Preoperative Image Updating for Guidance During Brain Tumor Resection

描述（由申请人提供）：本资助申请响应计划公告编号PAR-10-169（用于癌症研究的体内成像系统转化的学术-工业合作伙伴关系），提议达特茅斯和Medtronic Navigation（Louisville，CO）合作开发、验证和评价能够进行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、计算算法和专业知识来生成MR图像更新，并利用美敦力作为神经应用最先进图像引导系统领先商业开发商的能力和地位。如本提案中更详细描述的，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，它们将提高引导信息相对于共配准术前图像的诊断准确性。然而，更新是在手术后进行的，这在很大程度上是因为软件集成和信息流之间的差距，包括配准和跟踪、术中图像采集和处理以及病例期间所需的图像更新任务。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）通过工业标准神经图像引导系统的商业级集成开发图像更新平台，以及（2）验证该方法的诊断性能，并开始评价外科医生可获得更新后的图像引导信息时达到的手术准确性。考虑到在神经外科实践中已经从术前图像引导中获得的实质性收益，其中外科医生在动态演变的术野中明显的视觉线索与初始共配准但静态的术前图像体积之间进行推断，导航系统，建立图像参考框架的共配准似乎为外科医生提供了一致实现最精确手术的显著优势可能事实上，我们假设，即使是说明在手术开始时打开硬脑膜时发生的初始的、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，从而将外科医生所需的外推量减少到足以提高手术准确性的程度。通过与美敦力合作，我们将创建并验证一个能够进行图像更新的引导平台，该平台可以在未来轻松复制和传播，以支持旨在证明手术和患者结局相对于术前图像引导标准护理的改善的前瞻性临床试验。

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.