Deep adaptive blending network for 3D magnetic resonance image denoising

Xu, Yi, Han, Kang, Zhou, Yongming, Wu, Jian, Xie, Xin, and Xiang, Wei (2021) Deep adaptive blending network for 3D magnetic resonance image denoising. IEEE Journal of Biomedical and Health Informatics, 25 (9). pp. 3321-3331.

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The visual quality of magnetic resonance images (MRIs) is crucial for clinical diagnosis and scientific research. The main source of quality degradation is the noise generated during MRI acquisition. Although denoising MRI by deep learning methods shows great superiority compared with traditional methods, the deep learning methods reported to date in the literature cannot simultaneously leverage long-range and hierarchical information, and cannot adequately utilize the similarity in 3D MRI. In this paper, we address the two issues by proposing a deep adaptive blending network (DABN) characterized by a large receptive field residual dense block and an adaptive blending method. We first propose the large receptive field residual dense block that can capture long-range information and fuse hierarchical features simultaneously. Then we propose the adaptive blending method that produces denoised pixels by adaptively filtering 3D MRI, which explicitly utilizes the similarity in 3D MRI. Residual is also considered as a compensating item after adaptive filtering. The blending adaptive filter and residual are predicted by a network consisting of several large receptive field residual dense blocks. Experimental results show that the proposed DABN outperforms state-of-the-art denoising methods in both clinical and simulated MRI data.

Item ID: 70153
Item Type: Article (Research - C1)
ISSN: 2168-2208
Keywords: blending method, convolutional neural network, hierarchical features fusion, large receptive field, Magnetic resonance image denoising
Copyright Information: © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
Date Deposited: 07 Apr 2022 02:14
Downloads: Total: 3
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