In Vivo Analysis of Heterogeneous Brain Deformation Computations for Model-Updated Image Guidance

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Abstract
Neurosurgical image-guidance has historically relied on the registration of the patient and preoperative imaging series with surgical instruments in the operating room (OR) coordinate space. Recent studies measuring intraoperative tissue motion have suggested that deformation-induced misregistration from surgical loading is a serious concern with such systems. In an effort to improve registration fidelity during surgery, we are pursuing an approach which uses a predictive computational model in conjunction with data available in the OR to update the high resolution preoperative image series. In previous work. we have developed an in vivo experimental system in the porcine brain which has been used to investigate a homogeneous finite element rendering of consolidation theory as a tissue deformation model. In this paper, our computational approach has been extended to include heterogeneous tissue property distributions determined from an image-to-grid segmentation scheme. Results produced under two different loading conditions show that heterogeneity in the stiffness properties and interstitial pressure gradients varied over a range of physiologically reasonable values account for 1-3% and 5-8% of the predicted tissue motion, respectively, while homogeneous linear elasticity is responsible for 60-70% of the surgically-induced motion that has been recoverable with our model-based @approach.