CT理论与应用研究2026，Vol.35Issue(1)：58-66,9.DOI:10.15953/j.ctta.2025.270
深度学习重建算法在60 kVp超低管电压CT成像中的定量精度与噪声抑制性能:模体研究
Quantitative Precision and Noise Reduction Efficacy of Deep Learning Reconstruction Algorithms in 60-kVp Ultra-low Tube Voltage Computed Tomography:A Phantom Study
曹博宣 1曾栋 1边兆英 1胡志 2刘恩 3崔骐 3文戈 2周建伟 2马建华 4王昊1
作者信息
- 1. 南方医科大学生物医学工程学院,广州 510515
- 2. 南方医科大学南方医院 放射科,广州 510515||南方医科大学南方医院 影像中心,广州 510515
- 3. 东软医疗系统股份有限公司,沈阳 110167
- 4. 南方医科大学生物医学工程学院,广州 510515||西安交通大学生命科学技术学院,西安 710049
- 折叠
摘要
Abstract
In this study,we systematically evaluated the iodine quantification accuracy and image noise suppression capabilities of a deep learning reconstruction algorithm(ClearInfinity,CI)under 60 kVp ultra-low tube voltage computed tomography(CT)conditions,comparing it with filtered backprojection(FBP)and hybrid iterative reconstruction(ClearView,CV).A CT performance phantom containing inserts with varying iodine concentrations(40,28,22,12,6,3,and 2 mg/mL)was scanned six times(60 kVp,386 mA)using a NeuViz Epoch Elite CT scanner.Images were reconstructed using FBP,CV(at 20%,40%,60%,and 80%intensities),and CI(at equal intensity).CT values,image noise(standard deviation SD),and coefficients of variation(cv)of the iodine inserts were measured.Absolute percentage bias(APB)and contrast-to-noise ratio(CNR)were calculated.Results show that CI achieved optimal quantitative accuracy at 40%reconstruction intensity and provided the strongest noise reduction at 80%,with a maximum SD reduction of up to 79.59%.At all intensity levels,CI significantly outperformed CV and FBP in terms of APB,noise suppression(especially at low iodine concentrations),measurement stability,and CNR.These findings confirm that CI is an effective solution for producing low-noise,low-bias,and highly stable images in ultra-low-dose CT.关键词
低剂量CT/图像噪声/变异系数/深度学习重建Key words
low-dose CT/image noise/coefficient of variation/deep learning reconstruction分类
信息技术与安全科学引用本文复制引用
曹博宣,曾栋,边兆英,胡志,刘恩,崔骐,文戈,周建伟,马建华,王昊..深度学习重建算法在60 kVp超低管电压CT成像中的定量精度与噪声抑制性能:模体研究[J].CT理论与应用研究,2026,35(1):58-66,9.
