3D IMAGE ANAL ALGORITHMS FOR AUTOMATIC COMP OF GROUND-GLASS OPACITY LUNG TUMOR

肺肿瘤磨玻璃影自动计算的 3D 图像分析算法

• 批准号: 7960176
• 负责人: CHANDRA KAMBHAMETTU
• 金额: $ 5.46万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7960176
• 关键词: Algorithms; Anus; Appearance; Area; Caliber; Cancer Center; Caring; Computer Retrieval of Information on Scientific Projects Database; Data; Delaware; Excision; Funding; Glass; Goals; Grant; Image; Image Analysis; Imagery; Institution; Lung Neoplasms; Lung nodule; Measurement; Modeling; Motion; Nodule; Outcome Measure; Patients; Protocols documentation; Pulmonary vessels; Quantitative Evaluations; Research; Research Ethics Committees; Research Personnel; Resolution; Resources; Series; Source; Surface; System; Thick; Three-Dimensional Image; Time; Training; United States National Institutes of Health; base; candidate selection; data acquisition; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 3D Image Analysis algorithms for Automatic computation of ground-glass opacity (GGO) of lung tumors PI:Prof. Chandra Kambhamettu Ground-glass refers to the HRCT appearance of a hazy opacity that does not obscure the associated pulmonary vessels. This appearance results from parenchymal abnormalities that are below the spatial resolution of HRCT. GGO area of a nodule can be observed in the HRCT images by the appearance of image pixels, where-in pixels are blurred and have less opacity compared to other "thick/opaque" nodule pixels. CT data is obtained from the Helen F. Graham Cancer Center of Christiana Care. Data acquisition will be used absent all patient identifiers and according to IRB approved protocol. Aim 1: Time-series alignment of lung and nodule(s). The goal is to obtain global rigid, global nonrigid, local nonrigid alignment parameters between the CT lung and tumor data obtained at different time instances. We segment the lung and nodules, use Extended-Superquadrics to model, and then perform spline based nonrigid motion estimation between different time instance data. Aim 2: GGO ratio estimation and 3D measurement of lung nodule. GGO pixels are identified using proposed training system for each nodule. Measurements are then performed consisting of the diameter, surface area of coverage, volume and GGO coverage, and visualized. Study questions include the feasibility of proposed image analysis and visualization tools for assistance in the radiologic analysis of high-resolution computed tomographic images in order to automatically detect, extract, time-align lung nodules, and classify them with ground-glass opacity (GGO) ratios. Outcome measures include the qualitative, quantitative evaluation of the developed system in assisting the selection of candidates for curative limited resection.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 中心，不一定是研究者的机构。 用于自动计算肺部肿瘤毛玻璃混浊 (GGO) 的 3D 图像分析算法 PI：Prof.钱德拉·坎巴梅图 毛玻璃是指 HRCT 中出现的模糊不透明现象，不会遮挡图像 相关肺血管。这种外观是由实质异常引起的 低于 HRCT 的空间分辨率。可以观察到结节的 GGO 区域 在HRCT图像中，通过图像像素的外观，其中像素是模糊的 与其他“厚/不透明”结节像素相比，不透明度较低。 CT 数据来自 Christiana Care 的 Helen F. Graham 癌症中心。 将在没有所有患者标识符的情况下使用数据采集，并根据 符合 IRB 批准的协议。 目标 1：肺和结节的时间序列对齐。目标是获得全局刚性， CT 肺部和肿瘤数据之间的全局非刚性、局部非刚性对准参数 在不同的时间实例获得。我们对肺部和结节进行分割，使用扩展超二次曲面进行建模，然后在不同时间实例数据之间执行基于样条的非刚性运动估计。 目标 2：肺结节的 GGO 比率估计和 3D 测量。使用针对每个结节提出的训练系统来识别 GGO 像素。然后进行直径、覆盖表面积、体积和 GGO 覆盖范围的测量，并进行可视化。 研究问题包括所提出的图像分析和可视化工具的可行性，以辅助高分辨率计算机断层扫描图像的放射学分析，以便自动检测、提取、时间对齐肺结节，并将其分类 毛玻璃混浊 (GGO) 比率。结果测量包括对已开发系统的定性、定量评估，以协助选择治疗性有限切除的候选者。