TY - JOUR
T1 - A Neurosurgical Craniotomy Training System Based on Haptic Virtual Reality Simulation
AU - Zhang, Guobin
AU - Li, Keliang
AU - Sun, Qiyuan
AU - Wu, Wenqi
AU - Li, Shuai
AU - Liu, Zhenzhong
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Human–machine interaction
KW - neurosurgical craniotomy
KW - simulator
KW - training system
KW - virtual reality (VR)
UR - https://www.scopus.com/pages/publications/105019558852
U2 - 10.1109/THMS.2025.3616313
DO - 10.1109/THMS.2025.3616313
M3 - Article
AN - SCOPUS:105019558852
SN - 2168-2291
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
ER -