Abstract
In neurosurgery, dissection and retraction are basic techniques for approaching the site of pathology. These techniques are carefully performed in order to avoid damage to nerve tissues or blood vessels. However, novice surgeons cannot train in such techniques using the haptic cues of existing training systems. This paper proposes a real-time simulation scheme for training in dissection and retraction when opening a brain fissure, which is a procedure for creating a working space before treating an affected area. In this procedure, spatulas are commonly used to perform blunt dissection and brain tissue retraction. In this study, the interaction between spatulas and soft tissues is modeled on the basis of a finite element method (FEM). The deformation of soft tissue is calculated according to a corotational FEM by considering geometrical nonlinearity and element inversion. A fracture is represented by removing tetrahedrons using a novel mesh modification algorithm in order to retain the manifold property of a tetrahedral mesh. Moreover, most parts of the FEM are implemented on a graphics processing unit (GPU). This paper focuses on parallel algorithms for matrix assembly and matrix rearrangement related to FEM procedures by considering a sparse-matrix storage format. Finally, two simulations are conducted. A blunt dissection simulation is conducted in real time (less than 20 ms for a time step) using a soft-tissue model having 4807 nodes and 19,600 elements. A brain retraction simulation is conducted using a brain hemisphere model having 8647 nodes and 32,639 elements with force feedback (less than 80 ms for a time step). These results show that the proposed method is effective in simulating dissection and retraction for opening a brain fissure.
Highlights
Virtual reality (VR) surgery simulation is a safe and efficient approach to surgical training
Because surgeons rely on haptic cues in various contexts, the physics of soft-tissue deformation should be reliable for graphical rendering and for haptic rendering
In order to accelerate the calculation, we developed an algorithm for collision detection that is implemented on a graphics processing unit (GPU) [11]
Summary
Virtual reality (VR) surgery simulation is a safe and efficient approach to surgical training. An early simulator focusing on retraction in neurosurgery was the virtual retractor developed by Koyama et al [5] It modeled the deformations of intracranial vessels using geometrical theory, but physical consistency was not considered. Hansen et al developed a real-time simulator for brain retraction [6] An efficient implementation of an FEM solver is proposed to simulate the opening of a brain fissure. A stabilization algorithm for the fracture simulation based on element removal is incorporated into the proposed GPU-accelerated FEM framework. The three above-mentioned procedures, i.e., assembling the stiffness matrix, solving linear equations, and rearranging the stiffness matrix, are implemented by considering a sparse-matrix storage format (“Implementation and GPU parallelization”). To evaluate the performance of these implementations, a blunt dissection and brain retraction are simulated, and the results of these simulations are presented (“Results”)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
