{"title":"Active exploration and reconstruction of vascular networks using microrobot swarms","authors":"Xingzhou Du, Yibin Wang, Junhui Law, Kaiwen Fang, Hui Chen, Yuezhen Liu, Jiangfan Yu","doi":"10.1038/s42256-025-01012-y","DOIUrl":null,"url":null,"abstract":"<p>Angiography is essential in interventional operations to image the vascular network. Passive contrast agents applied in angiography highly rely on the flow direction, making the imaging of upstream regions and embolic branches challenging. Active imaging is demanded for the accurate localization of blockages and lesions in vascular networks. Here an active exploration and reconstruction strategy is proposed, enabling full imaging of three-dimensional (3D) vascular networks with flow and blockage. The strategy implements magnetic particle swarms as active agents, which can be guided on demand towards the desired directions. An image processing unit is developed to capture the 3D position of the swarm inside the vessel. A simultaneous mapping and exploration sequence is proposed to realize the exploration, and the entire structure of the 3D vascular network is reconstructed after obtaining the position data. The proposed strategy is validated in vascular networks with different structures and conditions, and it enables the thorough exploration and reconstruction of regions that cannot be accessed by passive contrast agents. This strategy is promising in locating stenoses, thrombi and fistulae in vascular systems.</p>","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 1","pages":""},"PeriodicalIF":18.8000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1038/s42256-025-01012-y","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Angiography is essential in interventional operations to image the vascular network. Passive contrast agents applied in angiography highly rely on the flow direction, making the imaging of upstream regions and embolic branches challenging. Active imaging is demanded for the accurate localization of blockages and lesions in vascular networks. Here an active exploration and reconstruction strategy is proposed, enabling full imaging of three-dimensional (3D) vascular networks with flow and blockage. The strategy implements magnetic particle swarms as active agents, which can be guided on demand towards the desired directions. An image processing unit is developed to capture the 3D position of the swarm inside the vessel. A simultaneous mapping and exploration sequence is proposed to realize the exploration, and the entire structure of the 3D vascular network is reconstructed after obtaining the position data. The proposed strategy is validated in vascular networks with different structures and conditions, and it enables the thorough exploration and reconstruction of regions that cannot be accessed by passive contrast agents. This strategy is promising in locating stenoses, thrombi and fistulae in vascular systems.
期刊介绍:
Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements.
To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects.
Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.