Application Value of Deep Learning Reconstruction in Low-dose Chest CT for COPD
ZHANG Xiaoyong
XUE Liwei
TANG Borong
LIN Qiong
YE Xiongxin
ZHENG Wanyi
LIN Xiaojuan
XIANG Ling
LIU Yuanfen
Abstract：Objective:To compare image quality between deep learning image reconstruction(DLIR)low-dose chest CT and iterative reconstruction(ASIR-V)standard-dose chest CT in patients with chronic obstructive pulmonary disease(COPD).Methods:A total of 106 patients were prospectively enrolled and underwent standard-dose(SD)and low-dose(LD)chest CT scans.The LD scans were reconstructed using ASIR-V(LD-AR)and three DLIR strength levels(LD-DL/DM/DH),whereas the SD scans were reconstructed using ASIR-V(SD-AR).Noise(standard deviation),signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)of the anatomical structures were measured or calculated.The subjective image quality was scored by radiologists.Results:Effective radiation dose was(4.0±1.37)mSv for the SD group and(1.14±0.47)mSv for the LD group.The LD-DLIR images exhibited lower noise,higher SNR,and higher CNR than the SD-AR images,with the LD-DH performing the best.Subjective scores indicated superior noise levels,anatomical clarity,and emphysema visualization in LD-DLIR images compared to SD-AR,with LD-DH receiving the highest score.Conclusion:DLIR significantly improved chest CT image quality in patients with COPD while reducing the radiation dose by 71.5%,with DLIR-H providing optimal performance.
Keywords：deep learning image reconstructionradiation dosechronic obstructive pulmonary diseaseimage qualityiterative reconstruction
Publication Date：2026-01-31
Online Publishing Date：2026-03-06(First online date of this platform, not the publication date of the document)
Pages：8( 94-101 )