SlicerCBM: automatic framework for biomechanical analysis of the brain.

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Mechanics

Background:

• Brain shift during neurosurgery alters anatomy, complicating surgical target localization.
• Accurate prediction of intra-operative brain deformations is crucial for successful neurosurgical outcomes.
• Biomechanical models offer a potential solution for predicting these deformations.

Purpose of the Study:

• To develop an automated framework for predicting intra-operative brain deformations.
• To integrate biomechanical modeling into a streamlined workflow for neurosurgical applications.

Main Methods:

• A novel framework combining a meshless total Lagrangian explicit dynamics (MTLED) algorithm with open-source software (3D Slicer).
• Generation of patient-specific biomechanical brain models from pre-operative MRI.
• Computation of brain deformation using MTLED and output of predicted intra-operative MRI.

Main Results:

• The framework successfully predicted intra-operative deformations in nine patients across craniotomy, tumor resection, and electrode placement scenarios.
• Model construction averaged 3 minutes, with deformation computation ranging from 13-23 minutes.
• Quantitative evaluation showed ~95% of ventricle surface nodes within two times the in-plane resolution for craniotomy and tumor resection cases.

Conclusions:

• The developed framework enables broader application of biomechanical modeling in both research and clinical neurosurgery.
• Successful prediction of intra-operative deformations in nine patients validates the framework's utility.