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乳房介入机器人的仿生设计与组织操控研究。

Study on Bionic Design and Tissue Manipulation of Breast Interventional Robot.

机构信息

Key Laboratory of Advanced Processing Technology and Intelligent Manufacturing (Heilongjiang Province), Harbin University of Science and Technology, Harbin 150080, China.

Postdoctoral Research Center of Instrument Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

出版信息

Sensors (Basel). 2024 Oct 3;24(19):6408. doi: 10.3390/s24196408.

DOI:10.3390/s24196408
PMID:39409447
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11479311/
Abstract

Minimally invasive interventional surgery is commonly used for diagnosing and treating breast cancer, but the high fluidity and deformability of breast tissue reduce intervention accuracy. This study proposes a bionic breast interventional robot that mimics the scorpion's predation process, actively manipulating tissue deformation to control target displacement and enhance accuracy. The robot's structure is designed using a modular method, and its kinematics and workspace are analyzed and solved. To address the nonlinear breast tissue deformation problem, a hierarchical tissue method is proposed to simplify the three-dimensional problem into a two-dimensional one. A two-dimensional tissue deformation solver is established based on the minimum energy method for quick resolution. The problem is treated as quasi-static, deriving the displacement relationship between external manipulation points and internal tissue targets. The method of active manipulation of tissue deformation is simulated using MATLAB (2019-b) software to verify the feasibility of the method. Results show maximum errors of 1.7 mm for prostheses and 2.5 mm for in vitro tissues in the and directions. This method improves intervention accuracy in breast surgery and offers a new solution for breast cancer diagnosis and treatment.

摘要

微创介入手术常用于诊断和治疗乳腺癌，但由于乳房组织的高流动性和可变形性，降低了介入的准确性。本研究提出了一种仿生乳房介入机器人，它模拟了蝎子的捕食过程，主动操纵组织变形来控制目标位移，提高准确性。机器人的结构采用模块化方法设计，对其运动学和工作空间进行了分析和求解。针对非线性乳房组织变形问题，提出了一种层次化组织方法，将三维问题简化为二维问题。基于最小能量法建立了二维组织变形求解器，实现快速求解。将问题视为准静态，推导出外部操作点与内部组织目标之间的位移关系。使用 MATLAB（2019-b）软件对组织变形的主动操纵方法进行了模拟，验证了该方法的可行性。结果表明，在 和 方向上，假体的最大误差为 1.7 毫米，离体组织的最大误差为 2.5 毫米。该方法提高了乳房手术中的介入准确性，为乳腺癌的诊断和治疗提供了新的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/4818171d6dda/sensors-24-06408-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/0e1cbadceaa9/sensors-24-06408-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/71c8b292f2c9/sensors-24-06408-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/4818171d6dda/sensors-24-06408-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/202144cf6ae9/sensors-24-06408-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/7819ba312ddc/sensors-24-06408-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/b044a84d5931/sensors-24-06408-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/0e1cbadceaa9/sensors-24-06408-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/d0e142b378d8/sensors-24-06408-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/cefc1d535c08/sensors-24-06408-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/b3cca7516c4b/sensors-24-06408-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/71c8b292f2c9/sensors-24-06408-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b30d/11479311/4818171d6dda/sensors-24-06408-g014.jpg

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