@article {963, title = {Synthetic MRI improves radiomics-based glioblastoma survival prediction}, journal = {NMR in Biomedicine}, year = {2022}, chapter = {e4754}, doi = {10.1002/nbm.4754}, author = {Elisa Moya-S{\'a}ez and Rafael Navarro-Gonz{\'a}lez and Santiago Cepeda and {\'A}ngel P{\'e}rez-N{\'u}{\~n}ez and Rodrigo de Luis-Garcia and Santiago Aja-Fernández and Carlos Alberola-L{\'o}pez} } @article {934, title = {Accurate free-water estimation in white matter from fast diffusion MRI acquisitions using the spherical means technique}, journal = {Magnetic Resonance in Medicine}, volume = {87}, year = {2021}, month = {2022}, pages = {1028-1035}, type = {Techncial Note}, abstract = {

Purpose To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of different b-values. Data sets considered comprise 32-64 gradients near plus 6 gradients near . Theory and Methods The spherical means of each diffusion MRI set with the same b-value are computed. These means are related to the inherent diffusion parameters within the voxel (free- and cellular-water fractions; cellular-water diffusivity), which are solved by constrained nonlinear least squares regression. Results The proposed method outperforms those based on mixtures of two Gaussians for the kind of data sets considered. W.r.t. the accuracy, the former does not introduce significant biases in the scenarios of interest, while the latter can reach a bias of 5\%{\textendash}7\% if fiber crossings are present. W.r.t. the precision, a variance near , compared to 15\%, can be attained for usual configurations. Conclusion It is possible to compute reliable estimates of the free-water fraction inside the white matter by complementing typical DTI acquisitions with few gradients at a lowb-value. It can be done voxel-by-voxel, without imposing spatial regularity constraints.

}, keywords = {diffusion MRI, free water, spherical means, white matter}, doi = {https://doi.org/10.1002/mrm.28997}, author = {Antonio Trist{\'a}n-Vega and Guillem Par{\'\i}s and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez} } @article {899, title = {Apparent propagator anisotropy from single-shell diffusion MRI acquisitions}, journal = {Magnetic Resonance in Medicine}, volume = {85}, year = {2021}, month = {2021}, pages = {2869-2881}, chapter = {2869}, doi = {https://doi.org/10.1002/mrm.28620}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28620}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Derek K. Jones} } @article {900, title = {Efficient and accurate EAP imaging from multi-shell dMRI with Micro-Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT)}, journal = {NeuroImage}, volume = {227}, year = {2021}, month = {2021}, pages = {117616}, issn = {1053-8119}, doi = {https://doi.org/10.1016/j.neuroimage.2020.117616}, url = {http://www.sciencedirect.com/science/article/pii/S1053811920311010}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {937, title = {Gray matter cortical changes in patients with persistent headache after COVID-19 infection: an exploratory study}, booktitle = {International Headache Congress 2021}, year = {2021}, month = {2021}, publisher = {International Headache Society \& European Headache Federation}, organization = {International Headache Society \& European Headache Federation}, address = {Virtual Congress}, abstract = {

Objective: To evaluate gray matter alterations in patients with persistent headache after COVID-19 resolution.
Methods: Exploratory case-control study. Highresolution 3D brain T1-weighted Magnetic Resonance Imaging data were acquired in patients with persistent
headache after COVID-19 infection and healthy controls (HC). FreeSurfer (version 6.0) was employed to segment the T1-weighted images and extract the mean values of the cortical curvature (CC) and thickness (CT), surface area (SA) and gray matter volume (GMV) of 68 cortical regions. GMV comparisons were adjusted for intracranial volume. Significant results were considered with p \< 0.05 (False Discovery Rate corrected).
Results: Ten patients with persistent headache after COVID-19 (mean age: 53.8 +- 7.8 years; nine women) and 10 HC balanced for age and sex (mean age: 53.1 +- 7.0 years; nine women) were included in the study. Significant higher mean SA and GMV values were found in patients with persistent headache compared to HC in the bilateral medial orbitofrontal cortex, left rostral middle frontal gyrus, and right pars opercularis and superior frontal gyrus. In the patients, significant higher GMV in the right caudal anterior cingulate gyrus and SA values in five temporal, frontal and parietal regions were observed. No CC or CT changes were found.
Conclusions: Persistent headache after COVID-19 infection is related to gray matter cortical changes defined by higher GMV and SA values mainly localized in frontal regions.

}, url = {https://journals.sagepub.com/doi/pdf/10.1177/03331024211034005}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Moro, Ra{\'u}l and Rodrigo de Luis-Garc{\'\i}a} } @article {907, title = {Multimodal fusion analysis of structural connectivity and gray matter morphology in migraine}, journal = {Human Brain Mapping}, volume = {42}, year = {2021}, pages = {908-921}, abstract = {

No specific migraine biomarkers have been found in single-modality MRI studies. We aimed at establishing biomarkers for episodic and chronic migraine using diverse MRI modalities. We employed canonical correlation analysis and joint independent component analysis to find structural connectivity abnormalities that are related to gray matter morphometric alterations. The number of streamlines (trajectories of estimated fiber-tracts from tractography) was employed as structural connectivity measure, while cortical curvature, thickness, surface area, and volume were used as gray matter parameters. These parameters were compared between 56 chronic and 54 episodic migraine patients, and 50 healthy controls. Cortical curvature alterations were associated with abnormalities in the streamline count in episodic migraine patients compared to controls, with higher curvature values in the frontal and temporal poles being related to a higher streamline count. Lower streamline count was found in migraine compared to controls in connections between cortical regions within each of the four lobes. Higher streamline count was found in migraine in connections between subcortical regions, the insula, and the cingulate and orbitofrontal cortex, and between the insula and the temporal region. The connections between the caudate nucleus and the orbitofrontal cortex presented worse connectivity in chronic compared to episodic migraine. The hippocampus was involved in connections with higher and lower number of streamlines in chronic migraine. Strengthening of structural networks involving pain processing and subcortical regions coexists in migraine with weakening of cortical networks within each lobe. The multimodal analysis offers a new insight about the association between brain structure and connectivity.

}, keywords = {Brain, Magnetic Resonance Imaging, connectome, diffusion magnetic resonance imaging, migraine disorders}, doi = {https://doi.org/10.1002/hbm.25267}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25267}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Rodrigo de Luis-Garc{\'\i}a} } @conference {939, title = {Resting-state functional alterations in patients with persistent headache after COVID-19 infection: an exploratory study}, booktitle = {International Headache Congress 2021}, year = {2021}, month = {2021}, publisher = {International Headache Society \& European Headache Federation}, organization = {International Headache Society \& European Headache Federation}, address = {Virtual Congress}, abstract = {

Objective: To evaluate resting-state functional alterations in patients with persistent headache after COVID-19 resolution.
Methods: Exploratory case-control study. Highresolution brain resting-state functional Magnetic Resonance Imaging data were acquired in patients with
persistent headache after COVID-19 infection and healthy controls (HC). CONN toolbox (version 17) was employed to assess the resting-state functional connectivity between 84 cortical and subcortical gray matter regions of interest. Significant results were considered with p \< 0.05 (Family Discovery Rate and seed-level corrected).
Results: Ten patients with persistent headache after COVID-19 (mean age: 53.8 +- 7.8 years; nine women) and 10 HC balanced for age and sex (mean age: 51.9 +- 6.6 years; nine women) were included in the study. Statistically significant higher functional connectivity was observed in the patients with persistent headache compared to HC in 10 connections. These connections were composed of an occipital region and another region that included the isthmus cingulate gyrus, a frontal or a parietal area. In the patients, significant lower functional connectivity was found in 12 connections between the cingulate and hippocampal gyri, parietal, temporal and frontal regions.
Conclusions: Patients with persistent headache after COVID-19 infection present strengthened functional connectivity with occipital regions and weakened functional connectivity between frontal, temporal and parietal regions.

