Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space-Reference-Cited by-同舟云学术

Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space

Published:2024-06 Issue:6 Volume:28 Page:3583-3596
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Li Zhaotong¹^ORCID,Li Sha²^ORCID,Zhang Zeru³^ORCID,Wang Fei²^ORCID,Wu Fengliang⁴^ORCID,Gao Song³^ORCID

Affiliation:

1. Laboratory of Digital Medicine, Department of Medical Informatics, Medical School of Nantong University, Nantong, China

2. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China

3. Institute of Medical Technology, Peking University Health Science Center, Beijing, China

4. Beijing Key Laboratory of Spinal Disease Research, Engineering Research Center of Bone and Joint Precision Medicine, Department of Orthopedics, Peking University Third Hospital, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science Research of Jiangsu Higher Education Institutions of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10550931/10412627.pdf?arnumber=10412627

Reference60 articles.

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4. Undersampled Multi-Contrast MRI Reconstruction Based on Double-Domain Generative Adversarial Network

5. Common artefacts encountered on images acquired with combined compressed sensing and SENSE

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