Open Access

ARTICLE

Magnetic Resonance Image-Based Modeling for Neurosurgical Interventions

Yongqiang Li^1,#, Changxin Lai^1,#, Chengchen Zhang², Alexa Singer¹, Suhao Qiu¹, Boming Sun², Michael S. Sacks³, Yuan Feng^1,*

1 Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
2 Department of Functional Neurosurgery, Rujin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
3 Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, USA .
# Contributed equally. This paper is submitted for Y.C. Fung best paper award competition.

* Corresponding Author: Yuan Feng. Email: .

Molecular & Cellular Biomechanics 2019, 16(4), 245-251. https://doi.org/10.32604/mcb.2019.07441

Abstract

Surgeries such as implantation of deep brain stimulation devices require accurate placement of devices within the brain. Because placement affects performance, image guidance and robotic assistance techniques have been widely adopted. These methods require accurate prediction of brain deformation during and following implantation. In this study, a magnetic resonance (MR) image-based finite element (FE) model was proposed by using a coupled Eulerian-Lagrangian method. Anatomical accuracy was achieved by mapping image voxels directly to the volumetric mesh space. The potential utility was demonstrated by evaluating the effect of different surgical approaches on the deformation of the corpus callosum (CC) region. The results showed that the maximum displacement of the corpus callosum increase with an increase of interventional angle with respect to the midline. The maximum displacement of the corpus callosum for different interventional locations was predicted, which is related to the brain curvature and the distance between the interventional area and corpus callosum (CC). The estimated displacement magnitude of the CC region followed those obtained from clinical observations. The proposed method provided an automatic pipeline for generating realistic computational models for interventional surgery. Results also demonstrated the potential of constructing patient-specific models for image-guided, robotic neurological surgery.

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