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    Investigating the Use of Generative Adversarial Networks-Based Deep Learning for Reducing Motion Artifacts in Cardiac Magnetic Resonance by Ma ZP, Zhu YM, Zhang XD, Zhao YX, Zheng W, Yuan SR, Li GY, Zhang TL

    Published 2025-02-01
    “…Ze-Peng Ma,1,2,* Yue-Ming Zhu,3,* Xiao-Dan Zhang,4 Yong-Xia Zhao,1 Wei Zheng,3 Shuang-Rui Yuan,1 Gao-Yang Li,1 Tian-Le Zhang1 1Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, People’s Republic of China; 2Hebei Key Laboratory of Precise Imaging of inflammation Tumors, Baoding, Hebei Province, 071000, People’s Republic of China; 3College of Electronic and Information Engineering, Hebei University, Baoding, Hebei Province, 071002, People’s Republic of China; 4Department of Ultrasound, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-Dan Zhang, Department of Ultrasound, Affiliated Hospital of Hebei University, No. 212 of Yuhua East Road, Lianchi District, Baoding, 071000, People’s Republic of China, Tel +86 17325535302, Email xiaodanzhangzxd@126.comObjective: To evaluate the effectiveness of deep learning technology based on generative adversarial networks (GANs) in reducing motion artifacts in cardiac magnetic resonance (CMR) cine sequences.Methods: The training and testing datasets consisted of 2000 and 200 pairs of clear and blurry images, respectively, acquired through simulated motion artifacts in CMR cine sequences. …”
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