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

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 111-111, 2019, DOI:10.32604/mcb.2019.07098

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… More >

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

Molecular & Cellular Biomechanics, Vol.16, No.4, pp. 245-251, 2019, DOI: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… More >