影像科学与光化学2026，Vol.44Issue(4)：144-153,10.DOI:10.7517/issn.1674-0475.2026.04.20
人工智能辅助下儿童胸部CT低剂量扫描参数自适应优化及多中心临床应用研究
Adaptive Optimization of Low-dose Chest CT Scanning Parameters for Chil-dren with Artificial Intelligence Assistance and Its Multi-center Clinical Application
摘要
Abstract
Objective:This study aims to establish an artificial intelligence-based adaptive optimization strategy for pediatric chest CT scanning parameters using an MLP-CNN hybrid architecture CT adaptive parameter optimization(CT-APO)system.The system dynamically optimizes parameters including kVp,mA,rotation time,and collimator width to maximize radiation dose reduction while maintaining image quality,with validation through multi-center clinical applications.Methods:A prospective,multi-center,randomized controlled study was conducted,enrolling 2 576 pediatric patients aged 0～16 years who underwent chest CT examinations from January 2023 to September 2024.Patients were stratified by age and weight,then randomly assigned concealment to either the control group(n=1 283)or the study group(CT-APO system,n=1 293)using convolutional neural network-based adaptive tube voltage selection technology.Primary evaluation metrics included volume CT dose index(CTDIvol),dose-length product(DLP),effective radiation dose(ED),signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),and subjective image quality scores on a 5-point scale.Results:The average tube voltage in the study group was significantly lower than that of the control group(P＜0.001).Overall,the CTDIvol,DLP,and estimated effective dose in the study group were reduced compared to the control group(P＜0.001).There were no statistically significant differences between the study group and the control group in objective image quality parameters(image noise,SNR,CNR)or subjective image quality scores(P＞0.05).For lesions such as pulmonary parenchymal nodules(≤5 mm),bronchial wall thickening,and interstitial lung changes,the detection rates in both groups showed no statistically significant differences(P＞0.05).Conclusion:The artificial intelligence-based adaptive optimization system for pediatric chest CT scanning parameters can significantly reduce radiation dose while maintaining diagnostic image quality and clinical diagnostic efficacy.The system demonstrates excellent cross-platform applicability and adaptive learning capabilities,making it particularly suitable for infants and low-weight patient populations.This technology holds promise for optimizing pediatric CT examination practices,reducing radiation risks,and improving medical safety.关键词
儿童胸部CT/人工智能/辐射剂量/图像质量/自适应参数优化/深度学习Key words
pediatric chest CT/artificial intelligence/radiation dose/image quality/adaptive parameter optimization/deep learning分类
医药卫生引用本文复制引用
冉崇荣,邓丽佳,卢晓玉,雷敏..人工智能辅助下儿童胸部CT低剂量扫描参数自适应优化及多中心临床应用研究[J].影像科学与光化学,2026,44(4):144-153,10.基金项目
四川省自然科学基金项目(2025ZNSFSC1772) （2025ZNSFSC1772）
绵阳市妇幼保健院.绵阳市儿童医院2024年院级科研项目(2024-KY-012). （2024-KY-012）