}, url = {https://journals.sagepub.com/doi/pdf/10.1177/03331024211034005}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Moro, Ra{\'u}l and Rodrigo de Luis-Garc{\'\i}a} } @conference {938, title = {White matter microstructural alterations in patients with persistent headache after COVID-19 infection: an exploratory study}, booktitle = {International Headache Congress 2021}, year = {2021}, month = {2021}, publisher = {International Headache Society \& European Headache Federation}, organization = {International Headache Society \& European Headache Federation}, address = {Virtual Congress}, abstract = {

Objective: To evaluate white matter alterations in patients with persistent headache after COVID-19 resolution.
Methods: Exploratory case-control study. Highresolution brain diffusion Magnetic Resonance Imaging data were acquired in patients with persistent headache after COVID-19 infection and healthy controls (HC). Tract-Based Spatial Statistics was used to compare fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD) and the return-to-axial (RTAP), return-to-origin (RTOP) and return-to-plane probability (RTPP) between the groups. RTAP, RTOP and RTPP were obtained with a new approach called AMURA (https://www.lpi.tel.uva.es/AMURA). Significant results were considered with p \< 0.05 (Family-Wise Error corrected) and region size larger than 30 mm3.
Results: Ten patients with persistent headache after COVID-19 (mean age: 53.8 +- 7.8 years; nine women) and 10 HC balanced for age and sex (mean age: 53.1 +- 7.0 years; nine women) were included in the study. Significant higher AD and lower RTPP values were found in patients with persistent headache compared to HC in five regions from the corona radiata, and the external and internal capsule. In the patients, significant lower RTPP values were identified in six additional areas from the same tracts and the superior longitudinal fasciculus. No additional changes were found.
Conclusions: White matter axonal alterations are present in patients with persistent headache after COVID-19 infection.

}, url = {https://journals.sagepub.com/doi/pdf/10.1177/03331024211034005}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Moro, Ra{\'u}l and Rodrigo de Luis-Garc{\'\i}a} } @article {912, title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge}, journal = {bioRxiv}, year = {2021}, month = {2021}, doi = {10.1101/2021.03.02.433228}, url = {https://www.biorxiv.org/content/early/2021/03/02/2021.03.02.433228}, author = {De Luca, Alberto and Ianus, Andrada and Leemans, Alexander and Palombo, Marco and Shemesh, Noam and Zhang, Hui and Alexander, Daniel C and Nilsson, Markus and Froeling, Martijn and Biessels, Geert-Jan and Zucchelli, Mauro and Frigo, Matteo and Albay, Enes and Sedlar, Sara and Alimi, Abib and Deslauriers-Gauthier, Samuel and Deriche, Rachid and Fick, Rutger and Maryam Afzali and Tomasz Pieciak and Bogusz, Fabian and Santiago Aja-Fern{\'a}ndez and Ozarslan, Evren and Derek K. Jones and Chen, Haoze and Jin, Mingwu and Zhang, Zhijie and Wang, Fengxiang and Nath, Vishwesh and Parvathaneni, Prasanna and Morez, Jan and Sijbers, Jan and Jeurissen, Ben and Fadnavis, Shreyas and Endres, Stefan and Rokem, Ariel and Garyfallidis, Eleftherios and Sanchez, Irina and Prchkovska, Vesna and Rodrigues, Paulo and Landman, Bennet A and Schilling, Kurt G} } @article {933, title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge}, journal = {NeuroImage}, year = {2021}, month = {2021}, pages = {118367}, issn = {1053-8119}, doi = {https://doi.org/10.1016/j.neuroimage.2021.118367}, url = {https://www.sciencedirect.com/science/article/pii/S1053811921006431}, author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C. Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fern{\'a}ndez and Evren {\"O}zarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fengxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G. Schilling} } @proceedings {856, title = {AMURA with standard single-shell acquisition can detect changes beyond the Diffusion Tensor: a migraine clinical study}, volume = {4549}, year = {2020}, month = {2020}, abstract = {AMURA (Apparent Measures Using Reduced Acquisitions) is an alternative formulation to drastically reduce the number of samples needed for the estimation of diffusion properties related to the Ensemble Average diffusion Propagator (EAP). Although these measures were initially intended for medium-to-high b-values, in this work we evaluate their performance in DTI-like acquisitions. Fifty healthy controls, 54 episodic migraine (EM) and 56 chronic migraine (CM) patients were compared, using a single-shell diffusion scheme at b=1000 s/mm2. We compare AMURA measures (return-to-origin, return-to-axis and return-to-plane probabilities) to traditional DTI measures. Differences between EM and controls were only detectable using the return-to-origin probability.}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez} } @inbook {895, title = {Alternative Diffusion Anisotropy Metric from Reduced MRI Acquisitions}, booktitle = {Computational Diffusion MRI}, year = {2020}, pages = {13{\textendash}24}, publisher = {Springer, Cham}, organization = {Springer, Cham}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Rodrigo de Luis-Garc{\'\i}a and Derek K. Jones} } @article {891, title = {Alternative Microstructural Measures to Complement Diffusion Tensor Imaging in Migraine Studies with Standard MRI Acquisition}, journal = {Brain Sciences}, volume = {10}, year = {2020}, month = {2020}, pages = {711}, abstract = {The white matter state in migraine has been investigated using diffusion tensor imaging (DTI) measures, but results using this technique are conflicting. To overcome DTI measures, we employed ensemble average diffusion propagator measures obtained with apparent measures using reduced acquisitions (AMURA). The AMURA measures were return-to-axis (RTAP), return-to-origin (RTOP) and return-to-plane probabilities (RTPP). Tract-based spatial statistics was used to compare fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity from DTI, and RTAP, RTOP and RTPP, between healthy controls, episodic migraine and chronic migraine patients. Fifty healthy controls, 54 patients with episodic migraine and 56 with chronic migraine were assessed. Significant differences were found between both types of migraine, with lower axial diffusivity values in 38 white matter regions and higher RTOP values in the middle cerebellar peduncle in patients with a chronic migraine (p \< 0.05 family-wise error corrected). Significantly lower RTPP values were found in episodic migraine patients compared to healthy controls in 24 white matter regions (p \< 0.05 family-wise error corrected), finding no significant differences using DTI measures. The white matter microstructure is altered in a migraine, and in chronic compared to episodic migraine. AMURA can provide additional results with respect to DTI to uncover white matter alterations in migraine.}, issn = {2076-3425}, doi = {10.3390/brainsci10100711}, url = {https://www.mdpi.com/2076-3425/10/10/711}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Rodrigo de Luis-Garc{\'\i}a and Rodr{\'\i}guez, Margarita and Santiago Aja-Fern{\'a}ndez} } @article {839, title = {Direction-averaged diffusion-weighted MRI signal using different axisymmetric B-tensor encoding schemes}, journal = {Magnetic Resonance in Medicine}, volume = {n/a}, year = {2020}, month = {2020}, keywords = {B-tensor encoding, diffusion-weighted~MRI, direction-averaged diffusion signal, high b-value, power-law}, doi = {10.1002/mrm.28191}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28191}, author = {Maryam Afzali and Santiago Aja-Fern{\'a}ndez and Derek K. Jones} } @proceedings {857, title = {Fewer number of gradient directions in diffusion MRI can be counterbalanced with higher sample size: a migraine clinical study}, volume = {4550}, year = {2020}, month = {2020}, abstract = {The effect of changes in the acquisition parameters on Diffusion Tensor Imaging (DTI) has been studied, but for very specific situations. A whole-brain comparison of 54 episodic migraine (EM) and 56 chronic migraine (CM) patients, using diffusion schemes of 61, 40 and 21 gradient orientations, was performed. Statistical comparisons were repeated reducing the sample size until no significant differences were found. Higher number of regions with significant lower axial diffusivity in CM compared to EM were found using 61 gradient directions. With a larger sample size, results with 40 and 21 directions were equivalent to results acquired with 61 directions.}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Santiago Aja-Fern{\'a}ndez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Rodrigo de Luis-Garc{\'\i}a} } @article {892, title = {Gray Matter Structural Alterations in Chronic and Episodic Migraine: A Morphometric Magnetic Resonance Imaging Study}, journal = {Pain Medicine}, volume = {21}, year = {2020}, pages = {2997-3011}, abstract = {

This study evaluates different parameters describing the gray matter structure to analyze differences between healthy controls, patients with episodic migraine, and patients with chronic migraine.Cohort study.Spanish community.Fifty-two healthy controls, 57 episodic migraine patients, and 57 chronic migraine patients were included in the study and underwent T1-weighted magnetic resonance imaging acquisition.Eighty-four cortical and subcortical gray matter regions were extracted, and gray matter volume, cortical curvature, thickness, and surface area values were computed (where applicable). Correlation analysis between clinical features and structural parameters was performed.Statistically significant differences were found between all three groups, generally consisting of increases in cortical curvature and decreases in gray matter volume, cortical thickness, and surface area in migraineurs with respect to healthy controls. Furthermore, differences were also found between chronic and episodic migraine. Significant correlations were found between duration of migraine history and several structural parameters.Migraine is associated with structural alterations in widespread gray matter regions of the brain. Moreover, the results suggest that the pattern of differences between healthy controls and episodic migraine patients is qualitatively different from that occurring between episodic and chronic migraine patients.

