Robust Detection of Early Small Airway Disease

项目摘要 慢性阻塞性肺疾病(COPD)是导致发病率和死亡率的主要原因。尽管 吸烟人数下降，慢性阻塞性肺病死亡率继续上升，现已成为#年的第三大死因 美国。慢性阻塞性肺疾病的慢性气流受限是由小气道疾病和 实质破坏(肺气肿)。最近的研究表明，小气道的核心作用 破坏在COPD发病机制中的作用。这一证据激起了人们对体内评估的兴趣 小气道疾病在疾病的早期发作。小气道疾病的早期识别可能 导致更好的患者诊断，早期治疗干预，并提供更敏感的标记物来阐明 这种疾病的发病机制及其生物分子基础可能会为亟需的药物发现提供信息。 计算机断层扫描(CT)是一种已被证明是有效的定量的成像方式 实质上的破坏。然而，从小范围获得直接测量所需的成像分辨率 航空公司已经超出了CT扫描的范围。一种称为参数响应映射(PRM)的最新技术 建议通过将吸气相和肺气肿相匹配来区分小气道疾病引起的气体滞留和肺气肿 呼气CT扫描和应用密度阈值鉴别功能性小气道疾病(FSAD)和 肺气肿。 尽管PRM取得了成功，但它显示出一些限制，这些限制排除了准确性、稳健性和 对其早期疾病结果的解释：CT密度值高度依赖于采集参数 (剂量、重建核、身体大小变化)，引入依赖于对象和扫描仪 混血儿。尽管临床试验使用明确定义的采集方案和模校准采集， 偏差和噪声模式仍然取决于受试者。特别是，许多使用PRM的研究都使用了 在不同剂量水平下获得的吸气和呼气图像。 该项目将充分利用我们在图像驱动统计方面的最新发展 表征组织减少有害影响PRM的主要因素。和谐共处 统计框架中的CT扫描将在横断面和纵向实现稳健的PRM指标 学习。统计特征还将导致定义自适应阈值来检测肺气肿 以及FSAD最小化第一类和第二类错误权衡。我们将验证协调PRM指标的稳健性 并通过研究其与肺功能的关系来研究其临床意义。我们的 初步数据显示，我们可以获得协调的图像，将扫描仪和受试者- 依赖的混血儿。我们在CT图像中的组织表征也证明了它适合于提供 定义稳健自适应阈值的统计框架。总而言之，这项研究提出的目的是 AWARD将充分利用通过COPDgene研究获得的全面数据集。

Project Summary Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality. Despite declines in smoking, mortality from COPD continues to increase and is now the 3rd leading cause of death in the US. The chronic airflow limitation of COPD is caused by a mixture of small airway disease and parenchymal destruction (emphysema). Recent studies have suggested a central role of small airway destruction in the pathogenesis of COPD. This evidence has sparked the interest in in-vivo assessment of small airway disease overall at the early onset of the disease. Early identification of small airway disease could lead to better patient diagnosis, early therapeutic intervention and provide more sensitive markers to elucidate the pathogenesis of the disease and its biomolecular basis that could inform much-needed drug discovery. Computed Tomography (CT) is an imaging modality that has proven to be effective in the quantification of parenchymal destruction. However, the imaging resolution required to obtain direct measures from small airways is beyond the limits of CT scans. A recent technique called parametric response mapping (PRM) proposes to distinguish gas trapping due to small airway disease from emphysema by matching inspiratory and expiratory CT scans and applying density thresholds to distinguish functional small airway disease (FSAD) and emphysema. Despite its success, the PRM shows some limitations that are precluding the accuracy, robustness and interpretation of its results in early disease: The CT density values highly depend on acquisition parameters (dosage, reconstruction kernel, changes in body size) that introduce subject- and scanner-dependent confounders. Although clinical trials use well defined acquisition protocols and phantom-calibrated acquisitions, the biases and noise patterns still are subject-dependent. In particular, many studies using PRM employ inspiratory and expiratory images that are obtained at different dose levels. This project will take full advantage of our most recent developments in image-driven statistical characterization of tissues to reduce the harmful effects of the main factors affecting PRM. The harmonization of CT scans in a statistical framework will enable robust PRM metrics in cross-sectional and longitudinal studies. The statistical characterization will also lead to define adaptive thresholds to detect the emphysema and FSAD minimizing type I and II error trade-off. We will validate the robustness of harmonized PRM metrics in multiparametric acquisitions and study its clinical relevance by studying associations with lung function. Our preliminary data shows that we can obtain harmonized images that minimize the scanner and subject- dependent confounders. Our tissue characterization in CT images also has proved its suitability to provide a statistical framework to define robust adaptive thresholds. Together, the research proposed in the aims of this award will take full advantage of the comprehensive dataset available through the COPDGene study.