Ming Ni
Peking University
22 Papers
Ming Ni is an academic researcher from Peking University. The author has contributed to research in topics: Medicine & Computer science. The author has co-authored 1 publications.
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Papers
Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance
TL;DR: In this article , the authors evaluated image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).
A Deep Learning Approach for MRI in the Diagnosis of Labral Injuries of the Hip Joint
Ming Ni,Xiaoyi Wen,Wen Chen,Yuqing Zhao,Yuan Yuan,Piaoe Zeng,Qizheng Wang,Yong Wang,Huishu Yuan +8 more
TL;DR: The diagnosis of labral injury on MRI is time‐consuming and potential for incorrect diagnoses, so it is important to have a good understanding of the underlying cause of injury.
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Preliminary exploration of deep learning-assisted recognition of superior labrum anterior and posterior lesions in shoulder MR arthrography.
Ming Ni,Lixiang Gao,Wen Chen,Qiang Zhao,Yuqing Zhao,Chenyu Jiang,Hui-zong Yuan +6 more
TL;DR: Deep learning can be used to identify SLAP lesions upon initial MR arthrography examination and SLAP-Net performs comparably to senior radiologists.
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Deep Learning Approach for MRI in the Classification of Anterior Talofibular Ligament Injuries.
TL;DR: In this paper , a deep learning method was used for classifying anterior talofibular ligament (ATFL) injuries based on magnetic resonance imaging (MRI), and the results were compared with four radiologists with 5-30 years of experience.
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Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.
Ming Ni,Xiaoyi Wen,Meng-Ze Zhang,Chenyu Jiang,Benjun Wang,Xianchang Zhang,Qiang Zhao,Ning Lang,Liang Jiang,Hui Sh Yuan +9 more
TL;DR: In this paper , the authors explored preoperative dMRI parameters to predict the postoperative outcome of cervical spondylotic myelopathy (CSM) through multifactor correlation analysis and found that fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction and orientation division index significantly positively correlated with CSM patient postoperative outcomes.
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