Impact of CT deep learning image reconstruction can reduce radiation dose and improve image quality:based on phantom study
FAN Lihua
LI Ming
JIA Yongjun
HAN Dong
YU Yong
ZHENG Yunsong
WEI Wei
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.
Keywords：deep learning image reconstructionradiation doseimage qualityphantom
Publication Date：2025-09-20
Online Publishing Date：2025-10-22(First online date of this platform, not the publication date of the document)
Pages：7( 1064-1070 )