Automatic framework for patient-specific modelling of tumour resection-induced brain shift. (April 2022)

• Record Type: Journal Article
• Authors: Yu, Yue; Safdar, Saima; Bourantas, George; Zwick, Benjamin; Joldes, Grand; Kapur, Tina; Frisken, Sarah; Kikinis, Ron; Nabavi, Arya; Golby, Alexandra; Wittek, Adam; Miller, Karol
• Is Part Of: Computers in biology and medicine. Volume 143(2022)
• Publication Date: 2022-04
• Subjects: Patient-specific modelling -- Modelling -- Biomechanical model -- Meshless methods -- Brain shift -- Tumour resection; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105271
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Graphical abstract: Image 1 Highlights: An automatic framework for patient-specific modelling of tumour resection-induced brain shift. Creating comprehensive biomechanical brain models automatically. Using a suite of meshless algorithms as solver to compute the brain deformation during the resection. Predicting tissue deformations and tumour cavity boundaries for tumour resection.