Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data.

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Mechanics

Background:

• Brain shift during neurosurgery reduces tumor resection accuracy.
• Finite element models (FEMs) are used to predict brain shift, but their accuracy and efficiency lack evaluation using public data.

Purpose of the Study:

• To evaluate and compare various FEMs for predicting inward brain shift using intraoperative imaging data.
• To analyze the impact of different parameters on FEM accuracy and computational efficiency.

Main Methods:

• Utilized the public REtroSpective Evaluation of Cerebral Tumors (RESECT) database with data from four low-grade glioma patients.
• Evaluated FEMs with varying boundary conditions, mesh sizes, and material properties, including head rotation angles.
• Analyzed effects of tissue viscoelasticity, mesh size, craniotomy position, CSF drainage, and meningeal rigidity.

Main Results:

• Initial mean target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration.
• FEM prediction with non-rigid meninges and heterogeneous material reduced mean TRE by 1.84 ± 0.83 mm.
• Non-rigid meninges modeling was statistically significant for low-grade gliomas; heterogeneity was not.

Conclusions:

• Non-rigid meninges modeling is crucial for accurate brain shift prediction in neurosurgery.
• Optimal head orientation and CSF drainage estimation requires minimal parameter steps, leading to low TRE fluctuation.