Yu Yue, Safdar Saima, Bourantas George, Zwick Benjamin, Joldes Grand, Kapur Tina, Frisken Sarah, Kikinis Ron, Nabavi Arya, Golby Alexandra, Wittek Adam, Miller Karol
Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Comput Biol Med. 2022 Apr;143:105271. doi: 10.1016/j.compbiomed.2022.105271. Epub 2022 Jan 30.
Our motivation is to enable non-biomechanical engineering specialists to use sophisticated biomechanical models in the clinic to predict tumour resection-induced brain shift, and subsequently know the location of the residual tumour and its boundary. To achieve this goal, we developed a framework for automatically generating and solving patient-specific biomechanical models of the brain. This framework automatically determines patient-specific brain geometry from MRI data, generates patient-specific computational grid, assigns material properties, defines boundary conditions, applies external loads to the anatomical structures, and solves differential equations of nonlinear elasticity using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm. We demonstrated the effectiveness and appropriateness of our framework on real clinical cases of tumour resection-induced brain shift.
我们的动机是使非生物力学工程专家能够在临床中使用复杂的生物力学模型来预测肿瘤切除引起的脑移位，并随后了解残余肿瘤的位置及其边界。为实现这一目标，我们开发了一个框架，用于自动生成和求解特定患者的脑部生物力学模型。该框架可根据MRI数据自动确定特定患者的脑几何形状，生成特定患者的计算网格，分配材料属性，定义边界条件，对解剖结构施加外部载荷，并使用无网格全拉格朗日显式动力学（MTLED）算法求解非线性弹性微分方程。我们在肿瘤切除引起脑移位的实际临床病例中证明了我们框架的有效性和适用性。
