@proceedings {989, title = {Impact of free-water correction on white matter changes measured by diffusion tensor imaging in migraine}, volume = {4601}, year = {2023}, month = {2023}, abstract = {

Menstrual migraine affects about 25\% of female migraine patients. However, the diagnosis of migraine is particularly difficult because the brain changes associated with migraine are challenging to detect with imaging techniques. Diffusion-weighted MRI (dMRI) permits the detection of alterations in the microenvironment of the brain tissues. We investigate whether removing the contribution of the free water component from the diffusion-signal can provide increased sensitivity to identify white matter changes in migraine using diffusion tensor metrics.

}, author = {Guadilla, Irene and Fouto, Ana and {\'A}lvaro Planchuelo-G{\'o}mez and Trist{\'a}n-Vega, Antonio and Ruiz-Tagle, Amparo and Esteves, In{\^e}s and Caetano, Gina and Silva, Nuno and Vilela, Pedro and Gil-Gouveia, Raquel and Aja-Fern{\'a}ndez, Santiago and Figueiredo, Patr{\'\i}cia and Nunes, Rita} } @article {994, title = {Increased MRI-based Brain Age in chronic migraine patients}, journal = {The Journal of Headache and Pain}, volume = {24}, year = {2023}, pages = {133}, abstract = {

Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.

}, keywords = {Biomarkers, Brain age, machine learning, migraine disorders, neuroimaging}, issn = {1129-2377}, doi = {10.1186/s10194-023-01670-6}, url = {https://doi.org/10.1186/s10194-023-01670-6}, author = {Navarro-Gonz{\'a}lez, Rafael and Garc{\'\i}a-Azor{\'\i}n, David and Guerrero-Peral, {\'A}ngel L. and {\'A}lvaro Planchuelo-G{\'o}mez and Aja-Fern{\'a}ndez, Santiago and de Luis-Garc{\'\i}a, Rodrigo} } @proceedings {986, title = {Increased T1w MRI-based brain age in chronic migraine patients}, volume = {5327}, year = {2023}, month = {2023}, abstract = {

Brain-age is an emerging neuroimaging biomarker that represents the aging status of the brain using machine learning techniques from MRI data. It has been successfully applied to the study of different neurological and psychiatric conditions. We hypothesize that patients with migraine may show an increased brain age gap (difference between the age estimated from the MRI data and the chronological age). After building a brain age model from 2,781 healthy subjects, we tested this hypothesis on a dataset with 210 healthy controls and migraine patients. Results showed an increased brain age in chronic migraine patients with respect to healthy controls.

}, author = {Navarro-Gonz{\'a}lez, Rafael and Garc{\'\i}a-Azor{\'\i}n, David and Guerrero, {\'A}ngel L and {\'A}lvaro Planchuelo-G{\'o}mez and Aja-Fern{\'a}ndez, Santiago and de Luis-Garc{\'\i}a, Rodrigo} } @article {843, title = {Identificacion of MRI-based psychosis subtypes: Replication and refinement}, journal = {Progress in Neuro-Psychopharmacology and Biological Psychiatry}, volume = {100}, year = {2020}, pages = {109907}, abstract = {

The identification of the cerebral substrates of psychoses such as schizophrenia and bipolar disorder is likely hampered by its biological heterogeneity, which may contribute to the low replication of results in the field. In this study we aimed to replicate in a completely new sample and supplement the results of a previous study with additional data on this topic. In the aforementioned study we identified a schizophrenia cluster characterized by high mean cortical curvature and low cortical thickness, subcortical hypometabolism and progressive negative symptoms. Here, we have used magnetic resonance images from 61 schizophrenia and 28 bipolar patients, as well as 51 healthy controls and a cluster analysis to search for possible subgroups primarily characterized by cerebral structural data. Diffusion tensor imaging (fractional anisotropy, FA), cognition, clinical data and electroencephalographic (EEG) modulation during a P300 task were used to validate the possible clusters. Two clusters of patients were identified. The first cluster (29 schizophrenia and 18 bipolar patients) showed decreased cortical thickness and area values, as well as lower subcortical volumes and higher cortical curvature in some regions, as compared to the second cluster. This first cluster also showed decreased FA in frontal lobe connections and worse cognitive performance. Although this cluster also showed longer illness duration, there were first episode patients in both clusters and treatment doses and types were not different between clusters. Both clusters of patients showed decreased EEG task-related modulation. In conclusion, our data give additional support to a distinct biologically based cluster encompassing schizophrenia and bipolar disorder patients with cortical and subcortical alterations, hampered cortical connectivity and lower cognitive performance.

}, keywords = {Biotypes, Cortical thickness, Curvature, Subtypes, bipolar disorder, schizophrenia}, issn = {0278-5846}, doi = {https://doi.org/10.1016/j.pnpbp.2020.109907}, url = {http://www.sciencedirect.com/science/article/pii/S0278584619309595}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Lubeiro, Alba and N{\'u}{\~n}ez-Novo, Pablo and Gomez-Pilar, Javier and Rodrigo de Luis-Garc{\'\i}a and del Valle, Pilar and Mart{\'\i}n-Santiago, {\'O}scar and P{\'e}rez-Escudero, Adela and Vicente Molina} } @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} } @inbook {755, title = {Introduction to speckle filtering}, booktitle = {Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video}, year = {2018}, publisher = {IET}, organization = {IET}, chapter = {5}, issn = {978-1-78561-290-9}, author = {Gabriel Ramos-Llord{\'e}n and Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {616, title = {Influence of ultrasound speckle tracking strategies for motion and strain estimation}, journal = {Medical Image Analysis}, volume = {32}, year = {2016}, month = {2016}, pages = {184 - 200}, abstract = {

Abstract Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.

