Preoperative Image Updating for Guidance During Brain Tumor Resection

描述（由申请人提供）：本拨款申请响应项目公告编号PAR-10-169（癌症研究体内成像系统翻译学术-工业合作伙伴关系），提议达特茅斯大学与美敦力导航公司（路易斯维尔，CO）合作开发、验证和评估一种能够术中图像更新的多模态神经外科图像引导平台。该项目将利用达特茅斯现有的基础设施、计算算法和生成MR图像更新的专业知识，并利用美敦力作为神经应用领域最先进图像引导系统的领先商业开发商的能力和地位。正如本提案中更详细描述的那样，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，相对于联合注册的术前图像，它们将提高指导信息的诊断准确性。然而，这些更新在很大程度上是回顾性的，因为在软件集成和信息流方面存在差距，在共同配准和跟踪、术中图像采集和处理以及在病例中需要的图像更新任务之间存在差距。为了克服将图像更新技术转化为手术室的这些障碍，我们建议(1)通过商业级集成工业标准神经图像引导系统开发一个图像更新平台；(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准确性。鉴于术前图像引导在神经外科实践中取得的巨大成果，外科医生在动态变化的手术视野中明显的视觉线索和最初共同注册但静态的术前图像量之间进行推断，重新建立图像参考框架的指导系统似乎为外科医生提供了显著的优势，可以持续实现最精确的手术。事实上，我们假设，即使是在手术开始时，由于硬脑膜打开而发生的最初的、通常是实质性的脑转移的更新，也会显著提高指导信息的诊断准确性，从而减少外科医生所需的外推量，从而足以提高手术的准确性。通过与美敦力合作，我们将创建并验证一个能够更新图像的指导平台，该平台可以在未来很容易地复制和传播，以支持前瞻性临床试验，旨在证明相对于术前图像指导护理标准，手术和患者结果得到改善。

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.