Wu, ChulinZhang, HeyeChen, JiaqiGao, ZhifanZhang, PengfeiMuhammad, KhanDel Ser, Javier2022-05Wu , C , Zhang , H , Chen , J , Gao , Z , Zhang , P , Muhammad , K & Del Ser , J 2022 , ' Vessel-GAN : Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks ' , Future Generation Computer Systems , vol. 130 , pp. 128-139 . https://doi.org/10.1016/j.future.2021.12.0070167-739XPublisher Copyright: © 2021 Elsevier B.V.Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative adversarial network named vessel-GAN, which resorts to explainable knowledge-based artificial intelligence to perform image translation with increased trustworthiness. Specifically, we design a loss term to better learn the representations of blood vessels in CT angiography images. The loss term based on expert knowledge guides the generator to focus its training on the important features predicted by the discriminator. Additionally, we propose a generator architecture that effectively fuses spatio-temporal representations and further enhances temporal consistency, thereby improving the quality of the generated CT angiography images. The experiment is conducted on a dataset consisting of 232 patients with suspected coronary artery stenosis. Experimental results show that the PSNR value of vessel-GAN is 28.32 dB, SSIM value is 0.91 and MAE value is 47.36. To validate the effectiveness of the proposed synthesis method, we compare that with other image translation frameworks and GAN-based methods. Compared to other image translation methods, the proposed method vessel-GAN can generate more clearly visible blood vessels from source perfusion images. The CTA images generated by vessel-GAN are closer to the real CTA due to the use of adversarial learning. Compared with other GAN-based methods, vessel-GAN can produce sharper and more homogeneous outputs, including realistic vascular structures. The experiment demonstrates that the explainable generative adversarial network has superior performance for it can better control how models learn. Overall, the CT angiography images generated by vessel-GAN can potentially replace a separate standard CT angiography, allowing the possibility of “one-stop” cardiac examination for high-risk coronary artery disease patients who need assessment of myocardial ischemia.12enginfo:eu-repo/semantics/restrictedAccessjournal article10.1016/j.future.2021.12.007AngiographyExplainable AIGenerative adversarial networksMedical image processingMyocardial CT perfusionSoftwareHardware and ArchitectureComputer Networks and CommunicationsSDG 3 - Good Health and Well-beinghttp://www.scopus.com/inward/record.url?scp=85122322272&partnerID=8YFLogxK