Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

Zheng, Hong; Zeng, Kun; Guo, Di; Ying, Jiaxi; Yang, Yu; Xi, Peng; Huang, Feng; Chen, Zhong; Qu, Xiaobo

doi:10.1109/access.2018.2873484

Cited by 42 publications

(37 citation statements)

References 38 publications

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“…We also compared our results with some recently published state-of-the-art (SOTA) MCSR methods, including SSIP [27], SRGR [25], and Zengs model [30]. According to the re- Fig.…”

Section: E Comparing With State-of-the-art Methodsmentioning

confidence: 99%

“…Similar to the SISR methods, current MCSR methods can also be categorized into model-based methods and learning-based methods. Examples of model-based methods are based on non-local mean [23], total variation [24], edge gradient [25], shareable information [26], and similarity [27], [28]. The methods using dictionary learning [29] and convolutional neural network [30] are representatives of the learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Contrast Super-Resolution MRI Through a Progressive Network

Lyu

Shan

Steber

et al. 2020

IEEE Trans. Med. Imaging

123

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Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided therapy, and scientific research. A significant advantage of MRI over other imaging modalities such as computed tomography (CT) and nuclear imaging is that it clearly shows soft tissues in multi-contrasts. Compared with other medical image super-resolution (SR) methods that are in a single contrast, multi-contrast super-resolution studies can synergize multiple contrast images to achieve better super-resolution results. In this paper, we propose a one-level non-progressive neural network for low up-sampling multicontrast super-resolution and a two-level progressive network for high up-sampling multi-contrast super-resolution. Multicontrast information is combined in high-level feature space. Our experimental results demonstrate that the proposed networks can produce MRI super-resolution images with good image quality and outperform other multi-contrast super-resolution methods in terms of structural similarity and peak signal-to-noise ratio. Also, the progressive network produces a better SR image quality than the non-progressive network, even if the original low-resolution images were highly down-sampled.

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“…We also compared our results with some recently published state-of-the-art (SOTA) MCSR methods, including SSIP [27], SRGR [25], and Zengs model [30]. According to the re- Fig.…”

Section: E Comparing With State-of-the-art Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Contrast Super-Resolution MRI Through a Progressive Network

Lyu

Shan

Steber

et al. 2020

IEEE Trans. Med. Imaging

123

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“…We develop a gradient-guided residual network (DGGRN) that is based on two intuitions: (1) CNN-based SR methods [12,13] have achieved significant performance advances in MRI super-resolution; and (2) gradient features of the LR image facilitate the recovery of high-frequency details in an HR image [4,28,30,34,36]. DGGRN consists of two subnets.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…To address the over-smoothing issue, the gradient prior is widely applied in reconstruction- [4,27,30] and CNN-based MRI SR methods [33][34][35]. Image gradient provides the exact positions and magnitudes of high-frequency image parts, which are important for improving the accuracy of super-resolution performance.…”

Section: High-frequency Details Recoverymentioning

confidence: 99%

“…For MRI images, high-frequency image details such as edge structures of the sulcus gyrus and the cortex, substantially impact the detection of suspicious structures, the classification of malformations, and diagnosis. Thus, many studies have considered high-frequency factors in MRI super-resolution [8,[27][28][29][30]. For example, the interpolation approach improves the accuracy of edge reconstruction by introducing contrast guidance [29].…”

Section: Introductionmentioning

confidence: 99%

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Gradient-Guided Convolutional Neural Network for MRI Image Super-Resolution

2019

Applied Sciences

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Super-resolution (SR) technology is essential for improving image quality in magnetic resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency (HR) details from a low-resolution (LR) image. To address this challenge, we develop a gradient-guided convolutional neural network for improving the reconstruction accuracy of high-frequency image details from the LR image. A gradient prior is fully explored to supply the information of high-frequency details during the super-resolution process, thereby leading to a more accurate reconstructed image. Experimental results of image super-resolution on public MRI databases demonstrate that the gradient-guided convolutional neural network achieves better performance over the published state-of-art approaches.

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