ENTRI:机器人干预增强导航工具包

IF 3.4 Q2 ENGINEERING, BIOMEDICAL
Manish Sahu;Hisashi Ishida;Laura Connolly;Hongyi Fan;Anton Deguet;Peter Kazanzides;Francis X. Creighton;Russell H. Taylor;Adnan Munawar
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引用次数: 0

摘要

图像引导机器人介入是外科手术的变革前沿,它将先进的成像技术和机器人技术相结合,提高了手术的精确度和效果。本文探讨了在图像引导机器人研究中整合开源平台以提高态势感知能力的关键需求。我们提出了一个名为 ENTRI 的开源工具包,它将基于物理的约束制定框架 AMBF 与最先进的成像平台应用程序 3D Slicer 无缝地结合在一起。ENTRI 可帮助创建高度定制化的交互式数字双胞胎,将医学成像、机器人运动学和场景动力学的处理和可视化结合起来,实现实时机器人控制。通过可行性研究,我们在 3D Slicer 和 AMBF 中展示了物理机器人介入环境的实时同步,突出了低延迟更新和改进的可视化。本研究的源代码和补充材料可在 https://github.com/LCSR-CIIS/ENTRI 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ENTRI: Enhanced Navigational Toolkit for Robotic Interventions
Image-guided robotic interventions represent a transformative frontier in surgery, blending advanced imaging and robotics for improved precision and outcomes. This paper addresses the critical need for integrating open-source platforms to enhance situational awareness in image-guided robotic research. We present an open-source toolkit, named ENTRI, that seamlessly combines a physics-based constraint formulation framework, AMBF, with a state-of-the-art imaging platform application, 3D Slicer. ENTRI facilitates the creation of highly customizable interactive digital twins, that incorporate processing and visualization of medical imaging, robot kinematics, and scene dynamics for real-time robot control. Through a feasibility study, we showcase real-time synchronization of a physical robotic interventional environment in both 3D Slicer and AMBF, highlighting low-latency updates and improved visualization. The source code and supplementary materials for this study are available at https://github.com/LCSR-CIIS/ENTRI .
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