Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks - Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel; Roé-Vellvé, Núria; Domínguez-Merino, Enrique; Molina-Cabello, Miguel Ángel (IEEE, 2018)
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.
Keywords: Redes neuronales (Informática); Magnetic resonance imaging; Super resolution; Convolutional neural networks