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

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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: /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.

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