}, issn = {1526-2375}, doi = {10.1093/pm/pnaa271}, url = {https://doi.org/10.1093/pm/pnaa271}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Rodr{\'\i}guez, Margarita and Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a} } @article {844, title = {Micro-structure diffusion scalar measures from reduced MRI acquisitions}, journal = {PLOS ONE}, volume = {15}, year = {2020}, month = {2020}, pages = {1-25}, abstract = {

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity. In this work, we propose the so-called {\textquotedblleft}Apparent Measures Using Reduced Acquisitions{\textquotedblright} (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.

}, doi = {10.1371/journal.pone.0229526}, url = {https://doi.org/10.1371/journal.pone.0229526}, author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Maryam Afzali and Molendowska, Malwina and Tomasz Pieciak and Antonio Trist{\'a}n-Vega} } @article {914, title = {Q-space quantitative diffusion MRI measures using a stretched-exponential representation}, journal = {arXiv}, year = {2020}, url = {https://arxiv.org/abs/2009.07376}, author = {Tomasz Pieciak and Maryam Afzali and Fabian Bogusz and Santiago Aja-Fern{\'a}ndez and Derek K. Jones} } @article {903, title = {Response prediction for chronic migraine preventive treatment by gray matter morphometry in magnetic resonance imaging: a pilot study}, journal = {Revista de Neurologia}, volume = {71}, year = {2020}, pages = {399-406}, doi = {10.33588/rn.7111.2020488}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Ant{\'o}n-Juarros, Saray and Rodrigo de Luis-Garc{\'\i}a} } @article {913, title = {Simultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner}, journal = {Scientific Reports}, volume = {10}, year = {2020}, month = {2021}, pages = {1{\textendash}14}, author = {Algarin, Jose M and Diaz-Caballero, Elena and Borreguero, Jose and Galve, Fernando and Grau-Ruiz, Daniel and Rigla, Juan P and Bosch, Ruben and Gonzalez, Jose M and Pallas, Eduardo and Corberan, Miguel and Carlos Gramage and Santiago Aja-Fern{\'a}ndez and Alfonso R{\'\i}os and Jos{\'e} M. Benlloch and Joseba Alonso} } @article {897, title = {Simultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner}, journal = {Scientific Reports volume }, volume = {10}, year = {2020}, month = {2020}, chapter = {21470}, doi = {https://doi.org/10.1038/s41598-020-78456-2}, url = {https://www.nature.com/articles/s41598-020-78456-2}, author = {Jos{\'e} M. Algar{\'\i}n and Elena D{\'\i}az-Caballero and Jos{\'e} Borreguero and Fernando Galve and Daniel Grau-Ruiz and Juan P. Rigla and Rub{\'e}n Bosch and Jos{\'e} M. Gonz{\'a}lez and Eduardo Pall{\'a}s and Miguel Corber{\'a}n and Carlos Gramage and Santiago Aja-Fern{\'a}ndez and Santiago Aja-Fern{\'a}ndez and Jos{\'e} M. Benlloc and Joseba Alonso} } @article {826, title = {Structural connectivity alterations in chronic and episodic migraine: A diffusion magnetic resonance imaging connectomics study}, journal = {Cephalalgia}, volume = {40}, year = {2020}, pages = {367-383}, abstract = {

OBJECTIVE:

To identify possible structural connectivity alterations in patients with episodic and chronic migraine using magnetic resonance imaging data.

METHODS:

Fifty-four episodic migraine, 56 chronic migraine patients and 50 controls underwent T1-weighted and diffusion-weighted magnetic resonance imaging acquisitions. Number of streamlines (trajectories of estimated fiber-tracts), mean fractional anisotropy, axial diffusivity and radial diffusivity were the connectome measures. Correlation analysis between connectome measures and duration and frequency of migraine was performed.

RESULTS:

Higher and lower number of streamlines were found in connections involving regions like the superior frontal gyrus when comparing episodic and chronic migraineurs with controls (p \< .05 false discovery rate). Between the left caudal anterior cingulate and right superior frontal gyri, more streamlines were found in chronic compared to episodic migraine. Higher and lower fractional anisotropy, axial diffusivity, and radial diffusivity were found between migraine groups and controls in connections involving regions like the hippocampus. Lower radial diffusivity and axial diffusivity were found in chronic compared to episodic migraine in connections involving regions like the putamen. In chronic migraine, duration of migraine was positively correlated with fractional anisotropy and axial diffusivity.

CONCLUSIONS:

Structural strengthening of connections involving subcortical regions associated with pain processing and weakening in connections involving cortical regions associated with hyperexcitability may coexist in migraine

}, keywords = {Magnetic resonance imaging (MRI), Migraine, chronic migraine, connectomics, diffusion-weighted imaging, tractography}, doi = {10.1177/0333102419885392}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Rodrigo de Luis-Garc{\'\i}a} } @article {837, title = {White matter changes in chronic and episodic migraine: a diffusion tensor imaging study}, journal = {The Journal of Headache and Pain}, volume = {21}, year = {2020}, pages = {1}, chapter = {1}, abstract = {

BACKGROUND:

White matter alterations have been observed in patients with migraine. However, no microstructural white matter alterations have been found particularly in episodic or chronic migraine patients, and there is limited research focused on the comparison between these two groups of migraine patients.

METHODS:

Fifty-one healthy controls, 55 episodic migraine patients and 57 chronic migraine patients were recruited and underwent brain T1-weighted and diffusion-weighted MRI acquisition. Using Tract-Based Spatial Statistics (TBSS), fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity were compared between the different groups. On the one hand, all migraine patients were compared against healthy controls. On the other hand, patients from each migraine group were compared between them and also against healthy controls. Correlation analysis between clinical features (duration of migraine in years, time from onset of chronic migraine in months, where applicable, and headache and migraine frequency, where applicable) and Diffusion Tensor Imaging measures was performed.

RESULTS:

Fifty healthy controls, 54 episodic migraine and 56 chronic migraine patients were finally included in the analysis. Significant decreased axial diffusivity (p \< .05 false discovery rate and by number of contrasts corrected) was found in chronic migraine compared to episodic migraine in 38 white matter regions from the Johns Hopkins University ICBM-DTI-81 White-Matter Atlas. Significant positive correlation was found between time from onset of chronic migraine and mean fractional anisotropy in the bilateral external capsule, and negative correlation between time from onset of chronic migraine and mean radial diffusivity in the bilateral external capsule.

CONCLUSIONS:

These findings suggest global white matter structural differences between episodic migraine and chronic migraine. Patients with chronic migraine could present axonal integrity impairment in the first months of chronic migraine with respect to episodic migraine patients. White matter changes after the onset of chronic migraine might reflect a set of maladaptive plastic changes.

}, keywords = {Diffusion tensor imaging, Magnetic resonance imaging (MRI), Migraine, Tract-based spatial statistics, chronic migraine}, doi = {10.1186/s10194-019-1071-3}, url = {https://thejournalofheadacheandpain.biomedcentral.com/articles/10.1186/s10194-019-1071-3}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Rodrigo de Luis-Garc{\'\i}a} } @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} } @article {817, title = {Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling}, journal = {Magnetic resonance in medicine}, volume = {81}, year = {2019}, pages = {989{\textendash}1003}, author = {{\'O}scar Pe{\~n}a-Nogales and Zhang, Yuxin and Wang, Xiaoke and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Holmes, James H and Hernando, Diego} } @proceedings {809, title = {Reduced Eddy Current induced image distortions and Peripheral Nerve Stimulation based on the Optimal Diffusion-weighting Gradient Waveform Design (ODGD) formulation}, volume = {3488}, year = {2019}, abstract = {

Diffusion-Weighted MRI (DW-MRI) often suffers from signal attenuation due to long TE, motion-related artefacts, dephasing due to concomitant gradients (CGs), and image distortions due to eddy currents (ECs). Further, the application of rapidly switching gradients may cause peripheral nerve stimulation (PNS). These challenges hinder the progress, application and interpretability of DW-MRI. Therefore, based on the Optimized Diffusion-weighting Gradient waveforms Design (ODGD) formulation, in this work we design optimal (minimum TE) nth-order moment-nulling diffusion-weighting gradient waveforms with or without CG-nulling able to reduce EC induced distortions and PNS-effects. We assessed the feasibility of these waveforms in simulations and phantom experiments.

