An Implementation of FEM Simulation for Brain Shift Prediction to Enhance the Neurosurgical Planning
Issued Date
2025-01-01
Resource Type
eISSN
21693536
Scopus ID
2-s2.0-105026024338
Journal Title
IEEE Access
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Access (2025)
Suggested Citation
Somboonwong T., Ono K., Chumnanvej S., Suthakorn J., Ongwattanakul S. An Implementation of FEM Simulation for Brain Shift Prediction to Enhance the Neurosurgical Planning. IEEE Access (2025). doi:10.1109/ACCESS.2025.3648519 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113774
Title
An Implementation of FEM Simulation for Brain Shift Prediction to Enhance the Neurosurgical Planning
Corresponding Author(s)
Other Contributor(s)
Abstract
In many hospitals, especially in resource-limited settings, neurosurgeons lack access to advanced intraoperative imaging, such as intraoperative MRI or CT. This limitation severely restricts their ability to adapt surgical plans in real-time, especially when anatomical changes occur during the procedure. One of the most critical of these changes is brain shift, a phenomenon where brain tissue deforms during surgery, reducing the accuracy of image-guided interventions. This study addresses that critical gap by introducing a finite element method (FEM)-based simulation that predicts brain shift using only standard preoperative imaging, MRI and CT. Unlike existing approaches that rely on intraoperative imaging or data-driven updates, the proposed method models deformation based on physical factors such as gravity, remaining cerebrospinal fluid, and brain–skull contact, using a static linear elastic formulation. The method is feasible, hardware-efficient, and deployable in typical clinical environments. Validation through phantom experiments confirmed the feasibility and clinical acceptability of the approach, achieving a mean lesion localization error of 1.94±0.59 mm and shift-correction improvement of 46.12±25.84% compared with conventional planning. With a total simulation time of under six minutes, the proposed approach provides a practical and scalable solution to support neurosurgical planning in settings without intraoperative imaging.