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: 7720254
• 负责人: CHANDRA KAMBHAMETTU
• 金额: $ 4.41万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7720254
• 关键词: Algorithms; Anus; Appearance; Area; Blur; Caliber; Cancer Center; Caring; Computer Retrieval of Information on Scientific Projects Database; Data; 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; Three-Dimensional Imaging; Time; Training; United States National Institutes of Health; base; 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.

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