Model-Updated Image-Guided Neurosurgery: Preliminary Analysis Using Intraoperative MR.

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Mechanics

Background:

• Intraoperative brain shift poses challenges in neurosurgery, potentially leading to inaccurate tumor resection.
• Accurate prediction of brain deformation is crucial for improving surgical outcomes.

Purpose of the Study:

• To compare initial clinical data from an intraoperative Magnetic Resonance (MR) system with predictions from a three-dimensional finite element model of brain deformation.
• To evaluate the model's ability to account for intraoperative conditions like gravity and tumor decompression.

Main Methods:

• Collected preoperative and intraoperative MR data from a patient with an astrocytoma (grade 3).
• Co-registered image volumes and retrospectively measured cortical displacements and subsurface structure movements.
• Compared measured data with finite element model predictions under simulated intraoperative conditions.

Main Results:

• The finite element model predicted that gravity and decompression accounted for approximately 40% and 30% of shifting structures, respectively, totaling a 70% recovery.
• Results suggest a potential non-uniform decompressive stress distribution during tumor resection.
• Model predictions constrained by intraoperative surface data show promise for correcting brain shift.

Conclusions:

• Finite element modeling constrained by intraoperative data is a promising approach for correcting brain shift during surgery.
• Further clinical validation with volumetric intraoperative MR data is necessary to refine understanding of tissue mechanics during resection.