Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data-Reference-Cited by-同舟云学术

Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data

Published:2023 Issue: Volume:9 Page:448-458
ISSN:2333-9403
Container-title:IEEE Transactions on Computational Imaging
language:
Short-container-title:IEEE Trans. Comput. Imaging

Author:

Chen Dicheng¹^ORCID,Hu Wanqi¹,Liu Huiting¹,Zhou Yirong¹^ORCID,Qiu Tianyu¹^ORCID,Huang Yihui¹^ORCID,Wang Zi¹^ORCID,Lin Meijin²,Lin Liangjie³,Wu Zhigang³,Wang Jiazheng³,Chen Hao⁴,Chen Xi⁵,Yan Gen⁶,Guo Di⁷^ORCID,Lin Jianzhong⁸,Qu Xiaobo¹^ORCID

Affiliation:

1. Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China

2. Department of Applied Marine Physics & Engineering, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China

3. Philips, Beijing, China

4. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

5. Harvard Medical School, McLean Hospital, Belmont, MA, USA

6. Department of Radiology, Second Hospital Affiliated to Xiamen Medical College, Xiamen, China

7. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

8. Department of Radiology, Zhongshan Hospital affiliated to Xiamen University, Xiamen, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province of China

Health-Education Joint Research Project of Fujian Province

President Fund of Xiamen University

Nanqiang Outstanding Talent Program of Xiamen University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Mathematics,Computer Science Applications,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6745852/10038323/10103142.pdf?arnumber=10103142

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