Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands. keelinm@ gmail.com
Med Phys. 2012 Mar;39(3):1650-62. doi: 10.1118/1.3687891.
To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline and its results; (b) verify that the quantitative, regional ventilation measurements acquired through CT are meaningful for pulmonary function analysis; (c) identify the most effective of the calculated measurements in predicting pulmonary function; and (d) demonstrate the potential of the system to deliver clinically important information not available through conventional spirometry.
A pipeline of automatic segmentation and registration techniques is presented and demonstrated on a database of 216 subjects well distributed over the various stages of COPD (chronic obstructive pulmonary disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze the accuracy of the automatic methods. Quantitative measures representing ventilation are computed at every image voxel and analyzed to provide information about pulmonary function, both globally and on a regional basis. These CT derived measurements are correlated with results from spirometry tests and used as features in a kNN classifier to assign COPD global initiative for obstructive lung disease (GOLD) stage.
The steps of anatomical segmentation (of lungs, lobes, and vessels) and registration in the workflow were shown to perform very well on an individual basis. All CT-derived measures were found to have good correlation with spirometry results, with several having correlation coefficients, r, in the range of 0.85-0.90. The best performing kNN classifier succeeded in classifying 67% of subjects into the correct COPD GOLD stage, with a further 29% assigned to a class neighboring the correct one.
Pulmonary function information can be obtained from thoracic CT scans using the automatic pipeline described in this work. This preliminary demonstration of the system already highlights a number of points of clinical importance such as the fact that an inspiration scan alone is not optimal for predicting pulmonary function. It also permits measurement of ventilation on a per lobe basis which reveals, for example, that the condition of the lower lobes contributes most to the pulmonary function of the subject. It is expected that this type of regional analysis will be instrumental in advancing the understanding of multiple pulmonary diseases in the future.
分析使用完全自动技术从分别在屏气吸气和呼气时采集的一对胸部 CT 扫描中获取的肺功能。确定以下研究目标：(a) 描述和系统地分析处理管道及其结果；(b) 验证通过 CT 获得的定量、区域性通气测量值对肺功能分析有意义；(c) 确定计算出的测量值中最有效的预测肺功能的测量值；(d) 证明该系统有潜力提供通过传统肺活量计无法获得的临床重要信息。
提出了一个自动分割和配准技术的管道，并在一个分布在 COPD（慢性阻塞性肺疾病）各个阶段的 216 个受试者的数据库上进行了演示。在两次扫描中自动分割肺、裂、气道、叶和血管，并使用完全自动的非刚性配准算法将呼气扫描与吸气扫描配准。由专家观察者检查和评分分割和配准，以分析自动方法的准确性。在每个图像体素上计算表示通气的定量测量值，并进行分析，以提供有关全局和区域基础的肺功能的信息。这些 CT 衍生的测量值与肺活量计测试的结果相关联，并用作 kNN 分类器中的特征，以分配 COPD 全球倡议肺疾病（GOLD）阶段。
工作流程中的解剖分割（肺、叶和血管）和配准步骤在个体基础上表现非常出色。所有 CT 衍生的测量值都与肺活量计结果有很好的相关性，其中几个相关性系数 r 在 0.85-0.90 范围内。表现最好的 kNN 分类器成功地将 67%的受试者正确分类到 COPD GOLD 阶段，另有 29%被分配到一个接近正确的类别。
可以使用本文描述的自动管道从胸部 CT 扫描中获取肺功能信息。该系统的初步演示已经突出了许多临床重要的要点，例如仅吸气扫描对于预测肺功能不是最佳的。它还允许基于每个叶测量通气，这表明例如，下叶的状况对受试者的肺功能贡献最大。预计这种区域分析将有助于未来对多种肺部疾病的理解。
