Position-based dynamics simulator of vessel deformations for path planning in robotic endovascular catheterization

Journal Article (2022)
Author(s)

Zhen Li (TU Delft - Medical Instruments & Bio-Inspired Technology, Politecnico di Milano)

Enrico Manzionna (Politecnico di Milano)

Giovanni Monizzi (IRCCS)

Angelo Mastrangelo (IRCCS)

Maria Elisabetta Mancini (IRCCS)

Daniele Andreini (University of Milan, IRCCS)

Jenny Dankelman (TU Delft - Medical Instruments & Bio-Inspired Technology)

Elena De Momi (Politecnico di Milano)

Research Group
Medical Instruments & Bio-Inspired Technology
DOI related publication
https://doi.org/10.1016/j.medengphy.2022.103920
More Info
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Publication Year
2022
Language
English
Research Group
Medical Instruments & Bio-Inspired Technology
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Journal title
Medical Engineering and Physics
Volume number
110
Article number
103920
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Abstract

A major challenge during autonomous navigation in endovascular interventions is the complexity of operating in a deformable but constrained workspace with an instrument. Simulation of deformations for it can provide a cost-effective training platform for path planning. Aim of this study is to develop a realistic, auto-adaptive, and visually plausible simulator to predict vessels’ global deformation induced by the robotic catheter's contact and cyclic heartbeat motion. Based on a Position-based Dynamics (PBD) approach for vessel modeling, Particle Swarm Optimization (PSO) algorithm is employed for an auto-adaptive calibration of PBD deformation parameters and of the vessels movement due to a heartbeat. In-vitro experiments were conducted and compared with in-silico results. The end-user evaluation results were reported through quantitative performance metrics and a 5-Point Likert Scale questionnaire. Compared with literature, this simulator has an error of 0.23±0.13% for deformation and 0.30±0.85mm for the aortic root displacement. In-vitro experiments show an error of 1.35±1.38mm for deformation prediction. The end-user evaluation results show that novices are more accustomed to using joystick controllers, and cardiologists are more satisfied with the visual authenticity. The real-time and accurate performance of the simulator make this framework suitable for creating a dynamic environment for autonomous navigation of robotic catheters.

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