}, author = {{\'O}scar Pe{\~n}a-Nogales and Yuxin Zhang and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and James H. Holmes and Diego Hernando} } @inbook {818, title = {Return-to-Axis Probability Calculation from Single-Shell Acquisitions}, booktitle = {Computational Diffusion MRI}, year = {2019}, pages = {29-41}, publisher = {Springer}, organization = {Springer}, isbn = {978-3-030-05830-2}, doi = {10.1007/978-3-030-05831-9_3}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Molendowska, Malwina and Tomasz Pieciak and Luis-Garc{\'\i}a, Rodrigo} } @conference {815, title = {Single-Shell Return-to-the-Origin Probability Diffusion Mri Measure Under a Non-Stationary Rician Distributed Noise}, booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, year = {2019}, publisher = {IEEE}, organization = {IEEE}, author = {Tomasz Pieciak and Bogusz, Fabian and Antonio Trist{\'a}n-Vega and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez} } @conference {821, title = {White Matter Alterations in Chronic Migraine: A Diffusion Tensor Imaging and Structural Connectivity Study}, booktitle = {19th International Headache Congress International Headache Society}, year = {2019}, month = {2019}, publisher = {Cephalalgia}, organization = {Cephalalgia}, address = {Dublin, Ireland}, abstract = {

Objective: White matter alterations have been observed in patients with migraine. However, no microstructural white matter alterations have been found particularly in Episodic Migraine (EM) with respect to Chronic Migraine (CM) patients. In this study, we investigated whether there are significant differences between EM and CM, and between these groups and healthy controls, using diffusion Magnetic Resonance Imaging (dMRI) data.
Methods: We acquired high-resolution 3D brain T1-weighted and dMRI from 51 Healthy Controls (HC), 55 EM patients and 57 CM patients. Using Tract-Based Spatial Statistics, we compared Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD) between the different groups. We also obtained structural connectome matrices for each subject employing both dMRI and T1-weighted acquisitions. Number of streamlines, mean FA and mean AD for each white matter connection were compared between the three groups.
Results: Significant decreased AD (p \<.05 Family Wise Error corrected and volume \>30 mm3) were found in CM compared to EM in 38 white matter regions. Significant differences in the number of streamlines were found in 18 connections from the connectome when comparing migraine patients with healthy controls (p \<.05 False Discovery Rate corrected); significant differences were also found between CM and EM in one of these connections. Furthermore, significant differences in FA and AD were found in three and four connections from the connectome respectively (p \<.05 False Discovery Rate corrected); significant differences were also found between CM and EM in two of AD connections.
Conclusion: Our findings suggest global white matter structural differences between EM and CM, and structural connectivity alterations in migraine patients with respect to healthy controls, and in CM compared to EM.
Disclosure of Interest: None Declared.

}, doi = {https://doi.org/10.1177/0333102419859835}, url = {https://journals.sagepub.com/doi/full/10.1177/0333102419859835}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez and Rodr{\'\i}guez, Margarita and Rodrigo de Luis-Garc{\'\i}a} } @conference {752, title = {Bias Correction for Non-Stationary Noise Filtering in MRI}, booktitle = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI)}, year = {2018}, month = {2018}, address = {Washington DC}, author = {Tomasz Pieciak and I{\~n}aki Rabanillo-Viloria and Santiago Aja-Fern{\'a}ndez} } @article {736, title = {Exact Calculation of Noise Maps and g-Factor in GRAPPA using a k-space Analysis}, journal = {IEEE Transactions on Medical Imaging}, volume = {37}, year = {2018}, month = {2018}, pages = {480-490}, chapter = {480}, issn = {0278-0062}, doi = {10.1109/TMI.2017.2760921}, author = {I{\~n}aki Rabanillo-Viloria and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Diego Hernando} } @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 {733, title = {Joint Groupwise Registration and ADC Estimation in the Liver using a B-Value Weighted Metric}, journal = {Magnetic Resonance Imaging}, volume = {46}, year = {2018}, month = {2018}, pages = {1-8}, type = {Original Contribution}, chapter = {1}, abstract = {

The purpose of this work is to develop a groupwise elastic multimodal registration algorithm for robust ADC estimation in the liver on multiple breath hold diffusion weighted images.

We introduce a joint formulation to simultaneously solve both the registration and the estimation problems. In order to avoid non-reliable transformations and undesirable noise amplification, we have included appropriate smoothness constraints for both problems. Our metric incorporates the ADC estimation residuals, which are inversely weighted according to the signal content in each diffusion weighted image.

Results show that the joint formulation provides a statistically significant improvement in the accuracy of the ADC estimates. Reproducibility has also been measured on real data in terms of the distribution of ADC differences obtained from different\ b-values\ subsets.\ 

The proposed algorithm is able to effectively deal with both the presence of motion and the geometric distortions, increasing accuracy and reproducibility in diffusion parameters estimation.

}, keywords = {ADC Estimation, Diffusion Weighted Imaging, Groupwise Registration, Joint Optimization, Residual Minimization Metric}, doi = {https://doi.org/10.1016/j.mri.2017.10.002}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X17302187}, author = {Santiago Sanz-Est{\'e}banez and I{\~n}aki Rabanillo-Viloria and J Royuela-del-Val and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {784, title = {Joint Image Reconstruction and Phase Corruption Maps Estimation in Multi-Shot Echo Planar Imaging}, booktitle = {MICCAI}, year = {2018}, month = {09/2018}, publisher = {MICCAI}, organization = {MICCAI}, address = {Granada}, abstract = {

Multishot echo-planar imaging is a common strategy in diffusion Magnetic Resonance Imaging to reduce the artifacts caused by the long echo-trains in single-shot acquisitions. However, it su ers from shot-to-shot phase discrepancies associated to subject motion, which can notably degrade the quality of the reconstructed image. Consequently, some
type of motion-induced phases error correction needs to be incorporated into the reconstruction process. In this paper we focus on ridig motion induced errors, which have proved to corrupt the shots with linear phase maps. By incorporating this prior knowledge, we propose a maximum likelihood formulation that estimates both the parameters that characterize the linear phase maps and the reconstructed image. In order to make the problem tractable, we follow a greedy iterative procedure that alternates between the estimation of each of them. Simulation data are used to illustrate the performance of the method against state-of-the-art alternatives.

}, author = {I{\~n}aki Rabanillo-Viloria and Santiago Sanz-Est{\'e}banez and Santiago Aja-Fern{\'a}ndez and Joseph V. Hajnal and Carlos Alberola-L{\'o}pez and Lucilio Cordero-Grande} } @inbook {756, title = {Nonlinear despeckle filtering}, booktitle = {Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video}, year = {2018}, publisher = {IET}, organization = {IET}, chapter = {8}, issn = {978-1-78561-290-9}, author = {Santiago Aja-Fern{\'a}ndez and Gabriel Ramos-Llord{\'e}n and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @proceedings {760, title = {Optimal Diffusion-weighting Gradient Waveform Design (ODGD): Formulation and Experimental Validation}, year = {2018}, pages = {685}, address = {Paris, France}, author = {{\'O}scar Pe{\~n}a-Nogales and Yuxin Zhang and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and James H. Holmes and Diego Hernando} } @article {793, title = {Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling}, journal = {Magnetic resonance in medicine}, year = {2018}, author = {{\'O}scar Pe{\~n}a-Nogales and Zhang, Yuxin and Wang, Xiaoke and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Holmes, James H and Hernando, Diego} } @proceedings {759, title = {Return-to-the-origin probability calculation in single shell acquisitions}, year = {2018}, pages = {1414}, address = {Paris, France}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Malwina Molendowska and Tomasz Pieciak and Rodrigo de Luis-Garc{\'\i}a} } @article {779, title = {Robust Estimation of the Apparent Diffusion Coefficient Invariant to Acquisition Noise and Physiological Motion}, journal = {Magnetic Resonance Imaging}, volume = {53}, year = {2018}, pages = {123-133}, abstract = {

In this work we have proposed a methodology for the estimation of the apparent diffusion coefficient in the body from multiple breath hold diffusion weighted images, which is robust to two preeminent confounding factors: noise and motion during acquisition. We have extended a method for the joint groupwise multimodal registration and apparent diffusion coefficient estimation, previously proposed by the authors, in order to correct the bias that arises from the non-Gaussianity of the data and the registration procedure. Results show that the proposed methodology provides a statistically significant improvement both in robustness for displacement fields calculation and in terms of accuracy for the apparent diffusion coefficient estimation as compared with traditional sequential approaches. Reproducibility has also been measured on real data in terms of the distribution of apparent diffusion coefficient differences obtained from different b-values subsets. Our proposal has shown to be able to effectively correct the estimation bias by introducing additional computationally light procedures to the original method, thus providing robust apparent diffusion coefficient maps in the liver and allowing an accurate and reproducible analysis of the tissue.

