Artificial intelligence iterative reconstruction of CT images:Phantom experiment
10.13929/j.issn.1003-3289.2025.04.011
- VernacularTitle:深度学习全模型迭代算法用于重建CT图像:体模实验
- Author:
Wenjing CAO
1
;
Haohua SUN
1
;
Liyi ZHAO
1
;
Xiang LI
1
;
Guotao QUAN
1
Author Information
1. 上海联影医疗科技股份有限公司,上海 201800
- Publication Type:Journal Article
- Keywords:
tomography,X-ray computed;
image processing,computer-assisted;
deep learning;
phantoms,imaging
- From:
Chinese Journal of Medical Imaging Technology
2025;41(4):557-562
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of artificial intelligence iterative reconstruction(AIIR)for reconstruction CT images of phantoms.Methods AIIR was developed through combining model-based iterative reconstruction(MBIR)with deep learning(DL)techniques.CT scanning of CCT MITA IQ phantom,CT ACR 464 phantom,Catphan 700 phantom,disc stack phantom and CT PBU-60 whole body phantom were performed,and the images were reconstructed with conventional algorithms like filtered back projection(FBP)and KARL 3D iterative reconstruction,as well as AIIR,respectively.Then the noise,X-ray dosage,as well as low contrast resolution,high contrast spatial resolution,cone-beam artifacts and streaking artifacts of various reconstructed images were compared.Results Compared to CT images reconstructed with conventional algorithms,those reconstructed with AIIR showed 61.74％—99.76％ reduction of image noise and 60.00％—90.00％ reduction of X-ray dosage,while increased image low contrast resolution to 1.99-4.86 times and high contrast spatial resolution to 1.55-2.57 times.Additionally,AIIR significantly reduced cone-beam artifacts and streaking artifacts.Conclusion AIIR showed obvious advantages for reconstruction CT images of phantoms compared with conventional algorithms.
