Enhanced Navigation for Endoscopic Sinus Surgery Through Video Analysis

描述（由申请人提供）：本项目将利用广泛可用的高清内窥镜视频，为功能性内窥镜鼻窦手术（FESS）提供新的注册和可视化工具。这些工具将为外科医生提供更高的导航精度，并使准确测量手术进展的变化成为可能。该项目的关键创新是将计算视觉算法与传统导航方法相结合，以提供这些增强功能。算法将在FESS过程中获得的视频和导航数据进行回顾性评估。该项目有四个具体目标：目标1：开发精确到CT分辨率的视频CT配准算法。目标2：发展内窥镜图像表面形状估计方法。目标3：对基于视频ct的患者数据导航进行比较评估。目的4：评估术中对患者数据进行表面估计的准确性和可靠性。改进导航的意义在于：1)通过减少潜在的并发症和辐射暴露，提高患者的安全性和预后；2)通过改善临床工作流程和术中可视化的清晰度，降低成本。在美国，据估计每年有超过20万例鼻窦手术。所有这些都是在内窥镜指导下进行的，其中很大一部分可以或可以采用手术导航。因此，即使是结果和工作流程效率的适度改善也可以为患者和医疗保健系统带来显著的好处。该方法的创新之处在于使用内窥镜本身的图像作为基础：1)对术前或术中体积的配准，以及2)解剖表面的重建。先前的工作已经证明，这两个问题都是可以解决的。该项目将结合一个由工程和临床教师组成的经验丰富的团队的努力，并将重点放在将研究转化为临床相关数据上。该项目的方法将是发展和

DESCRIPTION (provided by applicant): This project will provide new registration and visualization tools for functional endoscopic sinus surgery (FESS) using widely available high-definition endoscopic video. These tools will provide higher accuracy navigation accuracy to the surgeon, and will make it possible to accurately measure change as surgery progresses. The key innovation in the project is the integration of algorithms for computational vision with traditional navigation methods to provide these enhancements. The algorithms will be evaluated retrospectively on video and navigation data acquired during FESS procedures. The project has four specific aims: Aim 1: Develop video-CT registration algorithms that are accurate to CT resolution. Aim 2: Develop methods for surface shape estimation from endoscopic images. Aim 3: Perform comparative evaluation of video-CT-based navigation on patient data. Aim 4: Assess the accuracy and reliability of intraoperative surface estimation on patient data. The significance of improved navigation is to 1) enhancement patient safety and outcomes by reducing potential complications and radiation exposure, and 2) to reduce cost by improving clinical workflow and clarity of intraoperative visualization. In the United States, it is estimate that there are more than 200,000 sinus surgery procedures performed annually. All of these are performed under endoscopic guidance, and a large fraction can or could employ surgical navigation. Thus, even moderate improvements in outcome and workflow efficiency can lead to significant benefits to both patients and the health care system. The innovation of the proposed approach is the use of the images from the endoscope itself as the basis for: 1) registration to pre-operative or intra-operative volumes, and 2) reconstruction of anatomic surfaces. Prior work has demonstrated that these problems are both solvable. The project will combine the efforts of an experienced team consisting of engineering and clinical faculty, and will focus on translation of the research to clinically relevant data. The methodology of the project will be to develop and validate algorithms extensively on cadaver models with the goal of achieving 0.5 mm accuracy for both registration and surface reconstruction. Once these goals are achieved, the algorithms will be assessed on patient data acquired during FESS procedures. Although aimed at FESS, the proposed methods are widely applicable to other areas of endoscopy and laparoscopy.