RT Journal Article
T1 Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting
A1 Thurnhofer-Hemsi, Karl
A1 López-Rubio, Ezequiel
A1 Domínguez-Merino, Enrique
A1 Luque-Baena, Rafael Marcos
A1 Roé-Vellvé, Núria
K1 Imágenes por resonancia magnética
AB The image acquisition process in the field of magnetic resonance imaging (MRI) does not always provide high resolution results that may be useful for a clinical analysis. Super-resolution (SR) techniques manage to increase the image resolution, being especially effective those based on examples that determine a correspondence between patterns of low resolution and high resolution. Deep learning neural networks have been applied in recent years to estimate this association with very competitive results. In this work, the starting point is a convolutional neuronal network to which a regularly spaced shifting mechanism over the input image is applied, with the aim of substantially improving the quality of the resulting image. This hybrid proposal has been compared with several SR techniques using the peak signal-to-noise ratio, structural similarity index and Bhattacharyya coefficient metrics. The results obtained on different MR images show a considerable improvement both in the restored image and in the residual image without an excessive increase in computing time.
PB Elsevier
YR 2020
FD 2020-07-20
LK https://hdl.handle.net/10630/32807
UL https://hdl.handle.net/10630/32807
LA eng
NO Thurnhofer-Hemsi, K., Lopez-Rubio, E., Dominguez, E., Luque-Baena, R. M., & Roé-Vellvé, N. (2020). Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting. Neurocomputing, 398, 314-327.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026