Huangxuan Zhao, Ziyang Xu, Lei Chen, Linxia Wu, Ziwei Cui, Jinqiang Ma, Tao Sun, Yu Lei, Nan Wang, Hongyao Hu, Yiqing Tan, Wei Lu, Wenzhong Yang, Kaibing Liao, Gaojun Teng, Xiaoyun Liang, Yi Li, Congcong Feng, Tong Nie, Xiaoyu Han, Dongqiao Xiang, Charles B L M Majoie, Wim H van Zwam, Aad van der Lugt, P Matthijs van der Sluijs, Theo van Walsum, Yun Feng, Guoli Liu, Yan Huang, Wenyu Liu, Xuefeng Kan, Ruisheng Su, Weihua Zhang, Xinggang Wang, Chuansheng Zheng
{"title":"大规模预训练帧生成模型可实现实时低剂量 DSA 成像：人工智能系统开发和多中心验证研究。","authors":"Huangxuan Zhao, Ziyang Xu, Lei Chen, Linxia Wu, Ziwei Cui, Jinqiang Ma, Tao Sun, Yu Lei, Nan Wang, Hongyao Hu, Yiqing Tan, Wei Lu, Wenzhong Yang, Kaibing Liao, Gaojun Teng, Xiaoyun Liang, Yi Li, Congcong Feng, Tong Nie, Xiaoyu Han, Dongqiao Xiang, Charles B L M Majoie, Wim H van Zwam, Aad van der Lugt, P Matthijs van der Sluijs, Theo van Walsum, Yun Feng, Guoli Liu, Yan Huang, Wenyu Liu, Xuefeng Kan, Ruisheng Su, Weihua Zhang, Xinggang Wang, Chuansheng Zheng","doi":"10.1016/j.medj.2024.07.025","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system.</p><p><strong>Methods: </strong>GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests.</p><p><strong>Findings: </strong>Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860).</p><p><strong>Conclusions: </strong>With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures.</p><p><strong>Funding: </strong>This study was supported by the National Key R&D Program and the National Natural Science Foundation of China.</p>","PeriodicalId":29964,"journal":{"name":"Med","volume":" ","pages":""},"PeriodicalIF":12.8000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.\",\"authors\":\"Huangxuan Zhao, Ziyang Xu, Lei Chen, Linxia Wu, Ziwei Cui, Jinqiang Ma, Tao Sun, Yu Lei, Nan Wang, Hongyao Hu, Yiqing Tan, Wei Lu, Wenzhong Yang, Kaibing Liao, Gaojun Teng, Xiaoyun Liang, Yi Li, Congcong Feng, Tong Nie, Xiaoyu Han, Dongqiao Xiang, Charles B L M Majoie, Wim H van Zwam, Aad van der Lugt, P Matthijs van der Sluijs, Theo van Walsum, Yun Feng, Guoli Liu, Yan Huang, Wenyu Liu, Xuefeng Kan, Ruisheng Su, Weihua Zhang, Xinggang Wang, Chuansheng Zheng\",\"doi\":\"10.1016/j.medj.2024.07.025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. 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The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests.</p><p><strong>Findings: </strong>Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. 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Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.
Background: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system.
Methods: GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests.
Findings: Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860).
Conclusions: With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures.
Funding: This study was supported by the National Key R&D Program and the National Natural Science Foundation of China.
期刊介绍：
Med is a flagship medical journal published monthly by Cell Press, the global publisher of trusted and authoritative science journals including Cell, Cancer Cell, and Cell Reports Medicine. Our mission is to advance clinical research and practice by providing a communication forum for the publication of clinical trial results, innovative observations from longitudinal cohorts, and pioneering discoveries about disease mechanisms. The journal also encourages thought-leadership discussions among biomedical researchers, physicians, and other health scientists and stakeholders. Our goal is to improve health worldwide sustainably and ethically.
Med publishes rigorously vetted original research and cutting-edge review and perspective articles on critical health issues globally and regionally. Our research section covers clinical case reports, first-in-human studies, large-scale clinical trials, population-based studies, as well as translational research work with the potential to change the course of medical research and improve clinical practice.
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