A Survey on the Progress of Computer-Assisted Vascular Intervention

被引:0
|
作者
Li N. [1 ,2 ]
He J. [3 ]
Chen Y. [3 ]
Zhou S. [1 ]
机构
[1] Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen
[2] University of Chinese Academy of Sciences, Beijing
[3] School of Computer Science and Engineering, Southeast University, Nanjing
关键词
angiographic image analysis; artificial intelligence; computer-assisted vascular intervention; robot-assisted vascular intervention; theranostics;
D O I
10.3724/SP.J.1089.2022.19038
中图分类号
学科分类号
摘要
Vascular diseases seriously threaten human health. As a commonly used minimally invasive surgery, vascular interventional diagnosis and treatment faces a variety of clinical burdens, so the support of computer-aided technology is urgently needed. In recent years, the rapid development of extravascular and intravascular medical imaging technology and artificial intelligence methods has promoted the related research of computer-assisted vascular intervention technology. The organic relationship between the basic content and key technologies of computer-assisted vascular intervention and its applications are reviewed and systematically analyzed, including multi-modal medical angiography image analysis, catheter guide wire position tracking, vascular associated lesion characteristics, morphological recognition, and other hot issues, and the relationships between the above contents and surgical planning and surgical navigation are analyzed. It is concluded that the clinical application trend in this field is to combine the integration of diagnosis and treatment with vascular interventional robot technology. Through the analysis of research status and development trends, it is concluded that the challenges faced by various research content are: the accuracy of the segmentation model to quantify the vascular structure and the completeness of topology extraction, intervention path vectorization and accuracy of multimodal information fusion, correlation between vascular anatomical labeling and surgical path navigation, and the level and evaluation method of human-computer interaction in vascular intervention. Analysis shows that in the process of solving these problems, the artificial intelligence diagnosis and treatment technology will continue to be promoted, especially the rapid development and clinical application of robot-assisted vascular intervention. © 2022 Institute of Computing Technology. All rights reserved.
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页码:985 / 1010
页数:25
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共 161 条
  • [1] Hu Shengshou, Gao Runlin, Liu Lisheng, Et al., Summary of the 2018 report on cardiovascular diseases in China, Chinese Circulation Journal, 34, 3, pp. 209-220, (2019)
  • [2] Brief report on stroke prevention and treatment in China, 2019[J], Chinese Journal of Cerebrovascular Diseases, 17, 5, pp. 272-281, (2020)
  • [3] Duan Jijun, Yan Yaqiong, Yang Niannian, Et al., International comparison analysis of China’s cancer incidence and mortality, Chinese Journal of the Frontiers of Medical Science (Electronic Version), 8, 7, pp. 17-23, (2016)
  • [4] Goldstein J A, Balter S, Cowley M, Et al., Occupational hazards of interventional cardiologists: prevalence of orthopedic health problems in contemporary practice, Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography & Interventions, 63, 4, pp. 407-411, (2004)
  • [5] Duran C, Lumsden A B, Bismuth J., A randomized, controlled animal trial demonstrating the feasibility and safety of the Magellan™ endovascular robotic system, Annals of Vascular Surgery, 28, 2, pp. 470-478, (2014)
  • [6] Swaminathan R V, Rao S V., Robotic-assisted transradial diagnostic coronary angiography, Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography & Interventions, 92, 1, pp. 54-57, (2018)
  • [7] Khan E M, Frumkin W, Ng G A, Et al., First experience with a novel robotic remote catheter system: Amigo™ mapping trial, Journal of Interventional Cardiac Electrophysiology: An International Journal of Arrhythmias and Pacing, 37, 2, pp. 121-129, (2013)
  • [8] Ma H, Smal I, Daemen J, Et al., Dynamic coronary roadmap-ping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering, Medical Image Analysis, 61, (2020)
  • [9] Zhou Y J, Xie X L, Zhou X H, Et al., Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy, Computerized Medical Imaging and Graphics, 83, (2020)
  • [10] Matl S, Brosig R, Baust M, Et al., Vascular image registration techniques: a living review, Medical Image Analysis, 35, pp. 1-17, (2017)