Enhanced Navigation for Endoscopic Sinus Surgery Through Video Analysis

描述(由申请人提供)：该项目将为功能性内窥镜鼻窦手术(FESS)提供新的注册和可视化工具，使用广泛可用的高清晰度内窥镜视频。这些工具将为外科医生提供更高的精确度导航精度，并将使精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。这些算法将根据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。目的2：发展从内窥镜图像估计表面形状的方法。目的3：对患者数据进行基于视频CT导航的对比评价。目的4：评估术中表面数据估计的准确性和可靠性。改进导航的意义是1)通过减少潜在的并发症和辐射暴露来提高患者的安全性和预后，以及2)通过改善临床工作流程和术中可视化的清晰度来降低成本。在美国，据估计，每年进行的鼻窦手术超过20万例。所有这些都是在内窥镜引导下进行的，大部分可以或可以使用手术导航。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。提出的方法的创新之处在于使用内窥镜本身的图像作为基础：1)配准到术前或术中体积，以及2)解剖表面重建。前人的工作已经证明，这两个问题都是可解的。该项目将结合由工程学和临床教师组成的经验丰富的团队的努力，并将专注于将研究转化为临床相关数据。该项目的方法论将是开发和 在身体模型上广泛验证算法，目标是实现0.5 mm的配准和表面重建精度。一旦实现了这些目标，这些算法将在FESS过程中获得的患者数据上进行评估。虽然所提出的方法是针对FESS的，但也广泛适用于其他领域的内窥镜和腹腔镜检查。

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.