Silent Zones of Lung Disease in COPD

慢性阻塞性肺疾病（COPD）是世界上第四大死亡原因， 美国，并与大量的呼吸道疾病的发病率。COPD的特征是肺量测定 由于肺实质（肺气肿）和气道的结构变化导致的气流阻塞。但 肺量测定诊断和CT上肺气肿的存在之间存在显著的不一致性。 吸气CT上的肺气肿定义为低密度区域<-950 Hounsfield单位（HU）。通过 通过图像配准，解剖匹配吸气和呼气CT扫描，我们推导出一个CT测量 称为肺变形的雅可比行列式（J），其是肺弹性的逐点测量。 呼吸时肺的扩张和收缩。我们假设基于CT的肺力学将使 识别根据传统CT密度标准显示正常但机械受损的区域 在呼吸过程中。我们将通过评估10，300名当前和以前的吸烟者来测试“沉默区”假设 入组COPD遗传流行病学（COPDGene）队列，具体目标如下。在目标1中， 我们将通过匹配吸气和呼气CT扫描来量化沉默区，并确定它们之间的关联 与肺功能、呼吸生活质量和功能容量有关。在目标2中，我们将使用6，284名受试者， 5年后完成了第二次COPDGene访问，以量化沉默区进展到 肺气肿区，并通过测试其相关性来确定沉默区的预后效用 FEV 1下降和死亡率。在目标3中，我们将开发一个深度卷积神经网络来识别Silent 区域直接来自吸气CT扫描，从而避免计算密集的图像匹配过程。 我将利用这一提议获得生物统计学、肺生理学、深度学习、 并行计算的大型医疗队列。该职业发展奖所创造的机会将 为我提供了一个明确的路径，以获得专业知识，并在COPD领域发展研究利基。 这项研究提案和职业发展计划的目标是通过积极的指导， 博士肺成像研究领域的领先专家、UAB肺成像实验室主任Surya Bhatt博士和 Arie Nakhmani，计算机视觉、图像配准和机器学习方法专家。的 拟议的研究将为我提供一套技能，以实现我的长期目标，一个独立的职业生涯， 转化研究，专注于COPD的医学成像和机器学习应用。

Project Summary: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and is associated with substantial respiratory morbidity. COPD is characterized by spirometric airflow obstruction due to structural changes in lung parenchyma (emphysema) and airways. However, there exists a marked discordance between spirometry diagnosis and presence of emphysema on CT. Emphysema on inspiratory CT is defined by low-density areas <-950 Hounsfield Units (HU). By anatomically matching inspiratory and expiratory CT scans through image registration, we derived a CT measure of lung elasticity termed the Jacobian determinant of lung deformation (J) which is a point-by-point measure of lung expansion and contraction during respiration. We hypothesize that the CT-based lung mechanics will enable identification of regions that appear normal per traditional CT density criteria but are mechanically compromised during respiration. We will test the “Silent Zones” hypothesis by evaluating 10,300 current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) cohort with the following specific aims. In Aim 1, we will quantify Silent Zones by matching inspiratory and expiratory CT scans and to determine their associations with lung function, respiratory quality of life and functional capacity. In Aim 2, we will use 6,284 subjects who completed a second COPDGene visit after 5-years to quantify the percentage of Silent Zones progressed into emphysematous areas and also to determine the prognostic utility of Silent Zones by testing their association with FEV1 decline and mortality. In Aim 3, we will develop a deep convolutional neural network to identify Silent Zones directly from inspiratory CT scans, thus avoiding the computationally intensive image matching process. I will utilize this proposal to acquire advanced training in biostatistics, lung physiology, deep learning, parallel computing for large medical cohorts. The opportunities created by this Career Development Award will provide me with a clearly delineated path to acquire expertise and develop a research niche in the field of COPD. The aims of this research proposal and career development plan are possible through the active mentorship of Dr. Surya Bhatt, a leading expert in lung imaging research and the Director of UAB Lung Imaging Lab and Dr. Arie Nakhmani, an expert in computer vision, image registration, and machine learning methodologies. The proposed study will provide me with the skill set to achieve my long-term goal of an independent career in translational research focusing on medical imaging and machine learning applications for COPD.