ArticleInternational journal of chronic obstructive pulmonary disease2026
Deep Learning Image Reconstruction Algorithm for Quantitative Assessment of Low-Dose Biphasic Chest CT in Chronic Obstructive Pulmonary Disease.
Article in International journal of chronic obstructive pulmonary disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 FEV 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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