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

Hai Kong; Zhidong Yuan; Haojie Zhou; Ganglin Liang; Zhonghong Yan; Guanxun Cheng; Zhanli Hu

doi:10.21037/qims-22-947

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

Quant Imaging Med Surg. 2023 Jul 1;13(7):4365-4379. doi: 10.21037/qims-22-947. Epub 2023 Jun 15.

Authors

Hai Kong^#¹, Zhidong Yuan^#², Haojie Zhou³, Ganglin Liang³, Zhonghong Yan¹, Guanxun Cheng², Zhanli Hu³

Affiliations

¹ School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.
² Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China.
³ Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

^# Contributed equally.

Abstract

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 index measure (SSIM) and most of the statistics (average CT value and its standard deviation) compared to other methods. Because P<0.05, the samples are significantly different. The data distribution at the pixel level between the images synthesized by the method in this paper and the high-energy CT images is also most similar.

Conclusions: Experimental results indicate that CBAM is able to suppress the noise and artifacts effectively and suggest that the image synthesized by the proposed method is closest to the high-energy CT image in terms of visual perception and objective evaluation metrics.

Keywords: Low-dose computed tomography; convolutional block attention module (CBAM); generative adversarial networks (GAN).