Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Zheng, Hong; Qu, Xiaobo; Bai, Zheng-Jian; Liu, Yunsong; Guo, Di; Dong, Jiyang; Xi, Peng; Chen, Zhong

doi:10.1186/s12880-016-0176-2

Cited by 37 publications

(21 citation statements)

References 31 publications

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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%

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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Section: Proposed Methodsmentioning

confidence: 99%

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

2019

Applied Sciences

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“…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%

Multi-Contrast Super-Resolution MRI Through a Progressive Network

Lyu

Shan

Steber

et al. 2020

IEEE Trans. Med. Imaging

137

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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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“…A feature‐based nonlocal means method has been proposed to upsample brain MRI. Recently, Zheng et al proposed a reference‐guided NEDI using the local‐weight similarity (ILWS) between different contrast‐weighted MR images. However, the methods relying on reference‐modal priors alone may produce some unexpected artifacts …”

Section: Introductionmentioning

confidence: 99%

Robust MR image super‐resolution reconstruction with cross‐modal edge‐preserving regularization

Zhu

Ren

Qiu

et al. 2019

Int J Imaging Syst Tech

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In this article, we propose a novel image super‐resolution (SR) reconstruction method in the field of magnetic resonance imaging, which is based on a cross‐modal edge‐preserving regularization integrating the internal gradient prior from the target‐modal image itself and the external gradient prior from the reference‐modal image obtained by pre‐scan in many medical imaging scenes. The reference‐modal image is a high‐resolution guidance image that has much shareable information such as gradient orientation on edge regions, which can be used to improve the image resolution of the target modal. In addition, to be robust against the misalignment between the target‐modal image and reference‐modal image, a multimodal registration is incorporated in the SR reconstruction process. In this work, the proposed SR method can be formulated as an alternating optimization problem, that is, the target‐modal and reference‐modal images are alternately updated through iterations. Experimental results on simulated and realistic images show the superior performance of the proposed approach over several state‐of‐the‐art SR techniques.

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Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Cited by 37 publications

References 31 publications

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

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

Multi-Contrast Super-Resolution MRI Through a Progressive Network

Robust MR image super‐resolution reconstruction with cross‐modal edge‐preserving regularization

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