A tensor-mass method-based vascular model and its performance evaluation for interventional surgery virtual reality...

Area of Science:

• Medical Simulation
• Computational Mechanics
• Surgical Training

Background:

• Physics-based vascular modeling is crucial for developing effective endovascular surgery training systems.
• Realistic simulation shortens the learning curve for novice surgeons acquiring complex operational skills.

Purpose of the Study:

• To develop and implement a novel physics-based vascular model for endovascular surgery simulators.
• To evaluate the performance of the proposed model against existing methods in terms of realism and speed.

Main Methods:

• Formulation and implementation of a vascular model using the tensor-mass method (TMM) with multimodel representation (mechanical, visual, collision).
• Development of triangular and tetrahedral TMM formulations within the Simulation Open-source Framework Architecture (SOFA).
• Experimental validation comparing TMM with mass-spring and finite element methods for refresh rate, stability, and visual realism.

Main Results:

• The tensor-mass method (TMM) demonstrates superior performance compared to existing physics-based methods.
• Achieved realistic and real-time vascular deformation simulation with refresh rates up to 256 frames per second on a triangular vascular topology.
• Experimental results confirm the stability and visual fidelity of the TMM-based vascular model.

Conclusions:

• The developed vascular model provides a foundational module for endovascular surgery simulators.
• The model meets critical real-time and realistic simulation requirements for effective surgical training.
• This approach enhances the utility of simulators in training surgeons for endovascular interventions.