Method for establishing a virtual training system for surgery based on robot-assisted intracranial fine operation

By combining Qt modules, OpenGL modules, and the external library Chai3d, a surgical virtual training system was established. The Sigma.7 force feedback tactile device was integrated. Based on the cranium and LHDM model, a progressive layered constraint-driven cutting method was adopted to solve the problem of unrealistic incision edge and internal effects in the surgical virtual training system, and a balance between high-precision tactile feedback and computational efficiency was achieved.

CN122116711APending Publication Date: 2026-05-29TIANJIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TIANJIN UNIVERSITY OF TECHNOLOGY
Filing Date
2026-02-11
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing surgical virtual training systems suffer from insufficient smoothness at the incision edges, unrealistic internal incision effects, and reduced realism in force feedback, thus affecting the immersion and precision of the operation.

Method used

A surgical virtual training system was built by combining Qt modules, OpenGL modules, and the external library Chai3d. The force feedback haptic device Sigma.7 was integrated. Based on the cranial brain model and LHDM model, a progressive hierarchical constraint-driven cutting method was adopted. Interactive operation was performed through the force feedback haptic device Sigma.7, and performance data was optimized.

Benefits of technology

It achieves real-time haptic feedback, enhances the immersiveness and interactive precision of operation, ensures the accuracy of brain tissue deformation, reduces computational overhead, improves incision smoothness and internal structural integrity, and provides a safe and reliable training method.

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Abstract

The application provides a method for establishing a surgical virtual training system based on robot-assisted intracranial fine operation, comprising the following steps: establishing a surgical virtual training system through a Qt module, an OpenGL module and an external library Chai3d; establishing a brain model and a surgical instrument model in the surgical virtual training system, and integrating a force feedback haptic device Sigma.7 in the surgical virtual training system; establishing a brain tissue model based on the brain model and in combination with an LHDM model; training the brain tissue model based on a progressive layered constraint-driven cutting method; obtaining performance data by performing interactive operation on the brain tissue model through the force feedback haptic device Sigma.7, and optimizing the performance of the surgical virtual training system based on the performance data. The application can ensure high authenticity and clinical relevance of the surgical virtual training system in the surgical training process.
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