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Automatic framework for patient-specific modelling of tumour resection-induced brain shift
Yue Yu1, Saima Safdar1, George Bourantas1
1Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Computers in Biology and Medicine
|February 5, 2022
Summary
This study introduces an automated framework for creating patient-specific biomechanical brain models. This tool helps predict brain shift after tumor removal, aiding surgeons in locating residual tumors.
Area of Science:
- Biomedical Engineering
- Computational Mechanics
- Neurosurgery
Background:
- Accurate prediction of brain shift is crucial for neurosurgical oncology.
- Current biomechanical models are complex and require specialized expertise.
- Tumor resection can cause significant brain deformation, impacting surgical precision.
Purpose of the Study:
- To develop an automated framework for patient-specific biomechanical brain modeling.
- To enable non-specialists to utilize sophisticated models for clinical applications.
- To predict tumor resection-induced brain shift and identify residual tumor boundaries.
Main Methods:
- Automated generation of patient-specific brain geometry from MRI data.
- Creation of computational grids and assignment of material properties.
- Solving nonlinear elasticity equations using the Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm.
Main Results:
- Demonstrated the effectiveness of the automated framework in clinical cases.
- Successfully predicted tumor resection-induced brain shift.
- Provided accurate localization of residual tumor boundaries.
Conclusions:
- The developed framework simplifies the use of advanced biomechanical models in clinical settings.
- Automated patient-specific modeling enhances surgical planning and accuracy.
- This approach has the potential to improve patient outcomes in neurosurgery.
