Hierarchical Organ-Aware Total-Body Standard-Dose PET Reconstruction From Low-Dose PET and CT Images
Author:
Affiliation:
1. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
2. School of Information Science and Technology, ShanghaiTech University, Shanghai, China
Funder
National Natural Science Foundation of China
Science and Technology Commission of Shanghai Municipality
Key Research and Development Program of Guangdong Province, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software
Link
http://xplorestaging.ieee.org/ielx7/5962385/6104215/10121706.pdf?arnumber=10121706
Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review;BMC Medical Imaging;2024-09-11
2. IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement;European Journal of Nuclear Medicine and Molecular Imaging;2024-07-23
3. Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis;IEEE Transactions on Medical Imaging;2024-05
4. Clinical Implementation of Total-Body PET in China;Journal of Nuclear Medicine;2024-05
5. Deep learning-based PET image denoising and reconstruction: a review;Radiological Physics and Technology;2024-02-06
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