A Neurosurgical Craniotomy Training System Based on Haptic Virtual Reality Simulation

Guobin Zhang; Keliang Li; Qiyuan Sun; Wenqi Wu; Shuai Li; Zhenzhong Liu

doi:10.1109/THMS.2025.3616313

A Neurosurgical Craniotomy Training System Based on Haptic Virtual Reality Simulation

Guobin Zhang
, Keliang Li
, Qiyuan Sun
, Wenqi Wu
, Shuai Li
, Zhenzhong Liu^*

^*Corresponding author for this work

BA56 Cognitive production industry

Tianjin University of Technology
University of Oulu
VTT (former employee or external)

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

Traditional neurosurgical training modes face challenges including high costs, limited resources, lengthy learning curves, and difficulties in personalized training. In this article, we developed an immersive neurosurgical craniotomy virtual training system (NeuroSimulator) that integrates haptic feedback, enabling comprehensive surgical skill learning through an operator-control interface. Specifically, we constructed the comprehensive neurosurgical craniotomy surgical procedural (CNCSP) dataset to guide operators in repetitive learning and personalized training of relevant surgical skills. To address surgical site model rendering complexity, we proposed an algorithm that integrates vertex curvature and edge-length cost calculation factors (VC&ECL-QEM), resolving the incompatibility between surgical area image rendering quality and efficiency. For intracranial soft tissue haptic deformation, we developed a hybrid soft tissue haptic deformation (HBD) model that combines mass-spring and volumetric elements, addressing the collapse and distortion issues of traditional models and achieving more realistic soft tissue haptic deformation. Experimental results demonstrate that VC&ECL-QEM can simplify nonsurgical area feature preservation while maintaining surgical site detail features, reflecting the effectiveness of model simplification. The HBD model focuses on improving soft tissue deformation realism and shows high consistency with finite element model deformation effects. A total of 83 participants highly recognized NeuroSimulator’s system performance in terms of operational compliance, rendering real-time performance, and deformation realism, achieving effective improvements in skill proficiency metrics including operation time, ineffective operations, guidance requests, and operation scores. NeuroSimulator provides an innovative, efficient, and practical solution for neurosurgical training and is expected to play an increasingly important role in medical education and clinical skill enhancement.

Original language	English
Pages (from-to)	953-962
Number of pages	10
Journal	IEEE Transactions on Human-Machine Systems
Volume	55
Issue number	6
DOIs	https://doi.org/10.1109/THMS.2025.3616313
Publication status	Published - Dec 2025
MoE publication type	A1 Journal article-refereed

Funding

Received 24 December 2024; revised 11 July 2025 and 28 August 2025; accepted 22 September 2025. Date of publication 17 October 2025; date of current version 2 December 2025. This work was supported in part by the National Key Research and Development Program under Grant 2022YFE0112500 and in part by the National Natural Science Foundation of China under Grant 61873188. This article was recommended by Associate Editor C. P. Hung. (Corresponding author: Zhenzhong Liu.) Guobin Zhang, Keliang Li, Qiyuan Sun, Wenqi Wu, and Zhenzhong Liu are with the Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China, and also with the National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]).

Keywords

Human–machine interaction
neurosurgical craniotomy
simulator
training system
virtual reality (VR)

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10.1109/THMS.2025.3616313

Cite this

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title = "A Neurosurgical Craniotomy Training System Based on Haptic Virtual Reality Simulation",

abstract = "Traditional neurosurgical training modes face challenges including high costs, limited resources, lengthy learning curves, and difficulties in personalized training. In this article, we developed an immersive neurosurgical craniotomy virtual training system (NeuroSimulator) that integrates haptic feedback, enabling comprehensive surgical skill learning through an operator-control interface. Specifically, we constructed the comprehensive neurosurgical craniotomy surgical procedural (CNCSP) dataset to guide operators in repetitive learning and personalized training of relevant surgical skills. To address surgical site model rendering complexity, we proposed an algorithm that integrates vertex curvature and edge-length cost calculation factors (VC\&ECL-QEM), resolving the incompatibility between surgical area image rendering quality and efficiency. For intracranial soft tissue haptic deformation, we developed a hybrid soft tissue haptic deformation (HBD) model that combines mass-spring and volumetric elements, addressing the collapse and distortion issues of traditional models and achieving more realistic soft tissue haptic deformation. Experimental results demonstrate that VC\&ECL-QEM can simplify nonsurgical area feature preservation while maintaining surgical site detail features, reflecting the effectiveness of model simplification. The HBD model focuses on improving soft tissue deformation realism and shows high consistency with finite element model deformation effects. A total of 83 participants highly recognized NeuroSimulator{\textquoteright}s system performance in terms of operational compliance, rendering real-time performance, and deformation realism, achieving effective improvements in skill proficiency metrics including operation time, ineffective operations, guidance requests, and operation scores. NeuroSimulator provides an innovative, efficient, and practical solution for neurosurgical training and is expected to play an increasingly important role in medical education and clinical skill enhancement.",

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author = "Guobin Zhang and Keliang Li and Qiyuan Sun and Wenqi Wu and Shuai Li and Zhenzhong Liu",

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A Neurosurgical Craniotomy Training System Based on Haptic Virtual Reality Simulation

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