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Background: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression., Objective: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques., Materials and Methods: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model., Results: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach., Conclusion: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients., Competing Interests: We have the following interests. 1. This study was partly supported by Novartis Institutes for Biomedical Research, Cambridge, MA, USA, from which Terry E. Robinson received a research grant. 2. Charles R. Hatt is an employee and stock option holder at Imbio LLC. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing the data and materials.

Rationale and Objectives: The aim of this study was to assess variability in quantitative air trapping (QAT) measurements derived from spatially aligned expiration CT scans., Materials and Methods: Sixty-four paired CT examinations, from 16 school-age cystic fibrosis subjects examined at four separate time intervals, were used in this study. For each pair, visually inspected lobe segmentation maps were generated and expiration CT data were registered to the inspiration CT frame. Measurements of QAT, the percentage of voxels on the expiration CT scan below a set threshold were calculated for each lobe and whole-lung from the registered expiration CT and compared to the true values from the unregistered data., Results: A mathematical model, which simulates the effect of variable regions of lung deformation on QAT values calculated from aligned to those from unaligned data, showed the potential for large bias. Assessment of experimental QAT measurements using Bland-Altman plots corroborated the model simulations, demonstrating biases greater than 5% when QAT was approximately 40% of lung volume. These biases were removed when calculating QAT from aligned expiration CT data using the determinant of the Jacobian matrix. We found, by Dice coefficient analysis, good agreement between aligned expiration and inspiration segmentation maps for the whole-lung and all but one lobe (Dice coefficient > 0.9), with only the lingula generating a value below 0.9 (mean and standard deviation of 0.85 ± 0.06)., Conclusion: The subtle and predictable variability in corrected QAT observed in this study suggests that image registration is reliable in preserving the accuracy of the quantitative metrics., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)