Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.
Purpose: To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly.Materials and Methods: Multi-channel DnCNN (MCDnCNN) method with...
| Publicado en: | Japanese Journal of Radiology Vol. 36; no. 9; pp. 566 - 575 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131336169&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131336169 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Sep2018 vid: 36 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131336169 131336169 NLM29982919 10.1007/s11604-018-0758-8 NLM29982919 131336169 ppf: 566 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. aug: au: Jiang, Dongsheng Dou, Weiqiang Vosters, Luc Xu, Xiayu Sun, Yue Tan, Tao affil: School of Basic Medical Science, Digital Medical Research Center, Fudan University, Shanghai, People’s Republic of China sug: subj: Imaging, Three-Dimensional Methods Neural Networks (Computer) Magnetic Resonance Imaging Methods Brain Anatomy and Histology Sensitivity and Specificity Data Collection Reference Values Algorithms Questionnaires Scales ab: Purpose: To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly.Materials and Methods: Multi-channel DnCNN (MCDnCNN) method with two training strategies was developed to denoise MR images with and without a specific noise level, respectively. To evaluate our method, three datasets from two public data sources of IXI dataset and Brainweb, including T1 weighted MR images acquired at 1.5 and 3 T as well as MR images simulated with a widely used MR simulator, were randomly selected and artificially added with different noise levels ranging from 1 to 15%. For comparison, four other state-of-the-art denoising methods were also tested using these datasets.Results: In terms of the highest peak-signal-to-noise-ratio and global of structure similarity index, our proposed MCDnCNN model for a specific noise level showed the most robust denoising performance in all three datasets. Next to that, our general noise-applicable model also performed better than the rest four methods in two datasets. Furthermore, our training model showed good general applicability.Conclusion: Our proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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