Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, 6525 GA, The Netherlands.
Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands.
Med Phys. 2017 Jul;44(7):3594-3603. doi: 10.1002/mp.12274. Epub 2017 May 22.
To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation.
Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available.
Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance.
The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD.
提出一种自动量化呼吸过程中气管形态变化的方法,并研究在添加 CT 肺气肿、气道壁厚度、空气滞留和通气测量值的情况下,该方法对气流受限的影响。
由于气管异常通常是局部发生的,因此提出了一种方法,该方法基于自动计算的矢状面和冠状面长度,自动确定最异常的气管段。在这个最异常的部分,使用从气管中心发出的四个等角射线来对气管形态进行编码,并将这些射线的归一化长度用作分类方案中的特征。因此,气管测量值除了肺气肿、空气滞留和通气外,还被用作分类为 GOLD 阶段的输入。使用分布在所有 GOLD 阶段的 200 名受试者的数据库,通过 k 最近邻算法来评估分类。在两个实验环境中评估性能:(a)仅获取吸气扫描时;(b)吸气和呼气扫描都可用时。
仅在吸气 CT 扫描时,测量气管形状仅为肺气肿测量值提供补充信息。在吸气设置中表现最好的一组是肺气肿和支气管测量值的组合。在吸气-呼气设置中表现最好的特征集包括肺气肿、通气、空气滞留和气管测量值。吸气和吸气-呼气设置的性能相似。
本研究中提出的全自动系统在吸气和呼气 CT 上提供了气管形态的信息。添加气管形态特征可提高基于 CT 的肺气肿和空气滞留测量值对 COPD 患者进行 GOLD 分期的能力,并且在研究 COPD 的不同方面时可能具有相关性。