}, keywords = {Acquisition Noise, Apparent Diffusion Coefficient, Diffusion Weighted Imaging, Multimodal Groupwise Registration, Patient Movement Correction}, doi = {https://doi.org/10.1016/j.mri.2018.07.005}, url = {https://www.sciencedirect.com/science/article/pii/S0730725X18300687}, author = {Santiago Sanz-Est{\'e}banez and Tomasz Pieciak and Carlos Alberola-L{\'o}pez and Santiago Aja-Fern{\'a}ndez} } @article {781, title = {Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers}, journal = {Magnetic Resonance Imaging}, volume = {54}, year = {2018}, month = {2018}, pages = {194 - 213}, issn = {0730-725X}, doi = {https://doi.org/10.1016/j.mri.2018.03.001}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X18300262}, author = {Santiago Aja-Fern{\'a}ndez and Tomasz Pieciak and Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Vicente Molina and Rodrigo de Luis-Garc{\'\i}a} } @inbook {757, title = {Techniques for tracking: image registration}, booktitle = {Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video}, year = {2018}, publisher = {IET}, organization = {IET}, chapter = {15}, issn = {978-1-78561-290-9}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {740, title = {ADC-Weighted Joint Registration-Estimation for Cardiac Diffusion Magnetic Resonance Imaging}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2017}, month = {12/2017}, abstract = {

The purpose of this work is to develop a method for the groupwise registration of diffusion weighted datasets of the heart which automatically provide smooth Apparent Diffusion Coefficient (ADC) estimations, by making use of a novel multimodal scheme. To this
end, we have introduced a joint methodology that simultaneously performs both the alignment of the images and the ADC estimation. In order to promote diffeomorphic transformations and to avoid undesirable noise amplification, we have included appropriate
smoothness constraints for both problems under the same formulation. The implemented multimodal registration metric incorporates the ADC estimation residuals, which are inversely weighted with the b-values to balance the influence of the signal level for each diffusion weighted image. Results show that the joint formulation provides more robust and precise ADC estimations and a significant improvement in the overlap of the contour
of manual delineations along the different b-values. The proposed algorithm is able to effectively deal with the presence of both physiological motion and inherent contrast variability for the different b-value images, increasing accuracy and robustness of the estimation of diffusion parameters for cardiac imaging.

}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and Jordi Broncano-Cabrero and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @proceedings {723, title = {Determination of the optimal set of b-values for ADC mapping under a Rician noise assumption}, year = {2017}, pages = {3341}, address = {Honolulu, HI, USA}, abstract = {

Mapping of the apparent diffusion coefficient (ADC), estimated from a set of diffusion-weighted (DW) images acquired with different b-values, often suffers from low SNR, which can introduce large variance in ADC maps. Unfortunately, there is no consensus on the optimal b-values to maximize the noise performance of ADC map. In this work, we determine the optimal b-values to maximize the noise performance of ADC mapping by using a Cram{\'e}r-Rao Lower Bound (CRLB) approach under realistic noise assumptions. The strong agreement between the CRLB-based analysis, Monte-Carlo simulations, and ADC phantom experiment, suggests the utility of this approach to optimize DW-MRI acquisitions.

}, author = {{\'O}scar Pe{\~n}a-Nogales and Diego Hernando and Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a} } @conference {689, title = {Effect of sampling on the estimation of the apparent coefficient of diffusion in MRI}, booktitle = {ICASSP 2017}, year = {2017}, month = {2017}, publisher = {IEEE signal processing Society}, organization = {IEEE signal processing Society}, address = {New Orleans, LA}, author = {Santiago Aja-Fern{\'a}ndez and {\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a} } @conference {700, title = {Groupwise Non-Rigid Registration on Multiparametric Abdominal DWI Acquisitions for Robust ADC Estimation: Comparison with Pairwise Approaches and Different Multimodal Metrics}, booktitle = {International Symposium on Biomedical Imaging: From Nano to Macro (ISBI2017)}, year = {2017}, month = {2017}, address = {Melbourne, Australia}, abstract = {

Registration of diffusion weighted datasets remains a challenging\ task in the process of quantifying diffusion indexes.\ Respiratory and cardiac motion, as well as echo-planar characteristic\ geometric distortions, may greatly limit accuracy on\ parameter estimation, specially for the liver. This work proposes\ a methodology for the non-rigid registration of multiparametric\ abdominal diffusion weighted imaging by using\ different well-known metrics under the groupwise paradigm.\ A three-stage validation of the methodology is carried out on\ a computational diffusion phantom, a watery solution phantom\ and a set of voluntary patients. Diffusion estimation\ accuracy has been directly calculated on the computational\ phantom and indirectly by means of a residual analysis on\ the real data. On the other hand, effectiveness in distortion\ correction has been measured on the phantom. Results have\ shown statistical significant improvements compared to pairwise\ registration being able to cope with elastic deformations.

}, author = {Santiago Sanz-Est{\'e}banez and {\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {666, title = {Non-Stationary Rician Noise Estimation in Parallel MRI using a Single Image: a Variance-Stabilizing Approach}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {39}, year = {2017}, month = {2017}, pages = {2015-2029}, chapter = {2015}, abstract = {

Parallel magnetic resonance imaging (pMRI) techniques have gained a great importance both in research and clinical communities recently since they considerably accelerate the image acquisition process. However, the image reconstruction algorithms needed to correct the subsampling artifacts affect the nature of noise, i.e. it becomes non-stationary. Some methods have been proposed in the literature dealing with the non-stationary noise in pMRI. However, their performance depends on information not usually available such as multiple acquisitions, receiver noise matrices, sensitivity coil profiles, reconstruction coefficients, or even biophysical models of the data. Besides, some methods show an undesirable granular pattern on the estimates as a side effect of local estimation. Finally, some methods make strong assumptions that just hold in the case of high signal-to-noise ratio (SNR), which limits their usability in real scenarios. We propose a new automatic noise estimation technique for non-stationary Rician noise that overcomes the aforementioned drawbacks. Its effectiveness is due to the derivation of a variance-stabilizing transformation designed to deal with any SNR. The method was compared to the main state-of-the-art methods in synthetic and real scenarios. Numerical results confirm the robustness of the method and its better performance for the whole range of SNRs.

}, issn = {0162-8828}, doi = {10.1109/TPAMI.2016.2625789}, author = {Tomasz Pieciak and Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas S{\'a}nchez-Ferrero} } @proceedings {724, title = {Optimal design of motion-compensated diffusion gradient waveforms }, year = {2017}, pages = {3340}, address = {Honolulu, HI, USA}, abstract = {

Diffusion-Weighted MRI (DW-MRI) often suffers from motion-related artifacts in organs that experience physiological motion. Importantly, organ motion during the application of diffusion gradients results in signal losses, which complicate image interpretation and bias quantitative measures. Motion-compensated gradient designs have been proposed, however they typically result in substantially lower b-values or severe concomitant gradient effects. In this work, we develop an approach for design of first- and second-order motion-compensated gradient waveforms based on a b-value maximization formulation including concomitant gradient nulling, and we compare it to existing techniques. The proposed design provides optimized b-values with motion compensation and concomitant gradient nulling.

