RT Journal Article
T1 Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks
A1 Thurnhofer Hemsi, Karl
A1 López-Rubio, Ezequiel
A1 Roé-Vellvé, Núria
A1 Domínguez-Merino, Enrique
A1 Molina-Cabello, Miguel Ángel
K1 Redes neuronales (Informática)
AB 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 thesame 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.
PB IEEE
YR 2018
FD 2018
LK https://hdl.handle.net/10630/16321
UL https://hdl.handle.net/10630/16321
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026