology has attracted more and more attention in the field of medical imaging because of the reduction of radiation damage to the human body. However, the large amount of quantum noise contained in LDCT images can affect physicians’ judgment. To solve the problem of large amounts of quantum noise and artifacts in LDCT images, a convolutional neural network denoising model based on cartoon texture decomposition of images (CATCNN) is developed in this study based on deep learning. The model first uses a U-Net-based image decomposition sub-network to decompose LDCT images into cartoon images and texture images. The texture images are then denoised using a texture denoising sub-network based on edge protection and Efficient Channel Attention, finally, cartoon images are summed with the denoised texture images to obtain images with improved quality. Our experimental results demonstrate that the proposed model outperforms existing technologies, achieving a peak signal-to-noise ratio value of 33.4666 dB and a structural similarity value of 0.9193. The visual and quantitative evaluation results suggest that the CATCNN model effectively improves the quality of LDCT images.