Synthetic high-energy computed tomography image via a Wasserstein generative adversarial network with the convolutional block attention module

作者：Hai Kong, Zhidong Yuan, Haojie Zhou, Ganglin Liang, Zhonghong Yan, Guanxun Cheng, Zhanli Hu · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2023 · DOI：10.21037/qims-22-947 · 被引用次数：4 · 研究领域：Advanced X-ray and CT Imaging、Medical Imaging Techniques and Applications、Advanced Image Processing Techniques

Background: Computed tomography (CT) is now universally applied into clinical practice with its non-invasive quality and reliability for lesion detection, which highly improves the diagnostic accuracy of patients with systemic diseases. Although low-dose CT reduces X-ray radiation dose and harm to the human body, it inevitably produces noise and artifacts that are detrimental to information acquisition and medical diagnosis for CT images. Methods: This paper proposes a Wasserstein generative adversarial network (WGAN) with a convolutional block attention module (CBAM) to realize a method of directly synthesizing high-energy CT (HECT) images through low-energy scanning, which greatly reduces X-ray radiation from high-energy scanning. Specifically, our proposed generator structure in WGAN consists of Visual Geometry Group Network (Vgg16), 9 residual blocks, upsampling and CBAM, a subsequent attention block. The convolutional block attention module is integrated into the generator for improving the denoising ability of the network as verified by our ablation comparison experiments. Results: Experimental results of the generator attention module ablation comparison indicate an optimization boost to the overall generator model, obtaining the synthesized high-energy CT with the best metric and denoising effect. In different methods comparison experiments, it can be clearly observed that our proposed method is superior in the peak signal-to-noise ratio (PSNR), structural similarity ...