Journal article
Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement
IEEE access, Vol.6, pp.57856-57867
2018
DOI: 10.1109/ACCESS.2018.2873484
Abstract
In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of gradient value between different contrast images to restore a high-resolution image from its input low-resolution version. The similarity of image patches is employed to estimate intensity parameters, leading a more accurate reconstructed image. Then, an iterative back-projection filter is applied to the reconstructed image to further increase the image quality. The new approach is verified on synthetic and real brain MRI images and achieves higher visual quality and higher objective quality criteria than the compared state-of-the-art super-resolution approaches. The gradient information of the multi-contrast MRI images is very useful. With a proper relation model, the proposed method enhances image edges in MRI image super-resolution. Improving the MRI image resolution from very low-resolution observations is challenging. We tackle this problem by first modeling the relation of gradient value in multi-contrast MRI and then performing fast supper-resolution methods. This relation model may be helpful for other MRI reconstruction problems.
Details
- Title: Subtitle
- Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement
- Creators
- Hong Zheng - Guilin University of Electronic TechnologyKun Zeng - Xiamen UniversityDi Guo - Xiamen University of TechnologyJiaxi Ying - Xiamen UniversityYu Yang - Xiamen UniversityXi Peng - Shenzhen Institutes of Advanced TechnologyFeng Huang - NeusoftZhong Chen - Xiamen UniversityXiaobo Qu - Xiamen University
- Resource Type
- Journal article
- Publication Details
- IEEE access, Vol.6, pp.57856-57867
- Publisher
- IEEE
- DOI
- 10.1109/ACCESS.2018.2873484
- ISSN
- 2169-3536
- eISSN
- 2169-3536
- Grant note
- 3502Z20183053 / Science and Technology Program of Xiamen 2017YFC0108703 / National Key R&D Program of China 20720180056 / Fundamental Research Funds for the Central Universities 61571380; 61871341; 61811530021; U1632274; 61672335; 61601389 / National Natural Science Foundation of China (10.13039/501100001809) 2018J06018; 2016J05205 / Natural Science Foundation of Fujian Province (10.13039/501100003392)
- Language
- English
- Date published
- 2018
- Academic Unit
- Radiology
- Record Identifier
- 9984446264102771
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