Dartmouth and Medtronic have established an academic-industrial partnership to develop, validate, and evaluate a multimodality neurosurgical image-guidance platform for brain tumor resection surgery that is capable of updating the spatial relationships between preoperative images and the current surgical field. Previous studies have shown that brain shift compensation through a modeling framework using intraoperative ultrasound and/or visible light stereovision to update preoperative MRI appears to result in improved accuracy in navigation. However, image updates have thus far only been produced retrospective to surgery in large part because of gaps in the software integration and information flow between the co-registration and tracking, image acquisition and processing, and image warping tasks which are required during a case. This paper reports the first demonstration of integration of a deformation-based image updating process for brain shift modeling with an industry-standard image guided surgery platform. Specifically, we have completed the first and most critical data transfer operation to transmit volumetric image data generated by the Dartmouth brain shift modeling process to the Medtronic StealthStation® system. StealthStation® comparison views, which allow the surgeon to verify the correspondence of the received updated image volume relative to the preoperative MRI, are presented, along with other displays of image data such as the intraoperative 3D ultrasound used to update the model. These views and data represent the first time that externally acquired and manipulated image data has been imported into the StealthStation® system through the StealthLink® portal and visualized on the StealthStation® display.

Date Published: 12 March 2013
PDF: 6 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867119 (12 March 2013); doi: 10.1117/12.2007987

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