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Communication Dans Un Congrès Année : 2017

Brain MRI Super-Resolution using Deep 3D Convolutional Networks

Résumé

Example-based single image super-resolution (SR) has recently shown outcomes with high reconstruction performance. Several methods based on neural networks have successfully introduced techniques into SR problem. In this paper, we propose a three-dimensional (3D) convolutional neural network to generate high-resolution (HR) brain image from its input low-resolution (LR) with the help of patches of other HR brain images. Our work demonstrates the need of fitting data and network parameters for 3D brain MRI.
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Dates et versions

hal-01596811 , version 1 (28-09-2017)

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Chi-Hieu Pham, Aurelien Ducournau, Ronan Fablet, François Rousseau. Brain MRI Super-Resolution using Deep 3D Convolutional Networks. ISBI 2017 : IEEE 14th International Symposium on Biomedical Imaging, Apr 2017, Melbourne, Australia. pp.197 - 200, ⟨10.1109/ISBI.2017.7950500⟩. ⟨hal-01596811⟩
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