Showing 241 - 260 results of 385 for search '"Radiology"', query time: 0.06s Refine Results
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    Predictors of biochemical and structural response to medical therapy in patients with active acromegaly following surgery: a real-world perspective by Maryam Rafieemanesh, Manizhe Ataee Kachuee, Ali Zare Mehrjardi, Alireza Khajavi, Mohammad Ghorbani, Mohammad Reza Mohajeri-Tehrani, Nahid Hashemi-Madani, Mohammad E. Khamseh

    Published 2025-02-01
    “…We aimed to explore the potential role of select clinical, biochemical, and radiological factors in predicting biochemical and structural responses to medical therapy in a real-world setting. …”
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    Evaluation and Management of Patients with Hematoma After Gynecologic and Obstetric Surgery by Bekir Kahveci, Mehmet Obut, Serhat Ege, Mete Sucu, Nurullah Peker, Osman Uzundere, Gaye Kahveci, Mehmet Sukru Budak

    Published 2021-04-01
    “…Specific treatment including surgery or interventional radiology is sometimes necessary. The aim of this study is to evaluate the cases of postoperative hematoma after gynecologic and obstetric surgery. …”
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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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