Multiscale brain MRI super-resolution using deep 3D convolutional networks

[Display omitted] •A residual-based deep 3D CNN architecture for super-resolution.•Comprehensive performance analysis of key elements of neural networks.•Multi-scale training approach to handle arbitrary scale factors.•Multimodal CNN for super-resolution. The purpose of super-resolution approaches i...

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Bibliographic Details
Published in:	Computerized medical imaging and graphics Vol. 77; p. 101647
Main Authors:	Pham, Chi-Hieu, Tor-Díez, Carlos, Meunier, Hélène, Bednarek, Nathalie, Fablet, Ronan, Passat, Nicolas, Rousseau, François
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.10.2019 Elsevier Science Ltd Elsevier
Subjects:	3D convolutional neural network Artificial neural networks Brain Brain MRI Computer Science Computer Vision and Pattern Recognition Convolution Cortex Image acquisition Image enhancement Image Processing Image reconstruction Image resolution Internal Medicine Learning Magnetic resonance imaging Medical Imaging Neural networks Neuroimaging NMR Nuclear magnetic resonance Optimization Other Post-processing Resonance Scaling factors Structural analysis Super-resolution Training Transfer learning Super-resolution 3D convolutional neural network Brain MRI brain MRI super-resolution
ISSN:	0895-6111, 1879-0771, 1879-0771
Online Access:	Get full text
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