医学
定量评估
深度学习
人工智能
肺病
放射科
迭代重建
算法
计算机科学
计算机视觉
模式识别(心理学)
图像处理
核医学
图像质量
图像(数学)
重建算法
医学影像学
计算机断层摄影术
定量分析(化学)
作者
Liwei Xue,Qiong Lin,Xiongxin Ye,Xiaoyong Zhang,B.X. Tang,Xiaojuan Lin,Wanyi Zheng,Yunjing Xue,Yuanfen Liu
摘要
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. Keywords: chronic obstructive pulmonary disease, chest computed tomography, deep learning image reconstruction, low dose, GOLD grading
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