Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Enrique Domínguez; Miguel A. Molina-Cabello (IJCNN, 2018)

Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Miguel A. Molina-Cabello (IWINAC (2), 2019)

QModeling: a Multiplatform, Easy-to-Use and Open-Source Toolbox for PET Kinetic Analysis. - Francisco Javier López-González; José Paredes-Pacheco; Karl Thurnhofer-Hemsi; Carlos Rossi; Manuel Enciso; Daniel Toro-Flores; Belén Murcia-Casas; Antonio L. Gutiérrez-Cardo; Núria Roé-Vellvé (Neuroinformatics, 2019)

Super-Resolution of 3D MRI Corrupted by Heavy Noise With the Median Filter Transform. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Lipika Deka (ICIP, 2020)

Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Miguel A. Molina-Cabello (Integr. Comput. Aided Eng., 2020)

Deep learning-based super-resolution of 3D magnetic resonance images by regularly spaced shifting. - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Enrique Domínguez; Rafael Marcos Luque Baena; Núria Roé-Vellvé (Neurocomputing, 2020)

Intensity normalization methods in brain FDG-PET quantification. - Francisco Javier López-González; Jesús Silva-Rodríguez; José Paredes-Pacheco; Aida Niñerola-Baizán; Nikos Efthimiou; Carmen Martín-Martín; Alexis Moscoso; Álvaro Ruibal; Núria Roé-Vellvé; Pablo Aguiar 001 (NeuroImage, 2020)