}, author = {{\'O}scar Pe{\~n}a-Nogales and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Yuxin Zhang and James H. Holmes and Diego Hernando} } @conference {687, title = {Harmonic Auto-Regularization for Non Rigid Groupwise Registration in Cardiac Magnetic Resonance Imaging.}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica 2016}, year = {2016}, month = {11/2016}, address = {Valencia, Spain}, abstract = {

In this paper we present a new approach for non rigid groupwise registration of cardiac magnetic resonance images by means of free-form deformations, imposing a prior harmonic deformation assumption. The procedure proposes a primal-dual framework for solving an equality constrained minimization problem, which allows an automatic estimate of the trade-off between image fidelity and the Laplacian smoothness terms for each iteration. The method has been applied to both a 4D extended cardio-torso phantom and to a set of voluntary patients. The accuracy of the method has been measured for the synthetic experiment as the difference in modulus between the estimated displacement field and the ground truth; as for the real data, we have calculated the Dice coefficient between the contour manual delineations provided by two cardiologists at end systolic phase and those provided by them at end diastolic phase and, consequently propagated by the registration algorithm to the systolic instant. The automatic procedure turns out to be competitive in motion compensation with other methods even though their parameters have been previously set for optimal performance in different scenarios.

}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and T. Sevilla-Ruiz and Revilla-Orodea, A. and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @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} } @conference {615, title = {Spatial and Spectral Anisotropy in HARP Images: An Automated Approach}, booktitle = {International Symposium on Biomedical Imaging: From Nano to Macro (ISBI2016)}, year = {2016}, month = {2016}, address = {Prague, Czech Republic}, abstract = {

Strain and related tensors play a major role in cardiac function\ characterization, so correct estimation of the local phase\ in tagged images is crucial for quantitative myocardial motion\ studies. We propose an Harmonic Phase related procedure\ that is adaptive in the spatial and the spectral domains: as for\ the former, we use an angled-steered analysis window prior to\ the Fourier Transform; as for the latter, the bandpass filter is\ also angle-adaptive. Both of them are narrow in the modulation\ direction and wide in the orthogonal direction.

Moreover,\ no parameters are manually set since their values are partially\ based on the information available at the DICOM headers and\ additional information is estimated from data. The procedure
is tested in terms of accuracy (on synthetic data) and reproducibility\ (on real data) of the deformation gradient tensor,\ measured by means of the distribution of the Frobenius norm\ differences between two tensor datasets.

}, keywords = {Anisotropic Gaussian Window, Automatic Band-Pass Filtering, HARmonic Phase, Strain Tensor, Tagged Magnetic Resonance Imaging, Thresholding}, author = {Santiago Sanz-Est{\'e}banez and Lucilio Cordero-Grande and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @article {654, title = {Spatially-variant noise filtering in Magnetic Resonance Imaging: A Consensus-based approach}, journal = {Knowledge-Based Systems}, year = {2016}, month = {2016}, doi = {http://dx.doi.org/10.1016/j.knosys.2016.05.053}, url = {http://www.sciencedirect.com/science/article/pii/S0950705116301575}, author = {Luis Gonz{\'a}lez-Jaime and Gonzalo Vegas-S{\'a}nchez-Ferrero and Etienne E. Kerre and Santiago Aja-Fern{\'a}ndez} } @book {660, title = {Statistical Analysis of Noise in MRI. Modeling, Filtering and Estimation}, year = {2016}, pages = {327}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Switzerland}, abstract = {

This unique text presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques. Features: provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques; describes noise and signal estimation for MRI from a statistical signal processing perspective; surveys the different methods to remove noise in MRI acquisitions from a practical point of view; reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions; examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal; includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets.

}, issn = {978-3-319-39933-1}, doi = {http://dx.doi.org/10.1007/978-3-319-39934-8}, url = {http://link.springer.com/book/10.1007/978-3-319-39934-8}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @conference {655, title = {Variance Stabilization of Noncentral-Chi Data: Application to Noise Estimation in MRI}, booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 2016}, year = {2016}, month = {2016}, address = {Prague, Czech Republic}, author = {Tomasz Pieciak and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {448, title = {Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images}, journal = {IEEE Transactions on image processing}, volume = {24}, year = {2015}, chapter = {345}, doi = {http://dx.doi.org/10.1109/TIP.2014.2371244}, author = {Gabriel Ramos-Llorden and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Santiago Aja-Fern{\'a}ndez} } @article {de2014attention, title = {Attention Deficit/Hyperactivity Disorder and Medication with Stimulants in Young Children: A DTI Study}, journal = {Progress in Neuro-Psychopharmacology and Biological Psychiatry}, volume = {57}, year = {2015}, publisher = {Elsevier}, chapter = {176}, doi = {http://dx.doi.org/10.1016/j.pnpbp.2014.10.014}, author = {Rodrigo de Luis-Garc{\'\i}a and Cab{\'u}s-Pi{\~n}ol, Gemma and Imaz-Roncero, Carlos and Daniel Argibay-Qui{\~n}ones and Gonzalo Barrio-Arranz and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @proceedings {543, title = {Blind Estimation of Spatially Variant Noise in GRAPPA MRI}, year = {2015}, pages = {SuAT7.4}, abstract = {

The reconstruction process in multiple coil MRI scanners makes the noise features in the final magnitude image become non-stationary, i.e. the variance of noise becomes position-dependent. Therefore, most noise estimators proposed in the literature cannot be used in multiple-coil acquisitions. This effect is augmented when parallel imaging methods, such as GRAPPA, are used to increase the acquisition rate.

In this work we propose a new technique that allows the estimation of the spatially variant maps of noise from the GRAPPA reconstructed signal when only one single image is available and no additional information is provided. Other estimators in the literature need extra information that is not always available, which has supposed an important limitation in the usage of noise models for GRAPPA. The proposed approach uses a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is estimated by a low pass filtering. The noise term is obtained via prior wavelet decomposition. Results in real and synthetic experiments evidence the suitability of the simplification used and the good performance of the proposed methodology.

}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @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 {541, title = {A Maximum Likelihood Approach to Diffeomorphic Speckle Tracking for 3D Strain Estimation in Echocardiography}, journal = {Medical Image Analysis}, year = {2015}, pages = {-}, abstract = {

Abstract The strain and strain-rate measures are commonly used for the analysis and assessment of regional myocardial function. In echocardiography (EC), the strain analysis became possible using Tissue Doppler Imaging (TDI). Unfortunately, this modality shows an important limitation: the angle between the myocardial movement and the ultrasound beam should be small to provide reliable measures. This constraint makes it difficult to provide strain measures of the entire myocardium. Alternative non-Doppler techniques such as Speckle Tracking (ST) can provide strain measures without angle constraints. However, the spatial resolution and noisy appearance of speckle still make the strain estimation a challenging task in EC. Several maximum likelihood approaches have been proposed to statistically characterize the behavior of speckle, which results in a better performance of speckle tracking. However, those models do not consider common transformations to achieve the final B-mode image (e.g. interpolation). This paper proposes a new maximum likelihood approach for speckle tracking which effectively characterizes speckle of the final B-mode image. Its formulation provides a diffeomorphic scheme than can be efficiently optimized with a second-order method. The novelty of the method is threefold: First, the statistical characterization of speckle generalizes conventional speckle models (Rayleigh, Nakagami and Gamma) to a more versatile model for real data. Second, the formulation includes local correlation to increase the efficiency of frame-to-frame speckle tracking. Third, a probabilistic myocardial tissue characterization is used to automatically identify more reliable myocardial motions. The accuracy and agreement assessment was evaluated in a set of 16 synthetic image sequences for three different scenarios: normal, acute ischemia and acute dyssynchrony. The proposed method was compared to six speckle tracking methods. Results revealed that the proposed method is the most accurate method to measure the motion and strain with an average median motion error of 0.42\ mm and a median strain error of 2.0 {\textpm} 0.9\%, 2.1 {\textpm} 1.3\% and 7.1 {\textpm} 4.9\% for circumferential, longitudinal and radial strain respectively. It also showed its capability to identify abnormal segments with reduced cardiac function and timing differences for the dyssynchrony cases. These results indicate that the proposed diffeomorphic speckle tracking method provides robust and accurate motion and strain estimation.

