Intraoperative brain shift prediction using a 3D inhomogeneous patient-specific finite element model

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Mechanics

Background:

• Intraoperative brain shift poses challenges in neurosurgery, potentially affecting surgical accuracy.
• Accurate presurgical planning and intraoperative guidance are crucial for optimizing surgical outcomes.

Purpose of the Study:

• To develop a 3D patient-specific finite element (FE) brain model for predicting intraoperative brain shift.
• To update preoperative magnetic resonance (MR) images using FE modeling for enhanced presurgical planning.

Main Methods:

• A template-based algorithm was used to construct patient-specific 3D FE brain models incorporating detailed anatomical structures.
• Gravity-induced brain shift was simulated and validated against intraoperative MR imaging data from a clinical case.
• FE models were used to update preoperative MR images, simulating intraoperative views for surgeons.

Main Results:

• The developed FE models demonstrated high mesh quality comparable to the template model.
• FE model predictions of brain shift were greater than observed intraoperatively but deemed surgically acceptable.
• Updated MR images generated from FE models were easily recognizable by surgeons.

Conclusions:

• Algorithms for creating 3D patient-specific FE brain models were successfully developed.
• The FE modeling approach enables prediction of gravity-induced brain shift and display on high-resolution MR images.
• This strategy supports both intraoperative MR image updating and presurgical planning in neurosurgery.