Pescio Matteo, Li Chenhao, Kundrat Dennis, Casadio Maura, Dagnino Giulio
Politecnico di Torino, Turin, Italy.
Università degli Studi di Genova, Genoa, Italy.
Int J Comput Assist Radiol Surg. 2025 Jun 24. doi: 10.1007/s11548-025-03458-2.
Cardiovascular diseases are the leading cause of mortality globally. Advances in interventional radiology and endovascular devices have made endovascular procedures effective alternatives to traditional open surgery, leading to their routine application in clinical practice. Within this framework, novel technologies, including robotic platforms and navigation software, have been developed to assist clinicians in executing endovascular interventions with improved dexterity, enhanced guidance, and superior clinical training, ultimately yielding better patient outcomes.
This study aims to develop a model-based simulation environment within the SOFA framework, to enable shape and force sensing for endovascular robotic procedures. The vascular catheter was modeled using beam theory, and realistic interactions between the catheter and vascular models were established using the finite element method (FEM) with both linear elastic and nonlinear hyper-elastic models. Experiments measured contact forces and positional changes during catheter insertion, comparing anatomical deformations with simulation results.
Experimental tests validated the simulated force and displacement measurements. The catheter contact force showed an absolute error of 0.0371 N (30.45%). Catheter tip displacement averaged 3.1 mm, and the proximal segment's Fréchet distance averaged 3.6 mm. For the anatomical model, the elastic FEM model performed best, with deformation measurement errors of 34%, 19%, and 59% across three different force scenarios.
The results indicate that the integration of advanced physical modeling, realistic human-robot interactions, and enhanced computational capabilities will facilitate the development of innovative solutions, enabling clinicians to achieve greater accuracy and reliability in minimally invasive surgical (MIS) applications, particularly in endovascular interventions.
心血管疾病是全球死亡的主要原因。介入放射学和血管内装置的进展使血管内手术成为传统开放手术的有效替代方案，从而导致其在临床实践中的常规应用。在此框架内，已经开发了包括机器人平台和导航软件在内的新技术，以协助临床医生以更高的灵活性、更强的引导性和更优质的临床培训来执行血管内介入手术，最终产生更好的患者治疗效果。
本研究旨在在SOFA框架内开发基于模型的模拟环境，以实现血管内机器人手术的形状和力传感。使用梁理论对血管导管进行建模，并使用线性弹性和非线性超弹性模型的有限元方法（FEM）建立导管与血管模型之间的实际相互作用。实验测量了导管插入过程中的接触力和位置变化，将解剖变形与模拟结果进行了比较。
实验测试验证了模拟的力和位移测量结果。导管接触力的绝对误差为0.0371 N（30.45%）。导管尖端位移平均为3.1 mm，近端节段的弗雷歇距离平均为3.6 mm。对于解剖模型，弹性有限元模型表现最佳，在三种不同力的情况下变形测量误差分别为34%、19%和59%。
结果表明，先进物理建模、实际人机交互和增强计算能力的整合将促进创新解决方案的开发，使临床医生在微创手术（MIS）应用中，特别是在血管内介入手术中，能够实现更高的准确性和可靠性。
