In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction.

Ping Li,Yong An,Weiwei Wang,Zhijian Song,Chenxi Zhang

doi:10.1038/srep28654

Abstract

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).

Highlights

To minimize their impact on both healthy tissue and brain function, neurosurgical procedures involving tumor resection below the cortex require the surgeon to establish a surgical path to the tumor, which causes brain retraction
We illustrated a workflow for simulating intraoperative brain retraction using the hyper-viscoelastic XFEM model-based framework
The point clouds were transformed to the postretraction image space

Summary

Introduction

To minimize their impact on both healthy tissue and brain function, neurosurgical procedures involving tumor resection below the cortex require the surgeon to establish a surgical path to the tumor, which causes brain retraction. Brain retraction caused by mechanical processes is a combination of brain tissue shear, compression and extension. Correcting for it requires modeling complex mechanical behaviors and the brain tissue topological discontinuity. Sun et al.[3] employed a linear poroelastic model to predict brain retraction that used two cameras to acquire the displacements of the retractors. Tension- and compression-induced brain tissue deformation behaviors To date, this promising biomechanical model has not been used to simulate brain retraction. We used an LRS to track displacements of the brain retractor surfaces, which were used as BCs, and the extended finite element method (XFEM) to produce an accurate representation of the tissue discontinuity. The results were quantitatively and qualitatively assessed using seven live swine

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Results

Conclusion