2026-06-01T19:49:21Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/163212026-02-03T11:15:56Zcom_10630_2254col_10630_37953

Thurnhofer Hemsi, Karl López-Rubio, Ezequiel Roé-Vellvé, Núria Domínguez-Merino, Enrique Molina-Cabello, Miguel Ángel 2018-07-20T09:56:27Z 2018-07-20T09:56:27Z 2018 https://hdl.handle.net/10630/16321 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. eng open access Redes neuronales (Informática) Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks journal article