}, doi = {http://dx.doi.org/10.1016/j.media.2015.05.001}, url = {http://www.sciencedirect.com/science/article/pii/S1361841515000687}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Johan G. Bosch and Santiago Aja-Fern{\'a}ndez} } @article {542, title = {Probabilistic Tissue Characterization for Ultrasound Images}, journal = {Insight Journal}, year = {2015}, abstract = {

This document describes the derivation of the mixture models commonly used in the literature to describe the probabilistic nature of speckle: The Gaussian Mixture Model, the Rayleigh Mixture Model, the Gamma Mixture Model and the Generalized Gamma Mixture Model. New algorithms were implemented using the Insight Toolkit
ITK for tissue characterization by means of a mixture model.


The source code is composed of a set of reusable ITK filters and classes. In addition to an overview of our implementation, we provide the source code, input data, parameters and output data that the authors used for validating the different probabilistic tissue characterization variants described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

}, url = {http://www.insight-journal.org/browse/publication/955}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {513, title = {Spatially variant noise estimation in MRI: A homomorphic approach}, journal = {Medical Image Analysis}, volume = {20}, year = {2015}, pages = {184 - 197}, doi = {http://dx.doi.org/10.1016/j.media.2014.11.005}, author = {Santiago Aja-Fern{\'a}ndez and Tomasz Pieciak and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {568, title = {Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization}, journal = {PLoS ONE}, volume = {10}, year = {2015}, pages = {e0138910}, doi = {10.1371/journal.pone.0138910}, url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0138910}, author = {Canales-Rodr{\'\i}guez, Erick J. and Daducci, Alessandro and Stamatios N. Sotiropoulos and Caruyer, Emmanuel and Santiago Aja-Fern{\'a}ndez and Radua, Joaquim and Yurramendi Mendizabal, Jes{\'u}s M. and Iturria-Medina, Yasser and Melie-Garc{\'\i}a, Lester and Alem{\'a}n-G{\'o}mez, Yasser and J-P Thiran and Sarr{\'o}, Salvador and Pomarol-Clotet, Edith and Salvador, Raymond} } @article {534, title = {A local fuzzy thresholding methodology for multiregion image segmentation}, journal = {Knowledge-Based Systems}, volume = {83}, year = {2015}, month = {07/2015}, pages = {1-12}, abstract = {

Abstract Thresholding is a direct and simple approach to extract different regions from an image. In its basic formulation, thresholding searches for a global value that maximizes the separation between output classes. The use of a single hard threshold value is precisely the source of important segmentation errors in many scenarios like noisy images or uneven illumination. If no connectivity or closed objects are considered, the method is prone to produce isolated pixels. In this paper a new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise. It is based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision. The starting point of the technique is the definition of the output centroids using a clustering method compatible with most thresholding techniques in the literature. The method makes use of the spatial information through a local aggregation step where the membership degree of each pixel is modified by local information that takes into account the memberships of the surrounding pixels. This makes the method robust to noise and artifacts. The general formulation of the proposed methodology allows the design of spatial aggregations for multiple applications, including the possibility of including heuristic information via a fuzzy inference rule base.

