Spatial adaptive and transformer fusion network (STFNet) for low‐count PET blind denoising with MRI-Reference-Cited by-同舟云学术

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Spatial adaptive and transformer fusion network (STFNet) for low‐count PET blind denoising with MRI

Published:2021-12-10 Issue:1 Volume:49 Page:343-356
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Zhang Lipei¹²,Xiao Zizheng³,Zhou Chao³,Yuan Jianmin⁴,He Qiang⁴,Yang Yongfeng¹²,Liu Xin¹²,Liang Dong¹²,Zheng Hairong¹²,Fan Wei³,Zhang Xu³,Hu Zhanli¹²

Affiliation:

1. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

2. Chinese Academy of Sciences Key Laboratory of Health Informatics Shenzhen China

3. Department of Nuclear Medicine Sun Yat‐sen University Cancer Center Guangzhou China

4. Central Research Institute Shanghai United Imaging Healthcare Shanghai China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15368

Reference39 articles.

1. PET/MR in Dementia and Other Neurodegenerative Diseases

2. Ultra-low-dose 18F-FDG brain PET/MR denoising using deep learning and multi-contrast information

3. Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning

4. Task Group 174 Report: Utilization of [ 18 F]Fluorodeoxyglucose Positron Emission Tomography ([ 18 F]FDG‐PET) in Radiation Therapy

5. Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy

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