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Abstract #3169

Multi-contrast Brain MRI Image Super-resolution with Gradient-guided Edge Enhancement

Hong Zheng1, Kun Zeng1, Di Guo2, Jiaxi Ying1, Yu Yang1, Xi Peng3, Zhong Chen1, and Xiaobo Qu1

1Department of Electronic Science, Xiamen University, Xiamen, China, 2Xiamen University of Technology, Xiamen, China, 3Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Since magnetic resonance imaging (MRI) can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. 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, iterative back-projection filter is applied to the reconstructed image to further increase image quality. The reconstructed edges are more consistent to the original high-resolution image, indicated with higher PSNR and SSIM than the compared methods.

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