The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network

Abstract

Background: The dose of radiation a patient receives when undergoing dual-energy computed tomography (CT) is of significant concern to the medical community, and balancing the tradeoffs between the level of radiation used and the quality of CT images is challenging. This paper proposes a method of synthesizing high-energy CT (HECT) images from low-energy CT (LECT) images using a neural network that achieves an alternative to HECT scanning by employing an LECT scan, which greatly reduces the radiation dose a patient receives.Methods: In the training phase, the proposed structure cyclically generates HECT and LECT images to improve the accuracy of extracting edge and texture features. Specifically, we combine multiple connection methods with channel attention (CA) and pixel attention (PA) mechanisms to improve the network's mapping ability of image features. In the prediction phase, we use a model consisting of only the network component that synthesizes HECT images from LECT images.Results: Our proposed method was conducted on clinical hip CT image data sets from Guizhou Provincial People's Hospital. In a comparison with other available methods [a generative adversarial network (GAN), a residual encoder-to-decoder network with a visual geometry group (VGG) pretrained model (RED-VGG), a Wasserstein GAN (WGAN), and CycleGAN] in terms of metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized mean square error (NMSE), and a visual effect evaluation, the proposed method was found to perform better on each of these evaluation criteria. Compared with the results produced by CycleGAN, the proposed method improved the PSNR by 2.44%, the SSIM by 1.71%, and the NMSE by 15.2%. Furthermore, the differences in the statistical indicators are statistically significant, proving the strength of the proposed method. Conclusions:The proposed method synthesizes high-energy CT images from low-energy CT images, which significantly reduces both the cost of treatment and the radiation dose received by patients. Based on both image quality score metrics and visual effects comparisons, the results of the proposed method are superior to those obtained by other methods. 2 Zhou et al. Synthesis of high-energy CT images

Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions

“…First, only one single DL-based CT image denoising algorithm, i.e., the U-Net, was validated. For other CT image denoising networks, e.g., GAN based DL algorithms ( 30 - 32 ) and 3D U-Net ( 33 ), we do not know how the results would become and careful comparisons are needed in future. Second, the different resolution data in this study were only obtained through numerical experiments of clinical CT images, and more evaluations of LDCT physical experiments need to be investigated in the future.…”

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set

“…U-Net-style networks have been successfully applied in various medical image-processing fields with stunning results ( 34 , 35 ). Hence, the generator in this article adopts a U-Net ( 36 ) style-network and introduces a residual structure ( 37 ) and an attention mechanism ( 38 ) to enhance the feature mapping and learning capabilities of the model, and to improve the stability of the model training process.…”

“…Attention mechanisms focus on useful information and reduce the weight of unimportant information. Previous image-processing research has achieved better effect enhancements through the introduction of attention mechanisms ( 32 , 34 , 41 ). Inspired by these works, we introduced attention mechanisms ( 38 ) into the proposed network, including channel attention and spatial attention mechanisms.…”