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

Dongsheng Jiang, Weiqiang Dou, Luc Vosters, Xiayu Xu, Yue Sun, Tao Tan

Research output: Contribution to journalArticleAcademicpeer-review

53 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)566-574
Number of pages9
JournalJapanese Journal of Radiology
Volume36
Issue number9
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • CNN
  • Deep learning
  • Denoising
  • MRI
  • Rician noise

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