Magnetic Resonance Image-Based Modeling for Neurosurgical Interventions
Magnetic Resonance Image-Based Modeling for Neurosurgical Interventions
复制标题
用于神经外科干预的基于磁共振图像的建模
DOI：
10.32604/mcb.2019.07098
复制
发表时间：
2019
影响因子：
--
通讯作者：
Feng Yuan
中科院分区：
文献类型：
--
作者：
Li Yongqiang;Lai Changxin;Zhang Chengchen;Singer Alexa;Qiu Suhao;Sun Bomin;Sacks Michael S.;Feng Yuan
植入深部脑刺激装置等手术需要将装置准确放置在大脑内。由于放置会影响性能，图像引导和机器人辅助技术已被广泛采用。这些方法需要准确预测植入期间和植入后的大脑变形。在本研究中，采用耦合欧拉-拉格朗日方法提出了基于磁共振（MR）图像的有限元（FE）模型。通过将图像体素直接映射到体积网格空间来实现解剖精度。通过评估不同手术方法对胼胝体（CC）区域变形的影响，证明了其潜在效用。结果显示，胼胝体最大位移随着介入相对中线角度的增大而增大。预测不同介入位置胼胝体的最大位移，该位移与大脑曲率以及介入区域与胼胝体（CC）之间的距离有关。 CC 区域的估计位移幅度遵循临床观察获得的位移幅度。所提出的方法提供了用于生成介入手术的真实计算模型的自动管道。结果还证明了为图像引导机器人神经手术构建患者特异性模型的潜力。
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 imageguided, robotic neurological surgery.
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影响因子：
10.9
作者：
Ji, Songbai;Roberts, David W.;Hartov, Alex;Paulsen, Keith D.
通讯作者：
Paulsen, Keith D.
影响因子：
9.7
作者：
Finan, John D.;Sundaresh, Sowmya N.;Morrison, Barclay, III
通讯作者：
Morrison, Barclay, III
影响因子：
--
作者：
S. Kleiven
通讯作者：
S. Kleiven
影响因子：
2.8
作者：
Y. Q. Li;X.-L. Gao
通讯作者：
Y. Q. Li;X.-L. Gao
DOI：
--
发表时间：
1982-10
期刊：
--
影响因子：
--
作者：
R. Christensen;L. Freund
通讯作者：
R. Christensen;L. Freund