18 March 2016 A novel craniotomy simulation system for evaluation of stereo-pair reconstruction fidelity and tracking
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Abstract
Brain shift compensation using computer modeling strategies is an important research area in the field of image-guided neurosurgery (IGNS). One important source of available sparse data during surgery to drive these frameworks is deformation tracking of the visible cortical surface. Possible methods to measure intra-operative cortical displacement include laser range scanners (LRS), which typically complicate the clinical workflow, and reconstruction of cortical surfaces from stereo pairs acquired with the operating microscopes. In this work, we propose and demonstrate a craniotomy simulation device that permits simulating realistic cortical displacements designed to measure and validate the proposed intra-operative cortical shift measurement systems. The device permits 3D deformations of a mock cortical surface which consists of a membrane made of a Dragon Skin® high performance silicone rubber on which vascular patterns are drawn. We then use this device to validate our stereo pair-based surface reconstruction system by comparing landmark positions and displacements measured with our systems to those positions and displacements as measured by a stylus tracked by a commercial optical system. Our results show a 1mm average difference in localization error and a 1.2mm average difference in displacement measurement. These results suggest that our stereo-pair technique is accurate enough for estimating intra-operative displacements in near real-time without affecting the surgical workflow.
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Xiaochen Yang, Xiaochen Yang, Logan W. Clements, Logan W. Clements, Rebekah H. Conley, Rebekah H. Conley, Reid C. Thompson, Reid C. Thompson, Benoit M. Dawant, Benoit M. Dawant, Michael I. Miga, Michael I. Miga, } "A novel craniotomy simulation system for evaluation of stereo-pair reconstruction fidelity and tracking", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978612 (18 March 2016); doi: 10.1117/12.2217301; https://doi.org/10.1117/12.2217301
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