CT Assessment of Lung Fissures: Anatomy and Correlated Function

描述（由申请人提供）：肺部疾病在美国的主要死亡原因中排名第三。高分辨率MDCT的可用性使得能够以以前无法获得的细节对体内肺结构进行成像，从而激发了使用成像来表型肺异常以更好地理解肺解剖结构及其与肺功能的相关性的极大兴趣。这可能导致更早的检测和诊断以及可能更有效的个性化患者管理。有迹象表明，肺裂的完整性和构型可能与肺功能相关，并可能在个体发生特定肺部疾病（如COPD（慢性阻塞性肺病）和ILD（间质性肺病））的易感性中发挥作用。裂隙完整性也可能是决定肺炎等疾病进展的主要因素。然而，裂隙完整性和构型以及周围组织的主观评估通常由放射科医师和肺病科医师主观地完成，并且这些评估既耗时又容易出错。为了利用现有的先进CT成像技术，并提供一种非侵入性的方式来评估这些关系，我们建议开发自动化的计算机化方案，可视化和定量测量和分类肺裂的结构和完整性，然后评估相关性，如果有的话，建立肺功能的措施和差异之间的肺结构。为了实现这些目标，我们将（1）收集一个包含1000多个肺部CT检查的大型和多样化的CT数据库，这些检查涵盖了广泛的肺部疾病，（2）优化先前开发的用于肺裂检测和肺叶分割的原型计算机化方案，并测试其通用性，（3）开发用于计算裂隙形态和裂隙不完整性的综合指数的自动定量计算机化工具，（4）通过与三位有经验的放射科医生的主观结果进行比较来评估我们的计算机化方案，（5）使用统计分析来评估肺解剖与肺功能之间的相关性（如果有的话）。本项目的独特优势包括但不限于：（1）使用计算几何分析在三维几何空间中执行、分析和可视化肺裂/肺叶分析，并且结果对肺结构的解剖学知识具有最小的依赖性，以及（2）通过分析从肺叶上的高分辨率CT图像重建的肺结构可以非侵入性地研究肺功能，分叶或全肺。我们对初步研究的成功感到鼓舞，这些研究支持我们的做法的可行性。 公共卫生相关性：本项目旨在通过开发和测试客观、自动化计算机检测和分类方案，确定肺裂解剖结构（包括但不限于肺裂完整性和肺功能）之间的关系（如有）相关信息。长期目标是实现一种客观、稳健和一致的计算机化方法，以提高肺部疾病早期检测和分类（诊断）的准确性，并可能实现更好的患者管理，以及为准确的早期疗效评估提供有用的工具。

DESCRIPTION (provided by applicant): Pulmonary diseases rank third among the leading causes of death in the United States. The availability of high resolution MDCT makes it possible to image in-vivo lung structures in previously unattainable details, thus stimulating great interest of using imaging to phenotype lung abnormalities for better understanding of lung anatomy and its correlation to lung function. This may lead to earlier detection and diagnosis as well as possibly to more efficacious individualized patient management. There are indications that fissures integrity and configuration may be correlated with lung function and may play a role in the susceptibility of individuals to the development of specific lung diseases such as COPD (Chronic Obstructive Pulmonary Disease) and ILD (Interstitial Lung Disease). Fissure integrity may also be a primary factor in determining progression of diseases such as pneumonia. However, subjective assessments of fissures completeness and configuration as well as surrounding tissues are typically done subjectively by radiologists and pulmonologists and these assessments are both time consuming and error prone. To take advantages of available advanced CT imaging techniques and to provide a non-invasive way for assessing these relationships, we propose to develop automated computerized schemes to visualize and quantitatively measure and classify pulmonary fissures structure and completeness and then assess correlations, if any, between established lung function measures and differences in lung structures. To achieve these objectives, we will (1) assemble a large and diverse CT database of over 1000 lung CT examinations that cover a wide range of depicted lung diseases, (2) optimize a previously developed prototype computerized scheme for lung fissure detection and lobe segmentation and test its generalizeability, (3) develop an automated quantitative computerized tool for computing a summary index for fissure configuration and fissure incompleteness, (4) evaluate our computerized schemes by comparing it with the subjective results of three experienced radiologists, and (5) assess the correlation, if any, between lung anatomy and lung function using statistical analyses. The unique advantages of this project include but are not limited to: (1) pulmonary fissure/lobe analysis is performed, analyzed and visualized in three-dimensional geometric space using computational geometry analysis and as a result have minimum dependence on anatomical knowledge of lung structure, and (2) lung functions may be studied non-invasively by analyzing pulmonary structures reconstructed from high resolution CT images on either a lobe-by-lobe or a whole lung basis. We are encouraged by the success of our preliminary studies that supports the feasibility of our approach. PUBLIC HEALTH RELEVANCE: This project aims to ascertain information about the relationship, if any, between pulmonary fissures anatomy, including but not limited to fissure completeness and lung function through the development and testing of an objective, automated computerized detection and classification scheme. The long term goal is to enable an objective robust and consistent computerized approach to improve accuracy of early detection and classification (diagnosis) of lung disease and possibly to enable better management of patients as well as provide a useful tool for accurate early assessment of therapeutic efficacy.