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Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

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Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

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.

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Funding

This project is not funded by any Grants.

Author information

Author notes

  1. Dongsheng Jiang and Weiqiang Dou have equal contributions on this work.

Authors and Affiliations

  1. School of Basic Medical Science, Digital Medical Research Center, Fudan University, Shanghai, People’s Republic of China

    Dongsheng Jiang

  2. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

    Weiqiang Dou

  3. GE Healthcare, MR Research China, Beijing, People’s Republic of China

    Weiqiang Dou

  4. The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Xiayu Xu

  5. Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Xiayu Xu

  6. Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Luc Vosters & Yue Sun

  7. Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Tao Tan

  8. ScreenPoint Medical, Nijmegen, The Netherlands

    Tao Tan

Authors
  1. Dongsheng Jiang
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  2. Weiqiang Dou
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  3. Luc Vosters
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  4. Xiayu Xu
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  5. Yue Sun
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  6. Tao Tan
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Corresponding author

Correspondence to Tao Tan.

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Ethical approval

The local ethics committee waived the need for review board approval and written informed consent, considering the retrospective character of this study.

Conflict of interest

There is no conflict of interest.

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Cite this article

Jiang, D., Dou, W., Vosters, L. et al. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Jpn J Radiol 36, 566–574 (2018). https://doi.org/10.1007/s11604-018-0758-8

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  • DOI: https://doi.org/10.1007/s11604-018-0758-8

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