Initial in-vivo analysis of 3D heterogeneous brain computations for model-updated image-guided neurosurgery

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Abstract
Registration error resulting from intraoperative brain shift due to applied surgical loads has long been recognized as one of the most challenging problems in the field of frameless stereotactic neurosurgery. To address this problem, we have developed a 3-dimensional finite element model of the brain and have begun to quantify its predictive capability in an in vivo porcine model. Previous studies have shown that we can predict the average total displacement within 15% and 6.6% error using intraparenchymal and temporal deformation sources, respectively, under relatively simple model assumptions. In this paper, we present preliminary results using a heterogeneous model with an expanding temporally located mass and show that we are capable of predicting an average total displacement to 5.7% under similar model initial and boundary conditions. We also demonstrate that our approach can be viewed as having the capability of recapturing approximately 75% of the registration inaccuracy that may be generated by preoperative-based image-guided neurosurgery.