A Practical Workflow for Implementing FEM-Based Brain Shift Prediction in Preoperative Neurosurgical Planning

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.