GPU-accelerated surgery simulation for opening a brain fissure

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.

Controlling the error on target motion through real‐time mesh adaptation: Applications to deep brain stimulation

“…Our work is limited in several ways, which we are currently investigating: We tackled only the discretisation error and avoided the difficult problem of “model error.” For example, we assumed the brain to behave in a corotational manner. Although this is corroborated by the literature,() we do not have physical evidence that this assumption is true in general, nor as to when it may break down. We assumed simple boundary conditions around the brain. This should be investigated in more detail to assess the effect of both geometrical and boundary condition uncertainty on the outcome of the simulations. Soft tissue properties vary from patient to patient by up to a few orders of magnitude.…”

Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation

“…The corotational formulation (Müller et al, 2002) was used by Bilger et al (2011), Dequidt et al (2015) and Sase et al (2015) to increase the range of authorized displacements (removing the artifacts of linear elasticity in large rotation) while running real-time simulations. A few brain models simulate hyper-elastic laws.…”

Biomechanical Modeling of Brain Soft Tissues for Medical Applications

“…Geometric and material non linearities are handled by the TLED method which allows computing deformations for very soft tissues in real-time. Finally, both the vascular neurosurgery simulator proposed by Dequidt et al (2015) and the opening brain ssure simulator presented in Sase et al (2015) are simulated based on a linear elastic law solved with the corotational approach(see paragraph 4.2.2). Biomechanical parameters for these two simulators are respectively set to E = 2.1 kPa, ν = 0.45 and E = 1 kPa, ν = 0.4.…”