A Practical Workflow for Implementing FEM-Based Brain Shift Prediction in Preoperative Neurosurgical Planning
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105037468974
Journal Title
2025 International Convention on Rehabilitation Engineering and Assistive Technology I Create 2025 Conference Proceedings
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SCOPUS
Bibliographic Citation
2025 International Convention on Rehabilitation Engineering and Assistive Technology I Create 2025 Conference Proceedings (2025)
Suggested Citation
Somboonwong T., Ono K., Chumnanvej S., Suthakorn J., Ongwattanakul S. A Practical Workflow for Implementing FEM-Based Brain Shift Prediction in Preoperative Neurosurgical Planning. 2025 International Convention on Rehabilitation Engineering and Assistive Technology I Create 2025 Conference Proceedings (2025). doi:10.1109/I-CREATE67590.2025.11478110 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116591
Title
A Practical Workflow for Implementing FEM-Based Brain Shift Prediction in Preoperative Neurosurgical Planning
Corresponding Author(s)
Other Contributor(s)
Abstract
Accurate image guidance in neurosurgery is often compromised by brain shift, tissue deformation that occurs after craniotomy. While advanced intraoperative imaging can compensate for this, it remains cost-prohibitive and unavailable in many hospitals, particularly in resource-limited settings. To address this gap, we propose a practical workflow for predicting brain shift preoperatively using the finite element method (FEM) and only standard MRI and/or CT scans. The workflow includes data preparation, meshing, material assignment, simulation setup, and integration strategies for clinical adoption. It is designed to be accessible and scalable, running on standard hardware and producing results within a timeframe suitable for preoperative planning. Feasibility was demonstrated through phantom-based validation, with accuracy remaining within clinically acceptable limits. Future work will focus on automating the workflow, accelerating computation, and connecting the system with hospital ICT infrastructure to support wider clinical adoption.