| Literature DB >> 35123136 |
Yue Yu1, Saima Safdar2, George Bourantas2, Benjamin Zwick2, Grand Joldes2, Tina Kapur3, Sarah Frisken3, Ron Kikinis3, Arya Nabavi4, Alexandra Golby3, Adam Wittek2, Karol Miller5.
Abstract
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.Entities:
Keywords: Biomechanical model; Brain shift; Meshless methods; Modelling; Patient-specific modelling; Tumour resection
Year: 2022 PMID: 35123136 PMCID: PMC9389918 DOI: 10.1016/j.compbiomed.2022.105271
Source DB: PubMed Journal: Comput Biol Med ISSN: 0010-4825 Impact factor: 6.698