Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920463 | Computers in Biology and Medicine | 2018 | 9 Pages |
Abstract
In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which can be grouped into two categories: single-contrast super-resolution and multi-contrast super-resolution, where the former has no reference information, and the latter applies a high-resolution image of another modality as a reference. In this paper, we propose a deep convolutional neural network model, which performs single- and multi-contrast super-resolution reconstructions simultaneously. Experimental results on synthetic and real brain MRI images show that our convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity.
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Authors
Kun Zeng, Hong Zheng, Congbo Cai, Yu Yang, Kaihua Zhang, Zhong Chen,