Deep Learning Image Reconstruction Algorithm for Quantitative Assessment of Low-Dose Biphasic Chest CT in Chronic Obstructive Pulmonary Disease

Objective: To evaluate the impact of deep learning image reconstruction (DLIR) on quantitatively assessing emphysema, air trapping and small airway dysfunction in chronic obstructive pulmonary disease (COPD) using low-dose inspiratory–expiratory chest CT. Methods: Sixty-nine COPD patients underwent low-dose inspiratory-expiratory chest CT scans and pulmonary function tests (PFT) were prospectively enrolled. The CT images were reconstructed using 50% adaptive statistical iterative reconstruction (ASiR-V), DLIR-high (DLIR-H), medium (DLIR-M), and low (DLIR-L) strengths. The volumes and its percentages (relative to whole lung) characterizing emphysema, air trapping and small airway dysfunction were quantified on the inspiratory-expiratory CT scans. Results: The total dose-length product was 128.99 ± 39.00 mGy·cm. For all patients, emphysema parameters were lowest for DLIR-H and highest for ASiR-V; small airway dysfunction parameters were highest with DLIR-H and lowest with ASiR-V; air trapping parameters were lowest with ASiR-V; highest with DLIR-M. Emphysema parameters demonstrated moderate negative correlations with FEV 1 /FVC (r = – 0.570 to – 0.649, all p < 0.001). Air trapping and small airway dysfunction parameters showed weak negative correlations with MEF25%, MEF50%, and MEF75% (r = – 0.320 to – 0.381, all p < 0.001). When differentiating GOLD I–II from III–IV, all parameters showed AUC values ranging from 0.69 to 0.76, without statistically differences among reconstructions (DeLong’s test, p > 0.05), while the optimal thresholds varied across reconstructions. Conclusion: In low-dose inspiratory–expiratory chest CT, DLIR may alter the lung function-related CT parameters compared to ASiR-V, but does not affect their correlations with PFTs or their efficacies in GOLD grading.