Clinical Implementation of Inspiratory-Expiratory Chest CT: Defining Quality Criteria for Diagnostic Quality and Detection of Concurrent FEV1 Decline following Lung Transplantation.

Purpose To define criteria for evaluating the diagnostic adequacy of expiratory CT and use these criteria to evaluate how expiratory quality affects the performance of quantitative air trapping in predicting the presence and progression of chronic lung allograft dysfunction (CLAD). Materials and Methods Consecutive post-lung transplantation inspiratory-expiratory chest CT scans acquired at the authors' institution between March 2020 and November 2023 were retrospectively evaluated for diagnostic adequacy by grading the tracheal morphology on expiratory CT scans and comparing CT lung volume measurements with spirometry data. Lung volumes and voxelwise air trapping were measured using a deep learning algorithm. Air trapping was compared against changes in spirometry data at baseline and follow-up using Pearson correlation and receiver operating characteristic curve analysis. Results A total of 603 inspiratory-expiratory chest CT scans in 192 patients who underwent lung transplantation (mean age, 57.2 years ± 13.3 [SD]); 121 male patients) were evaluated. Tracheal morphology was identified as predominantly convex on 29% (175 of 613) of the CT scans, resulting in an overestimation of the expiratory volume in these studies. The correlation of the lung volume measurements between CT and spirometry improved with tracheal concavity. A baseline air trapping level of 50% had 82.6% specificity and 34.0% sensitivity for diagnosing CLAD on studies with predominantly concave or flat morphology and a volume change of 1.0 L or greater. A 20% increase in air trapping resulted in 92.1% specificity and 20.0% sensitivity for a concurrent 10% decline in the forced expiratory volume in 1 second (FEV1). Conclusion Tracheal morphology was used to assess the diagnostic adequacy of expiratory phase CT. Increased air trapping was highly specific, but not very sensitive, for predicting an FEV1 decline and helped in the diagnosis and monitoring of CLAD progression. Keywords: CT, CT-Quantitative, Pulmonary, Lung, Physiological Studies, QA/QC, Transplantation, Technology Assessment, Quality Assurance, Artificial Intelligence, Air Trapping, Bronchiolitis Obliterans, Lung Transplant Supplemental material is available for this article. © RSNA, 2025.