@article {977,
	title = {Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI},
	journal = {Frontiers in Neuroimaging},
	volume = {2},
	year = {2023},
	issn = {2813-1193},
	doi = {10.3389/fnimg.2023.1055463},
	url = {https://www.frontiersin.org/articles/10.3389/fnimg.2023.1055463},
	author = {Moya-S{\'a}ez, Elisa and de Luis-Garc{\'\i}a, Rodrigo and Alberola-L{\'o}pez, Carlos}
}
@article {920,
	title = {Elastic AlignedSENSE for Dynamic MR Reconstruction: A Proof of Concept in Cardiac Cine},
	journal = {Entropy},
	volume = {23},
	year = {2021},
	pages = {555},
	abstract = {<p>Numerous methods in the extensive literature on magnetic resonance imaging (MRI) reconstruction exploit temporal redundancy to accelerate cardiac cine. Some of them include motion compensation, which involves high computational costs and long runtimes. In this work, we proposed a method {\textendash}-elastic alignedSENSE (EAS){\textendash}- for the direct reconstruction of a motion-free image plus a set of nonrigid deformations to reconstruct a 2D cardiac sequence. The feasibility of the proposed approach was tested in 2D Cartesian and golden radial multi-coil breath-hold cardiac cine acquisitions. The proposed approach was compared against parallel imaging compressed sense (sPICS) and group-wise motion corrected compressed sense (GWCS) reconstructions. EAS provides better results on objective measures with considerable less runtime when an acceleration factor is higher than 10x. Subjective assessment of an expert, however, invited proposing the combination of EAS and GWCS as a preferable alternative to GWCS or EAS in isolation.</p>
},
	issn = {1099-4300},
	doi = {10.3390/e23050555},
	url = {https://www.mdpi.com/1099-4300/23/5/555},
	author = {Alejandro Godino-Moya and Mench{\'o}n-Lara, Rosa-Mar{\'\i}a and Mart{\'\i}n-Fern{\'a}ndez, Marcos and Prieto, Claudia and Alberola-L{\'o}pez, Carlos}
}
@article {947,
	title = {Magnetic Resonance Simulation in Education: Quantitative Evaluation of an Actual Classroom Experience},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	pages = {6011},
	abstract = {<p>Magnetic resonance is an imaging modality that implies a high complexity for radiographers. Despite some simulators having been developed for training purposes, we are not aware of any attempt to quantitatively measure their educational performance. The present study gives an answer to the question: Does an MRI simulator built on specific functional and non-functional requirements help radiographers learn MRI theoretical and practical concepts better than traditional educational method based on lectures? Our study was carried out in a single day by a total of 60 students of a main hospital in Madrid, Spain. The experiment followed a randomized pre-test post-test design with a control group that used a traditional educational method, and an experimental group that used our simulator. Knowledge level was assessed by means of an instrument with evidence of validity in its format and content, while its reliability was analyzed after the experiment. Statistical differences between both groups were measured. Significant statistical differences were found in favor of the participants who used the simulator for both the post-test score and the gain (difference between post-test and pre-test scores). The effect size turned out to be significant as well. In this work we evaluated a magnetic resonance simulation paradigm as a tool to help in the training of radiographers. The study shows that a simulator built on specific design requirements is a valuable complement to traditional education procedures, backed up with significant quantitative results.</p>
},
	author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Matute-Teresa, F{\'a}tima and Bote-Lorenzo, Miguel L and G{\'o}mez-S{\'a}nchez, Eduardo and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos}
}
@article {946,
	title = {A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {210},
	year = {2021},
	pages = {106371},
	issn = {0169-2607},
	doi = {10.1016/j.cmpb.2021.106371},
	url = {https://doi.org/10.1016/j.cmpb.2021.106371},
	author = {Moya-S{\'a}ez, Elisa and {\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos}
}
@article {853,
	title = {Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction},
	journal = {Entropy},
	volume = {22},
	year = {2020},
	pages = {687},
	doi = {https://doi.org/10.3390/e22060687},
	url = {https://www.mdpi.com/1099-4300/22/6/687},
	author = {Mart{\'\i}n-Gonz{\'a}lez, Elena and Sevilla, Teresa and Revilla-Orodea, Ana and Pablo Casaseca-de-la-Higuera and Alberola-L{\'o}pez, Carlos}
}
@article {854,
	title = {Integration of an Intelligent Tutoring System in a Magnetic Resonance Simulator for Education: Technical Feasibility and User Experience},
	journal = {Computer Methods and Programs in Biomedicine},
	year = {2020},
	pages = {105634},
	doi = {https://doi.org/10.1016/j.cmpb.2020.105634},
	url = {https://authors.elsevier.com/a/1bM7z_3sJeWiZh},
	author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Bote-Lorenzo, Miguel L and G{\'o}mez-S{\'a}nchez, Eduardo and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos}
}
@article {841,
	title = {A Web-Based Educational Magnetic Resonance Simulator: Design, Implementation and Testing},
	journal = {Journal of Medical Systems},
	volume = {44},
	year = {2020},
	month = {2020},
	pages = {9},
	author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Bote-Lorenzo, Miguel L and S{\'a}nchez, Eduardo G{\'o}mez and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos}
}
@article {816,
	title = {Computation of exact g-factor maps in 3D GRAPPA reconstructions},
	journal = {Magnetic resonance in medicine},
	volume = {81},
	year = {2019},
	pages = {1353{\textendash}1367},
	author = {I{\~n}aki Rabanillo-Viloria and Zhu, Ante and Santiago Aja-Fern{\'a}ndez and Alberola-L{\'o}pez, Carlos and Hernando, Diego}
}
@conference {hoyos2019evaluation,
	title = {Evaluation in a real environment of a trainable cough monitoring app for smartphones},
	booktitle = {Mediterranean Conference on Medical and Biological Engineering and Computing},
	year = {2019},
	pages = {1175{\textendash}1180},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	author = {Hoyos-Barcel{\'o}, Carlos and Garmendia-Leiza, Jos{\'e} Ram{\'o}n and MD Aguilar-Garcia and Monge-{\'A}lvarez, Jes{\'u}s and P{\'e}rez-Alonso, Diego Asay and Alberola-L{\'o}pez, Carlos and Pablo Casaseca-de-la-Higuera}
}
@article {menchon2019reconstruction,
	title = {Reconstruction techniques for cardiac cine MRI},
	journal = {Insights into imaging},
	volume = {10},
	number = {1},
	year = {2019},
	pages = {100},
	publisher = {Springer Berlin Heidelberg},
	author = {Mench{\'o}n-Lara, Rosa-Mar{\'\i}a and Simmross-Wattenberg, Federico and Pablo Casaseca-de-la-Higuera and Mart{\'\i}n-Fern{\'a}ndez, Marcos and Alberola-L{\'o}pez, Carlos}
}
@conference {804,
	title = {Mapping Raw Acceleration Data on ActiGraph Counts: A Machine Learning Approach},
	booktitle = {Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality},
	year = {2018},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {ActiGraph, Actimetry, Microsoft Band 2, counts},
	isbn = {978-1-4503-6518-5},
	doi = {10.1145/3284179.3284260},
	url = {http://doi.acm.org/10.1145/3284179.3284260},
	author = {Mart{\'\i}n-Gonz{\'a}lez, Elena and Rodrigo de Luis-Garc{\'\i}a and Pablo Casaseca-de-la-Higuera and Garmendia-Leiza, J. R. and Jesus Maria Andres-de-Llano and Alberola-L{\'o}pez, Carlos}
}
