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Proceedings Paper

Retractor-induced brain shift compensation in image-guided neurosurgery
Author(s): Xiaoyao Fan; Songbai Ji; Alex Hartov; David Roberts; Keith Paulsen
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Paper Abstract

In image-guided neurosurgery, intraoperative brain shift significantly degrades the accuracy of neuronavigation that is solely based on preoperative magnetic resonance images (pMR). To compensate for brain deformation and to maintain the accuracy in image guidance achieved at the start of surgery, biomechanical models have been developed to simulate brain deformation and to produce model-updated MR images (uMR) to compensate for brain shift. To-date, most studies have focused on shift compensation at early stages of surgery (i.e., updated images are only produced after craniotomy and durotomy). Simulating surgical events at later stages such as retraction and tissue resection are, perhaps, clinically more relevant because of the typically much larger magnitudes of brain deformation. However, these surgical events are substantially more complex in nature, thereby posing significant challenges in model-based brain shift compensation strategies. In this study, we present results from an initial investigation to simulate retractor-induced brain deformation through a biomechanical finite element (FE) model where whole-brain deformation assimilated from intraoperative data was used produce uMR for improved accuracy in image guidance. Specifically, intensity-encoded 3D surface profiles at the exposed cortical area were reconstructed from intraoperative stereovision (iSV) images before and after tissue retraction. Retractor-induced surface displacements were then derived by coregistering the surfaces and served as sparse displacement data to drive the FE model. With one patient case, we show that our technique is able to produce uMR that agrees well with the reconstructed iSV surface after retraction. The computational cost to simulate retractor-induced brain deformation was approximately 10 min. In addition, our approach introduces minimal interruption to the surgical workflow, suggesting the potential for its clinical application.

Paper Details

Date Published: 8 March 2013
PDF: 8 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86710K (8 March 2013); doi: 10.1117/12.2007685
Show Author Affiliations
Xiaoyao Fan, Thayer School of Engineering at Dartmouth (United States)
Songbai Ji, Thayer School of Engineering at Dartmouth (United States)
Alex Hartov, Thayer School of Engineering at Dartmouth (United States)
Norris Cotton Cancer Ctr., Dartmouth Hitchcock Medical Ctr. (United States)
David Roberts, Dartmouth Medical School (United States)
Norris Cotton Cancer Ctr., Dartmouth Hitchcock Medical Ctr. (United States)
Keith Paulsen, Thayer School of Engineering at Dartmouth (United States)
Dartmouth Medical School (United States)
Norris Cotton Cancer Ctr., Dartmouth Hitchcock Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Ziv R. Yaniv, Editor(s)

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