Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

The purpose of this work was to explore the use of image registration-derived variables associated with computed tomographic (CT) imaging of the lung acquired at multiple volumes. As an evaluation of the utility of such an imaging approach, we explored two groups at the extremes of population ranging from normal subjects to severe asthmatics. A mass-preserving image registration technique was employed to match CT images at total lung capacity (TLC) and functional residual capacity (FRC) for assessment of regional air volume change and lung deformation between the two states. Fourteen normal subjects and thirty severe asthmatics were analyzed via image registration-derived metrics together with their pulmonary function test (PFT) and CT-based air-trapping. Relative to the normal group, the severely asthmatic group demonstrated reduced air volume change (consistent with air trapping) and more isotropic deformation in the basal lung regions while demonstrating increased air volume change associated with increased anisotropic deformation in the apical lung regions. These differences were found despite the fact that both PFT-derived TLC and FRC in the two groups were nearly 100% of predicted values. Data suggest that reduced basal-lung air volume change in severe asthmatics was compensated by increased apical-lung air volume change and that relative increase in apical-lung air volume change in severe asthmatics was accompanied by enhanced anisotropic deformation. These data suggest that CT-based deformation, assessed via inspiration vs. expiration scans, provides a tool for distinguishing differences in lung mechanics when applied to the extreme ends of a population range.

Relationships between structural and functional variables in asthmatic lungs at local and global (or lobar) levels remain to be discovered. This study aims to investigate local alterations of structural variables [bifurcation angle, circularity, airway wall thickness (WT), and hydraulic diameter ( D_h)] in asthmatic subjects, and their correlations with other imaging and pulmonary function test-based global and lobar metrics, including lung shape, air-trapping, regional volume change, and more. Sixty-one healthy subjects, and 67 nonsevere and 67 severe asthmatic subjects were studied. The structural variables were derived from computed tomography images at total lung capacity (TLC). Air-trapping was measured at functional residual capacity, and regional volume change (derived from image registration) was measured between functional residual capacity and TLC. The tracheal diameter and WT predicted by 61 healthy subjects were used to normalize the D_hand WT. New normalization schemes allowed for the dissociation of luminal narrowing and wall thickening effects. In severe asthmatic subjects, the alteration of bifurcation angle was found to be correlated with a global lung shape at TLC, and circularity was significantly decreased in the right main bronchus. While normalized WT increased especially in the upper lobes of severe asthmatic subjects, normalized D_hdecreased in the lower lobes. Among local structural variables, normalized D_hwas the most representative variable, because it was significantly correlated with alterations of functional variables, including pulmonary function test's data. In conclusion, understanding multiscale phenomena may help to provide guidance in the search for potential imaging-based phenotypes for the development and outcomes assessment of therapeutic intervention.

This study aims to assess regional ventilation, nonlinearity, and hysteresis of human lungs during dynamic breathing via image registration of four-dimensional computed tomography (4D-CT) scans. Six healthy adult humans were studied by spiral multidetector-row CT during controlled tidal breathing as well as during total lung capacity and functional residual capacity breath holds. Static images were utilized to contrast static vs. dynamic (deep vs. tidal) breathing. A rolling-seal piston system was employed to maintain consistent tidal breathing during 4D-CT spiral image acquisition, providing required between-breath consistency for physiologically meaningful reconstructed respiratory motion. Registration-derived variables including local air volume and anisotropic deformation index (ADI, an indicator of preferential deformation in response to local force) were employed to assess regional ventilation and lung deformation. Lobar distributions of air volume change during tidal breathing were correlated with those of deep breathing ( R² ≈ 0.84). Small discrepancies between tidal and deep breathing were shown to be likely due to different distributions of air volume change in the left and the right lungs. We also demonstrated an asymmetric characteristic of flow rate between inhalation and exhalation. With ADI, we were able to quantify nonlinearity and hysteresis of lung deformation that can only be captured in dynamic images. Nonlinearity quantified by ADI is greater during inhalation, and it is stronger in the lower lobes ( P < 0.05). Lung hysteresis estimated by the difference of ADI between inhalation and exhalation is more significant in the right lungs than that in the left lungs.

Lung air trapping is estimated via quantitative computed tomography (CT) using density threshold-based measures on an expiration scan. However, the effects of scanner differences and imaging protocol adherence on quantitative assessment are known to be problematic. This study investigates the effects of protocol differences, such as using different CT scanners and breath-hold coaches in a multicenter asthmatic study, and proposes new methods that can adjust intersite and intersubject variations. CT images of 50 healthy subjects and 42 nonsevere and 52 severe asthmatics at total lung capacity (TLC) and functional residual capacity (FRC) were acquired using three different scanners and two different coaching methods at three institutions. A fraction threshold-based approach based on the corrected Hounsfield unit of air with tracheal density was applied to quantify air trapping at FRC. The new air-trapping method was enhanced by adding a lung-shaped metric at TLC and the lobar ratio of air-volume change between TLC and FRC. The fraction-based air-trapping method is able to collapse air-trapping data of respective populations into distinct regression lines. Relative to a constant value-based clustering scheme, the slope-based clustering scheme shows the improved performance and reduced misclassification rate of healthy subjects. Furthermore, both lung shape and air-volume change are found to be discriminant variables for differentiating among three populations of healthy subjects and nonsevere and severe asthmatics. In conjunction with the lung shape and air-volume change, the fraction-based measure of air trapping enables differentiation of severe asthmatics from nonsevere asthmatics and nonsevere asthmatics from healthy subjects, critical for the development and evaluation of new therapeutic interventions.