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

Hong, Danfeng; Huang, Chenxi; Yang, Chenhui; Li, Jianpeng; Qian, Yunhan; Cai, Chunting

doi:10.3389/fnins.2020.577937

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…In an application to imaging prostate, Hong et al presented a Bayes shrinkage-based fused wavelet transform (BSbFWT) and Block-based autoencoder network (BBAuto-Net) for removal of noise from prostate MR images [ 121 ]. The method was tested on prostate mp-MRI data obtained from 1.5-T general electric (GE) and 3.0-T Siemens scanners with promising results obtained in comparison with conventional filters such as anisotropic, bilateral, Gabor, Gaussian, mean, NLM, wavelet, Wiener, autoencoders, and autoencoders with NLM filters.…”

Section: Noise Reduction In Mrimentioning

confidence: 99%

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

et al. 2022

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Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

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Section: Noise Reduction In Mrimentioning

confidence: 99%

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

et al. 2022

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“…Hong, et al [50] proposed an attention mechanism-based convolutional neural network for MRI denoising. They also applied the feature fusion technique by combining the local features with the global ones to boost the network's capacity.…”

Section: ⑷mentioning

confidence: 99%

Applicable artificial intelligence for brain disease: A survey

Huang

Wang

et al. 2022

Neurocomputing

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“…In [ 26 ], Ran et al suggested a residual encoder-decoder Wasserstein generated countermeasure network (RED-WGAN) for MR image denoising. Hong et al designed a spatial attention mechanism to obtain the area of interest in MR images, which made use of the multilevel structure and boosted the expressive ability of the network [ 27 ]. Tripathi and Bag proposed a novel CNN for MR image denoising.…”

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

Cited by 14 publications

References 39 publications

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

Applicable artificial intelligence for brain disease: A survey

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

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