分子影像学杂志2025，Vol.48Issue(9)：1064-1070,7.DOI:10.12122/j.issn.1674-4500.2025.09.02
CT深度学习图像重建可降低辐射剂量和提高图像质量:基于体模研究
Impact of CT deep learning image reconstruction can reduce radiation dose and improve image quality:based on phantom study
摘要
Abstract
Objective To evaluate the potential of deep learning image reconstruction(DLIR)in improving image quality and reducing radiation dose by comparing the noise power spectrum,task-based transfer function and lesion detection capability.Methods The ACR464 phantom was scanned using GE Revolution APEX CT and eight different noise indices(NI=10,14,16,18,20,22,24,28)were set.The original data were subjected to image reconstruction using filtered back-projection(FBP),multi-model iterative reconstruction algorithms(ASiR-V)at 40%,ASiR-V at 60%,ASiR-V at 80%,and different levels of deep learning image reconstruction(DLIR-L,DLIR-M,DLIR-H)algorithms.The image quality was evaluated by using imQuest software to calculate the noise power spectrum(NPS),task-based transfer function(TTF),and detection capability index(d')of different reconstruction algorithms.Results Among all the reconstruction algorithms,the NPS peak of DLIR-H was the lowest.With the increase of noise index,both NPS and fav move towards low frequencies.The fav of DLIR-H(0.24-0.27 mm-1)was only 40%lower than that of ASiR-V(0.26-0.28 mm-1).The TTF50%value was not affected by the DLIR level.The TTF50%value was(37.44±10.85)%and(46.24±15.28)%higher than that of ASiR-V60%and 80%,respectively.The detection ability of both large and small features in deep learning image reconstruction was 40%higher than that of ASiR-V.When comparing the radiation doses with comparable lesions detection capabilities of 40%ASiR-V at NI=10 and DLIR-H,the radiation dose for small features decreased by approximately 76.48%,and that for large features decreased by approximately 72.59%.Conclusion Deep learning image reconstruction can not only reduce noise,improve spatial resolution and lesion detectibility without changing noise texture,but also has more powerful ability to reduce radiation dose than ASiR-V.关键词
深度学习图像重建/辐射剂量/图像质量/体模Key words
deep learning image reconstruction/radiation dose/image quality/phantom引用本文复制引用
樊丽华,李明,贾永军,韩冬,于勇,郑运松,魏伟..CT深度学习图像重建可降低辐射剂量和提高图像质量:基于体模研究[J].分子影像学杂志,2025,48(9):1064-1070,7.基金项目
陕西省重点研发计划项目(2024SF-YBXM-524) （2024SF-YBXM-524）
