利用微机器人群对血管网络进行主动探索和重建

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xingzhou Du, Yibin Wang, Junhui Law, Kaiwen Fang, Hui Chen, Yuezhen Liu, Jiangfan Yu
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引用次数: 0

摘要

血管造影在介入手术中对血管网络成像是必不可少的。被动造影剂在血管造影中的应用高度依赖于血流方向,使得上游区域和栓塞分支的成像具有挑战性。为了准确定位血管网络中的阻塞和病变,需要主动成像。本文提出了一种积极的勘探和重建策略,可以对具有流动和阻塞的三维血管网络进行全面成像。该策略将磁粒子群作为主动体,可以根据需要将其引导到期望的方向。开发了一种图像处理单元来捕获船内蜂群的三维位置。提出了一种同步测绘和勘探序列来实现勘探,并在获得位置数据后重建三维血管网络的整体结构。所提出的策略在不同结构和条件的血管网络中得到了验证,它可以对被动造影剂无法进入的区域进行彻底的探索和重建。这种方法在定位血管系统中的狭窄、血栓和瘘管方面很有前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Active exploration and reconstruction of vascular networks using microrobot swarms

Active exploration and reconstruction of vascular networks using microrobot swarms

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.

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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
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.
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