This grant application responds to Program Announcement Number PAR-10-169 (Academic-Industrial Partnerships for Translation of In Vivo Imaging Systems for Cancer Investigations) by proposing a partnership between Dartmouth and Medtronic Navigation (Louisville, CO) to develop, validate and evaluate a multi- modality neurosurgical image-guidance platform capable of intraoperative image updating. The project will leverage Dartmouth's existing infrastructure, computational algorithms and expertise in generating MR image updates with Medtronic's capabilities and stature as a leading commercial developer of state-of-the-art image guidance systems for neuro-applications. As described in more detail in this proposal, we are now able to produce MR image-updates throughout all phases of tumor resection surgery and already have preliminary evidence that they will improve the diagnostic accuracy of the guidance information relative to co-registered preoperative images. However, the updates have been produced retrospective to surgery in large part because of gaps in the software integration and information flow between the co-registration and tracking, intraoperative image acquisition and processing, and image-updating tasks which are required during a case. To overcome these barriers to translating the image updating technique into the OR, we are proposing (1) to develop an image-updating platform through commercial-grade integration of an industry standard neuro-image guidance system and (2) to validate the diagnostic performance of the approach and to begin to evaluate the surgical accuracy attained when the updated image-guidance information is available to the surgeon. Given the substantial gains in neurosurgical practice that have resulted from preoperative image-guidance where the surgeon extrapolates between visual cues evident in the dynamically evolving surgical field and the initially co-registered but static preoperative image volume, a guidance system which re-establishes coregistration of the image reference frame would seem to offer the surgeon significant advantages in terms of consistently achieving the most accurate surgery possible. Indeed, we hypothesize that even an update which accounts for the initial, often substantial, brain shift occurring with the opening of the dura at the start of a procedure will significantly improve the diagnostic accuracy of the guidance information thereby reducing the amount of extrapolation required by the surgeon to an extent that is sufficient to improve surgical accuracy. By partnering with Medtronic, we will create and validate a guidance platform capable of image updating that can be readily duplicated and disseminated in the future to support prospective clinical trials designed to demonstrate improvements in surgical and patient outcomes relative to the preoperative image-guidance standard of care.
Through an academic-industrial partnership with Medtronic Navigation, we will develop an image-guidance platform for brain tumor resection surgery capable of updating the spatial relationships between images acquired preoperatively and the current surgical field using estimates of brain deformation produced by modeling methods that assimilate intraoperative data recorded with low-cost imaging technologies. Preliminary data shows that image updating is definitely feasible and appears to be diagnostically more accurate. We will determine whether the updated image views, which periodically compensate for intraoperative brain shift, lead to an improvement in the diagnostic accuracy of the guidance information and begin to evaluate the surgical accuracy that results when the updated images are available to the surgeon for neuro-navigation.
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