Preoperative Image Updating for Guidance During Brain Tumor Resection

描述(由申请者提供)：这项拨款申请是对编号PAR-10-169(学术-产业合作伙伴关系)的回应，建议达特茅斯公司和美敦力导航公司(科罗拉多州路易斯维尔)合作开发、验证和评估能够在术中进行图像更新的多模式神经外科图像指导平台。该项目将利用达特茅斯公司现有的基础设施、计算算法和专业知识，利用美敦力的能力和地位，利用美敦力的能力和地位，为神经应用程序开发最先进的图像引导系统。正如本提案中更详细地描述的那样，我们现在能够在肿瘤切除手术的所有阶段产生MR图像更新，并且已经有初步证据表明，它们将提高相对于联合配准的术前图像的指导信息的诊断准确性。然而，这些更新是追溯到手术后产生的，很大程度上是因为在病例期间所需的联合登记和跟踪、术中图像采集和处理以及图像更新任务之间的软件集成和信息流方面的差距。为了克服将图像更新技术转化为OR的这些障碍，我们建议(1)通过商业级集成行业标准的神经图像引导系统来开发图像更新平台，以及(2)验证该方法的诊断性能，并开始评估当更新的图像引导信息可供外科医生使用时所获得的手术精度。考虑到神经外科实践中由于术前图像引导所产生的实质性收益，其中外科医生在动态变化的外科领域中明显的视觉线索和最初共同配准的但静态的术前图像体积之间进行外推，重新建立图像参考系的共同配准的引导系统似乎在一致地实现尽可能准确的手术方面为外科医生提供了显著的优势。事实上，我们假设，即使是说明在手术开始时硬脑膜打开时发生的最初的、通常是实质性的脑移位的更新，也将显著提高引导信息的诊断准确性，从而减少外科医生所需的外推量，从而足以提高手术精度。通过与美敦力的合作，我们将创建和验证一个能够进行图像更新的指导平台，该平台可以在未来很容易地复制和传播，以支持预期的临床试验，旨在证明相对于术前图像指导护理标准，手术和患者的结果有所改善。

DESCRIPTION (provided by applicant): 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.