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

Abstract

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 FEV1/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.