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Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks
dc.contributor.author | Thurnhofer Hemsi, Karl | |
dc.contributor.author | López-Rubio, Ezequiel | |
dc.contributor.author | Roé-Vellvé, Núria | |
dc.contributor.author | Domínguez-Merino, Enrique | |
dc.contributor.author | Molina-Cabello, Miguel Ángel | |
dc.date.accessioned | 2018-07-20T09:56:27Z | |
dc.date.available | 2018-07-20T09:56:27Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/10630/16321 | |
dc.description.abstract | Enhancing resolution is a permanent goal in magnetic resonance (MR) imaging, in order to keep improving diagnostic capability and registration methods. Super-resolution (SR) techniques are applied at the postprocessing stage, and their use and development have progressively increased during the last years. In particular, example-based methods have been mostly proposed in recent state-of-the-art works. In this paper, a combination of a deep-learning SR system and a random shifting technique to improve the quality of MR images is proposed, implemented and tested. The model was compared to four competitors: cubic spline interpolation, non-local means upsampling, low-rank total variation and a three-dimensional convolutional neural network trained with patches of HR brain images (SRCNN3D). The newly proposed method showed better results in Peak Signal-to-Noise Ratio, Structural Similarity index, and Bhattacharyya coefficient. Computation times were at the same level as those of these up-to-date methods. When applied to downsampled MR structural T1 images, the new method also yielded better qualitative results, both in the restored images and in the images of residuals. | en_US |
dc.description.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Redes neuronales (Informática) | en_US |
dc.subject.other | Magnetic resonance imaging | en_US |
dc.subject.other | Super resolution | en_US |
dc.subject.other | Convolutional neural networks | en_US |
dc.title | Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks | en_US |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.centro | E.T.S.I. Informática | en_US |
dc.relation.eventtitle | 2018 IEEE World Congress on Computational Intelligence | en_US |
dc.relation.eventplace | Río de Janeiro, Brasil | en_US |
dc.relation.eventdate | Julio de 2018 | en_US |
dc.type.hasVersion | info:eu-repo/semantics/submittedVersion | es_ES |