Publications

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Conference Paper
Planchuelo-Gómez, Á., M. Descoteaux, S. Aja-Fernández, J. Hutter, D. K. Jones, and C. M. W. Tax, "Data-driven and physics-informed learning of efficient acquisition protocols", ISMRM Workshop on Diffusion MRI: From Research to Clinic, Amsterdam, The Netherlands, 2022.
Jiménez-Galindo, D., P. Casaseca-de-la-Higuera, and L. M. San-José-Revuelta, "A Novel Design Method for Digital FIR/IIR Filters Based on the Shuffle Frog-Leaping Algorithm", 2019 27th European Signal Processing Conference (EUSIPCO): IEEE, pp. 1–5, 2019.
Plumley, A., F. Padormo, M. Cercignani, R. O'Halloran, R. Teixeira, Á. Planchuelo-Gómez, A. Legouhy, T. Luo, and D. K. Jones, "Tensors and Tracts at 64 mT", ISMRM Workshop on Diffusion MRI: From Research to Clinic, Amsterdam, The Netherlands, 2022.
Journal Article
Aja-Fernández, S., G. París, C. Martín-Martín, D. K. Jones, and AT. -Vega, "Anisotropy measure from three diffusion-encoding gradient directions", Magnetic Resonance Imaging, vol. 88, pp. 38–43, 2022.
Aja-Fernández, S., A. Tristán-Vega, and D. K. Jones, "Apparent propagator anisotropy from single-shell diffusion MRI acquisitions", Magnetic Resonance in Medicine, vol. 85, issue 5, pp. 2869-2881, 2021.
Afzali, M., S. Aja-Fernández, and D. K. Jones, "Direction-averaged diffusion-weighted MRI signal using different axisymmetric B-tensor encoding schemes", Magnetic Resonance in Medicine, vol. n/a, 2020.
Pieciak, T., M. Afzali, F. Bogusz, S. Aja-Fernández, and D. K. Jones, "Q-space quantitative diffusion MRI measures using a stretched-exponential representation", arXiv, 2020.
Merino-Caviedes, S., L. Gutiérrez, J. Alfonso-Almazán, S. Sanz-Estébanez, L. Cordero-Grande, J. Quintanilla, J. Sánchez-González, M. Marina-Breysse, C. Galán-Arriola, D. Enríquez-Vázquez, et al., "Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy", Scientific Reports, vol. 11, 2021.
Javadikia, H., S. Sabzi, and J. I. Arribas, "An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange", Spanish Journal of Agricultural Research, vol. 16, issue 4, pp. e0204, 2018.
De Luca, A., A. Ianus, A. Leemans, M. Palombo, N. Shemesh, H. Zhang, D. C. Alexander, M. Nilsson, M. Froeling, G-J. Biessels, et al., "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge", NeuroImage, pp. 118367, 2021.
De Luca, A., A. Ianus, A. Leemans, M. Palombo, N. Shemesh, H. Zhang, D. C. Alexander, M. Nilsson, M. Froeling, G-J. Biessels, et al., "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge", NeuroImage, pp. 118367, 2021.
De Luca, A., A. Ianus, A. Leemans, M. Palombo, N. Shemesh, H. Zhang, D. C. Alexander, M. Nilsson, M. Froeling, G-J. Biessels, et al., "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge", NeuroImage, pp. 118367, 2021.
De Luca, A., A. Ianus, A. Leemans, M. Palombo, N. Shemesh, H. Zhang, D. C. Alexander, M. Nilsson, M. Froeling, G-J. Biessels, et al., "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge", bioRxiv, 2021.
De Luca, A., A. Ianus, A. Leemans, M. Palombo, N. Shemesh, H. Zhang, D. C. Alexander, M. Nilsson, M. Froeling, G-J. Biessels, et al., "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge", bioRxiv, 2021.
De Luca, A., A. Ianus, A. Leemans, M. Palombo, N. Shemesh, H. Zhang, D. C. Alexander, M. Nilsson, M. Froeling, G-J. Biessels, et al., "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge", bioRxiv, 2021.
Sabzi, S., H. Javadikia, and J. I. Arribas, "A three-variety automatic and non-intrusive computer vision system for the estimation of orange fruit pH value", Measurement, vol. 152, pp. 107298, 2020.