MRI denoising using progressively distribution-based neural network

Li, Sanqian; Zhou, Jiazhen; Liang, Dong; Liu, Qiegen

doi:10.1016/j.mri.2020.04.006

Cited by 35 publications

(21 citation statements)

References 29 publications

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“…Six kinds of deep learning models were trained: CNN-DMRI [ 28 ], RicianNet [ 29 ], 2D-DCRNet, 2D-Parallel-RicianNet, 3D-DCRNet, and 3D-Parallel-RicianNet. We compared these six deep learning models with four traditional denoising methods: NLM, BM3D, ODCT3D [ 57 ], and PRI-NLM3D [ 57 ].…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…Li et al designed a progressive network learning strategy by fitting the distribution of pixel-level and feature-level intensities. Their experimental results demonstrated the great potential of the proposed network [ 29 ]. Gregory et al created HydraNet, a multibranch deep neural network architecture that learned to denoise MR images at a multitude of noise levels, and proved the superiority of the network on denoising complex noise distributions compared to some deep learning-based methods [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

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Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Liu

2021

Computational Intelligence and Neuroscience

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Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D-Parallel-RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground-truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1-weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D-Parallel-RicianNet network achieves performance superior to that of several state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

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Section: Experiments Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Liu

2021

Computational Intelligence and Neuroscience

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“…The tensor library was used to accomplish the CGAN architecture. Li et al [117] proposed a progressive network learning strategy (PNLS) that fits the Racian distribution with large convolutional filters.…”

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

165

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Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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“…The batch normalization (BN) operation normalizes the input data; hence, it will destroy the original contrast of MR images. Besides, it has been pointed out that BN is more suitable to map data with regular distribution (Li et al, 2020). From the generation of Rician noise, it can be determined that the noise is non-linear.…”

Section: Feature Extraction Blockmentioning

confidence: 99%

“…Previous research has established that the noise in MRI is governed by the Rician distribution, in which both real and imaginary parts are corrupted by Gaussian noise with equal variance (Bhadauria and Dewal, 2013;Li et al, 2020). The Rician distribution is signal-dependent as distinct from additive Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising

et al. 2020

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Magnetic Resonance Imaging (MRI) is an indispensable tool in the diagnosis of brain diseases due to painlessness and safety. Nevertheless, Rician noise is inevitably injected during the image acquisition process, which leads to poor observation and interferes with the treatment. Owing to the complexity of Rician noise, using the elimination method of Gaussian to remove it does not perform well. Therefore, the feature fusion and attention network (FFA-DMRI) is proposed to separate noise from observed MRI. Inspired by the attention-guided CNN network (ADNet) and Convolutional block attention module (CBAM), a spatial attention mechanism has been specially designed to obtain the area of interest in MRI. Furthermore, the feature fusion block concatenates local with global information, which makes full use of the multilevel structure and boosts the expressive ability of network. The comprehensive experiments on Alzheimer's disease neuroimaging initiative dataset (ADNI) have demonstrated high effectiveness of FFA-DMRI with maintaining the crucial brain details. Moreover, in terms of visual inspections, the denoising results are also consistent with human perception.

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MRI denoising using progressively distribution-based neural network

Cited by 35 publications

References 29 publications

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Methods for image denoising using convolutional neural network: a review

FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising

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