Methods: The proposed method employs a recently introduced the multi-channel denoising convolutional neural networks (MCDnCNN). Improving the accuracy of MWF estimation based on WMI data acquired using a magnetic resonance (MR) multiple gradient recalled echo (mGRE) imaging sequence is desired. However, MWF estimation is typically sensitive to noise. In particular, myelin water fraction (MWF) is derived from MWI data for quantifying myelination. Guojun Xu 1,2, Yongquan He 1, Qiurong Yu 1, Hongjian He 3,4, Zhiyong Zhao 4, Mingxia Fan 1, Jianqi Li 1, Dongrong Xu 2ġ Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China Ģ Molecular Imaging and Neuropathology Division, Columbia University Department of Psychiatry & New York State Psychiatric Institute, New York, NY, USA ģ Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China Ĥ Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, ChinaĬontributions: (I) Conception and design: D Xu, G Xu (II) Administrative support: D Xu, J Li (III) Provision of study materials or patients: D Xu, G Xu (IV) Collection and assembly of data: G Xu, Y He, Q Yu (V) Data analysis and interpretation: G Xu, D Xu (VI) Manuscript writing: All authors (VII) Final approval of manuscript: All authors.īackground: Myelin water imaging (MWI) is powerful and important for studying and diagnosing neurological and psychiatric diseases.
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