Abstract

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Title:: Low-dose CT denoising method with CNN and Transformer to preserve tiny details

Author(s):: LI Xiaozeng1; WANG Baozhu1; GUO Zhitao1; 2; Shanaz Sharmin Jui1; 1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China 2. Innovation and Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang 050299, China

Keywords:: Keywords: low-dose computed tomography image denoising deep learning tiny detail preservation

Abstract:: Abstract: Given that low-dose computed tomography significantly amplifies image noise due to the mitigation of radiation exposure, which degrades image quality and lowers the precision of clinical diagnoses, a novel model incorporating convolutional neural network and Transformer is established, in which an intra-patch feature extraction module is used to effectively preserve tiny details in the image. A double attention Transformer is constructed by incorporating a multiple-input channel attention module into the self-attention for tackling the problem of incorrect restoration of texture details during denoising using Swin Transformer. AAPM dataset is used for testing, and the results demonstrate that the proposed algorithm not only surpasses the existing algorithms in denoising performance, but also excels in preserving tiny details in the image.