}, issn = {1361-8415}, doi = {http://dx.doi.org/10.1016/j.media.2016.04.002}, url = {http://www.sciencedirect.com/science/article/pii/S1361841516300202}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {567, title = {Impact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach}, journal = {PLoS ONE}, volume = {10}, year = {2015}, pages = {e0137905}, doi = {10.1371/journal.pone.0137905}, url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0137905}, author = {Gonzalo Barrio-Arranz and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Marcos Mart{\'\i}n-Fern{\'a}ndez and Santiago Aja-Fern{\'a}ndez} } @article {532, title = {Improving GRAPPA reconstruction by frequency discrimination in the ACS lines}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {10}, year = {2015}, month = {2015}, pages = {1699-1710}, chapter = {1699}, abstract = {
Purpose
GRAPPA is a well-known parallel imaging method that recovers the MR magnitude image from aliasing by using a weighted interpolation of the data in k-space. To estimate the optimal reconstruction weights, GRAPPA uses a band along the center of the k-space where the signal is sampled at the Nyquist rate, the so-called autocalibrated (ACS) lines. However, while the subsampled lines usually belong to the medium- to high-frequency areas of the spectrum, the ACS lines include the low-frequency areas around the DC component. The use for estimation and reconstruction of areas of the k-space with very different features may negatively affect the final reconstruction quality. We propose a simple, yet powerful method to eliminate reconstruction artifacts, based on the discrimination of the low-frequency spectrum.
Methods
The proposal to improve the estimation of the weights lays on a proper selection of the coefficients within the ACS lines, which advises discarding those points around the DC component. A simple approach is the elimination of a square window in the center of the k-space, although more developed approaches can be used.
Results
The method is tested using real multiple-coil MRI acquisitions. We empirically show this approach achieves great enhancement rates, while keeping the same complexity of the original GRAPPA and reducing the g-factor. The reconstruction is even more accurate when combined with other reconstruction methods. Improvement rates of 35\ \% are achieved for 32 ACS and acceleration rate of 3.
Conclusions
The method proposed highly improves the accuracy of the GRAPPA coefficients and therefore the final image reconstruction. The method is fully compatible with the original GRAPPA formulation and with other optimization methods proposed in literature, and it can be easily implemented into the commercial scanning software.
}, doi = {10.1007/s11548-015-1172-7}, author = {Santiago Aja-Fern{\'a}ndez and Daniel Garc{\'\i}a-Mart{\'\i}n and Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {garmendia2013influence, title = {Influence of institutionalization on the sleep pattern in elderly population}, journal = {Sleep Medicine}, volume = {14}, year = {2013}, pages = {e181{\textendash}e182}, publisher = {Elsevier}, author = {Jose Ramon Garmendia-Leiza and Aguilar Garcia, M and Jes{\'u}s Mar{\'\i}a And De Llano and Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @conference {cordero2013integration, title = {Integration of biomechanical properties in a Markov random field: Application to myocardial motion estimation in cardiomyopathy patients}, booktitle = {Quantitative Medical Imaging}, year = {2013}, pages = {QW2G{\textendash}1}, publisher = {Optical Society of America}, organization = {Optical Society of America}, author = {Lucilio Cordero-Grande and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @article {aja2012influence, title = {Influence of noise correlation in multiple-coil statistical models with sum of squares reconstruction}, journal = {Magnetic Resonance in Medicine}, volume = {67}, number = {2}, year = {2012}, pages = {580{\textendash}585}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega} } @conference {cordero2011improving, title = {Improving Harmonic Phase Imaging by the Windowed Fourier Transform}, booktitle = {Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on}, year = {2011}, pages = {520{\textendash}523}, publisher = {IEEE}, organization = {IEEE}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @conference {585, title = {Intra Heartbeat Variability as a Tool for Cardiovascular Diagnosis and Monitoring}, booktitle = {XXIX Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, volume = {29}, year = {2011}, pages = {343-346}, address = {C{\'a}ceres, Spain}, author = {Daniel Ruiz-Aguado and Marcos Martin-Fern{\'a}ndez and J Royuela-del-Val and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @conference {vegas2010influence, title = {On the influence of interpolation on probabilistic models for ultrasonic images}, booktitle = {Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on}, year = {2010}, pages = {292{\textendash}295}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Diego Mart{\'\i}n-Mart{\'\i}nez and Santiago Aja-Fern{\'a}ndez and Cesar Palencia} } @proceedings {aja2006image, title = {Image quality assessment based on local variance}, year = {2006}, pages = {4815{\textendash}4818}, author = {Santiago Aja-Fern{\'a}ndez and Raul San Jose-Estepar and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {munoz2005image, title = {Image registration based on automatic detection of anatomical landmarks for bone age assessment}, journal = {WSEAS Transactions on Computers}, volume = {4}, number = {11}, year = {2005}, pages = {1596{\textendash}1603}, author = {Emma Mu{\~n}oz-Moreno and Rub{\'e}n C{\'a}rdenes-Almeida and Rodrigo de Luis-Garc{\'\i}a and Miguel Angel Martin-Fernandez and Carlos Alberola-Lopez} } @conference {aja2003inference, title = {Inference with fuzzy granules for computing with words: a practical viewpoint}, booktitle = {Fuzzy Systems, 2003. FUZZ{\textquoteright}03. The 12th IEEE International Conference on}, volume = {1}, year = {2003}, pages = {566{\textendash}571}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} }