Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network
By Kun Zeng, Hong Zheng, C. Cai, Yu Yang, Kaihua Zhang, Zhong Chen. 2018
140 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “17 Lately, so-called fully-convolution CNNs have become popular, in which average pooling across the whole input after the final activation layer replaces the fully-connected layers, significantly reducing the total number of weights in the network.”