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. 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. 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. Our proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise.
[1]Fudan Univ, Digital Med Res Ctr, Sch Basic Med Sci, Shanghai, Peoples R China;
[2]Radboud Univ Nijmegen, Med Ctr, Dept Radiol & Nucl Med, Nijmegen, Netherlands;
[3]MR Res China, GE Healthcare, Beijing, Peoples R China;
[4]Xi An Jiao Tong Univ, Sch Life Sci & Technol, Minist Educ, Key Lab Biomed Informat Engn, Xian, Shaanxi, Peoples R China;
[5]Xi An Jiao Tong Univ, BEBC, Xian, Shaanxi, Peoples R China;
[6]Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands;
[7]Eindhoven Univ Technol, Dept Biomed Engn, Eindhoven, Netherlands;
[8]ScreenPoint Med, Nijmegen, Netherlands
(8.0+极速模式)