}, issn = {0950-7051}, doi = {http://dx.doi.org/10.1016/j.knosys.2015.02.029}, url = {http://www.sciencedirect.com/science/article/pii/S095070511500129X}, author = {Santiago Aja-Fern{\'a}ndez and Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {curiale2014fully, title = {Fully Automatic Detection of Salient Features in 3-D Transesophageal Images}, journal = {Ultrasound in medicine \& biology}, volume = {40}, year = {2014}, month = {07/2014}, pages = {2868-2884}, publisher = {Elsevier}, chapter = {2868}, author = {Ariel H. Curiale and Haak, Alexander and Gonzalo Vegas-S{\'a}nchez-Ferrero and Ren, Ben and Santiago Aja-Fern{\'a}ndez and Johan G. Bosch} } @article {vegas2014gamma, title = {Gamma mixture classifier for plaque detection in intravascular ultrasonic images}, journal = {Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions on}, volume = {61}, number = {1}, year = {2014}, pages = {44{\textendash}61}, publisher = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Seabra, Jose and Rodriguez-Leor, Oriol and Serrano-Vida, Angel and Santiago Aja-Fern{\'a}ndez and Palencia, C and Marcos Martin-Fernandez and Sanches, J} } @article {aja2014noise, title = {Noise estimation in parallel MRI: GRAPPA and SENSE}, journal = {Magnetic resonance imaging}, volume = {32}, number = {3}, year = {2014}, pages = {281{\textendash}290}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @article {canales2014spherical, title = {Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and total variation spatial regularization}, journal = {arXiv preprint arXiv:1410.6353}, year = {2014}, author = {Canales-Rodr{\'\i}guez, Erick J and Daducci, Alessandro and Stamatios N. Sotiropoulos and Caruyer, Emmanuel and Santiago Aja-Fern{\'a}ndez and Radua, Joaquim and Mendizabal, Yosu Yurramendi and Iturria-Medina, Yasser and Melie-Garc{\'\i}a, Lester and Alem{\'a}n-G{\'o}mez, Yasser} } @article {aja2014statistical, title = {Statistical Noise Analysis in SENSE Parallel MRI}, journal = {arXiv preprint arXiv:1402.4067}, year = {2014}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @article {de2014white, title = {White matter abnormalities in chronic migraine patients are located in anterior corpus callosum: study using a new automatic tractography selection method}, journal = {EUROPEAN JOURNAL OF NEUROLOGY}, volume = {21}, year = {2014}, pages = {51{\textendash}51}, publisher = {WILEY-BLACKWELL 111 RIVER ST, HOBOKEN 07030-5774, NJ USA}, author = {De la Cruz, C and {\'A}ngel L. Guerrero and Penas, ML and Daniel Argibay-Qui{\~n}ones and Jose M Sierra and Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a} } @conference {vegas2013anisotropic, title = {Anisotropic diffusion filtering for correlated multiple-coil MRI}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {2956{\textendash}2959}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Gabriel Ramos-Llorden and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {gonzalez2013applying, title = {Applying a parametric approach for the task of nonstationary noise removal with missing information}, booktitle = {Computational Cybernetics (ICCC), 2013 IEEE 9th International Conference on}, year = {2013}, pages = {23{\textendash}28}, publisher = {IEEE}, organization = {IEEE}, author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {de2013atlas, title = {Atlas-based segmentation of white matter structures from DTI using tensor invariants and orientation}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {503{\textendash}506}, publisher = {IEEE}, organization = {IEEE}, author = {Rodrigo de Luis-Garc{\'\i}a and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {aja2013effective, title = {Effective noise estimation and filtering from correlated multiple-coil MR data}, journal = {Magnetic resonance imaging}, volume = {31}, number = {2}, year = {2013}, pages = {272{\textendash}285}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and V{\'e}ronique Brion and Antonio Trist{\'a}n-Vega} } @proceedings {ramos2014fast, title = {Fast Anisotropic Speckle Filter for Ultrasound Medical Images}, year = {2013}, pages = {253{\textendash}256}, publisher = {Springer International Publishing}, author = {Gabriel Ramos-Llorden and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @article {cordero2013groupwise, title = {Groupwise elastic registration by a new sparsity-promoting metric: application to the alignment of cardiac magnetic resonance perfusion images}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, volume = {35}, number = {11}, year = {2013}, pages = {2638{\textendash}2650}, publisher = {IEEE}, author = {Lucilio Cordero-Grande and S. Merino-Caviedes and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @inbook {garcia2013homeomorphic, title = {Homeomorphic Geometrical Transform for Collision Response in Surgical Simulation}, booktitle = {Pattern Recognition and Image Analysis}, year = {2013}, pages = {433{\textendash}440}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {tristan2013merging, title = {Merging squared-magnitude approaches to DWI denoising: An adaptive Wiener filter tuned to the anatomical contents of the image}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {507{\textendash}510}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and V{\'e}ronique Brion and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {brion2013noise, title = {Noise correction for HARDI and HYDI data obtained with multi-channel coils and Sum of Squares reconstruction: An anisotropic extension of the LMMSE}, journal = {Magnetic resonance imaging}, volume = {31}, number = {8}, year = {2013}, pages = {1360{\textendash}1371}, publisher = {Elsevier}, author = {V{\'e}ronique Brion and Poupon, Cyril and Riff, Olivier and Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Mangin, Jean-Fran{\c c}ois and Le Bihan, Denis and Poupon, Fabrice} } @conference {aja2013noise, title = {Noise estimation in magnetic resonance SENSE reconstructed data}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {1104{\textendash}1107}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega} } @inbook {gonzalez2013parametric, title = {Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering}, booktitle = {Pattern Recognition and Image Analysis}, year = {2013}, pages = {358{\textendash}365}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {aja2013quantitative, title = {Quantitative Diffusion MRI in the Presence of Noise: Effects of Filtering and Fitting Technique}, booktitle = {Quantitative Medical Imaging}, year = {2013}, pages = {QTu2G{\textendash}2}, publisher = {Optical Society of America}, organization = {Optical Society of America}, author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez and Hernando, Diego} } @conference {aja2013robust, title = {Robust estimation of MRI myocardial perfusion parameters}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {4382{\textendash}4385}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez} } @inbook {curiale2013speckle, title = {Speckle tracking in interpolated echocardiography to estimate heart motion}, booktitle = {Functional Imaging and Modeling of the Heart}, year = {2013}, pages = {325{\textendash}333}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {curiale2013strain, title = {Strain rate tensor estimation from echocardiography for quantitative assessment of functional mitral regurgitation}, booktitle = {Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on}, year = {2013}, pages = {788{\textendash}791}, publisher = {IEEE}, organization = {IEEE}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Teresa P{\'e}rez-Sanz and Santiago Aja-Fern{\'a}ndez} } @article {cordero2013magnetic, title = {A magnetic resonance software simulator for the evaluation of myocardial deformation estimation}, journal = {Medical engineering \& physics}, volume = {35}, number = {9}, year = {2013}, pages = {1331{\textendash}1340}, publisher = {Elsevier}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {vegas2012anisotropic, title = {Anisotropic LMMSE denoising of MRI based on statistical tissue models}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {1519{\textendash}1522}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Cesar Palencia and Deriche, Rachid} } @conference {tristan2012deblurring, title = {Deblurring of probabilistic ODFs in quantitative diffusion MRI}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {932{\textendash}935}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {tristan2012efficient, title = {Efficient and robust nonlocal means denoising of MR data based on salient features matching}, journal = {Computer methods and programs in biomedicine}, volume = {105}, number = {2}, year = {2012}, pages = {131{\textendash}144}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {vegas2012generalized, title = {A Generalized Gamma Mixture Model for Ultrasonic Tissue Characterization}, journal = {Computational and mathematical methods in medicine}, volume = {2012}, year = {2012}, publisher = {Hindawi Publishing Corporation}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Cesar Palencia and Marcos Martin-Fernandez} } @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} } @article {tristan2012least, title = {Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals}, journal = {NeuroImage}, volume = {59}, number = {4}, year = {2012}, pages = {4032{\textendash}4043}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @conference {aja2012mri, title = {A MRI phantom for cardiac perfusion simulation}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {638{\textendash}641}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Lucilio Cordero-Grande and Carlos Alberola-Lopez} } @article {casaseca2012optimal, title = {Optimal real-time estimation in diffusion tensor imaging}, journal = {Magnetic resonance imaging}, volume = {30}, number = {4}, year = {2012}, pages = {506{\textendash}517}, publisher = {Elsevier}, author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin and Raul San Jose-Estepar} } @article {vegas2012direct, title = {A direct calculation of moments of the sample variance}, journal = {Mathematics and Computers in Simulation}, volume = {82}, number = {5}, year = {2012}, pages = {790{\textendash}804}, publisher = {North-Holland}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Palencia, C{\'e}sar} } @conference {405, title = {Cuantificaci{\'o}n de la insuficiencia mitral funcional mediante el esfuerzo y la velocidad del miocardio}, booktitle = {XXIX Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2011}, address = {Centro de Cirug{\'\i}a de M{\'\i}nima Invasi{\'o}n Jes{\'u}s Us{\'o}n}, author = {Ariel H. Curiale and S{\'a}nchez-Ferrero, G Vegas and Teresa P{\'e}rez-Sanz and Santiago Aja-Fern{\'a}ndez} } @proceedings {cordero2011groupwise, title = {Groupwise myocardial alignment in magnetic resonance perfusion sequences}, year = {2011}, pages = {437{\textendash}440}, author = {Lucilio Cordero-Grande and S. Merino-Caviedes and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {aja2011noise, title = {Noise estimation in MR GRAPPA reconstructed data}, booktitle = {Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on}, year = {2011}, pages = {1815{\textendash}1818}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @inbook {brion2011parallel, title = {Parallel MRI noise correction: an extension of the LMMSE to non central $\chi$ distributions}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2011}, year = {2011}, pages = {226{\textendash}233}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {V{\'e}ronique Brion and Poupon, Cyril and Riff, Olivier and Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Mangin, Jean-Fran{\c c}ois and Le Bihan, Denis and Poupon, Fabrice} } @conference {vegas2011realistic, title = {Realistic log-compressed law for ultrasound image recovery}, booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on}, year = {2011}, pages = {2029{\textendash}2032}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Lucilio Cordero-Grande and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Cesar Palencia} } @proceedings {barrio2011saturn2, title = {SATURN2: An Improved Software Tool for Neuroimaging Analysis}, year = {2011}, author = {Gonzalo Barrio-Arranz and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Marcos Martin-Fernandez} } @article {aja2011statistical, title = {Statistical noise analysis in GRAPPA using a parametrized noncentral Chi approximation model}, journal = {Magnetic resonance in medicine}, volume = {65}, number = {4}, year = {2011}, pages = {1195{\textendash}1206}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and W Scott Hoge} } @article {aja2010background, title = {About the background distribution in MR data: a local variance study}, journal = {Magnetic resonance imaging}, volume = {28}, number = {5}, year = {2010}, pages = {739{\textendash}752}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @conference {aja2010dwi, title = {DWI acquisition schemes and diffusion tensor estimation: a simulation-based study}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE}, year = {2010}, pages = {3317{\textendash}3320}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Pablo Casaseca-de-la-Higuera} } @article {tristan2010dwi, title = {DWI filtering using joint information for DTI and HARDI}, journal = {Medical Image Analysis}, volume = {14}, number = {2}, year = {2010}, pages = {205{\textendash}218}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {garcia2010nurbs, title = {NURBS for the geometrical modeling of a new family of Compact-Supported Radial Basis Functions for elastic registration of medical images}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE}, year = {2010}, pages = {5947{\textendash}5950}, publisher = {IEEE}, organization = {IEEE}, author = {Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @inbook {vegas2010probabilistic, title = {Probabilistic-driven oriented speckle reducing anisotropic diffusion with application to cardiac ultrasonic images}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2010}, year = {2010}, pages = {518{\textendash}525}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Frangi, Alejandro F and Cesar Palencia} } @conference {aja2010soft, title = {Soft thresholding for medical image segmentation}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE}, year = {2010}, pages = {4752{\textendash}4755}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Fernandez, Martin} } @proceedings {aja2010statistical, title = {Statistical noise model in GRAPPA-reconstructed images}, year = {2010}, pages = {3859}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and W Scott Hoge} } @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} } @article {tristan2010new, title = {A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk{\textendash}Radon transform}, journal = {NeuroImage}, volume = {49}, number = {2}, year = {2010}, pages = {1301{\textendash}1315}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @conference {merino2010variationally, title = {A variationally based weighted re-initialization method for geometric active contours}, booktitle = {Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on}, year = {2010}, pages = {908{\textendash}911}, publisher = {IEEE}, organization = {IEEE}, author = {S. Merino-Caviedes and Gonzalo Vegas-S{\'a}nchez-Ferrero and P{\'e}rez, M Teresa and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez} } @article {garcia20093, title = {A 3-D collision handling algorithm for surgery simulation based on feedback fuzzy logic}, journal = {Information Technology in Biomedicine, IEEE Transactions on}, volume = {13}, number = {4}, year = {2009}, pages = {451{\textendash}457}, publisher = {IEEE}, author = {Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Emma Mu{\~n}oz-Moreno and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {aja2009automatic, title = {Automatic noise estimation in images using local statistics. Additive and multiplicative cases}, journal = {Image and Vision Computing}, volume = {27}, number = {6}, year = {2009}, pages = {756{\textendash}770}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @inbook {tristan2009bias, title = {Bias of least squares approaches for diffusion tensor estimation from array coils in DT{\textendash}MRI}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {919{\textendash}926}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @inbook {tristan2009blurring, title = {On the Blurring of the Funk{\textendash}Radon Transform in Q{\textendash}Ball Imaging}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {415{\textendash}422}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @inbook {tristan2009design, title = {Design and construction of a realistic DWI phantom for filtering performance assessment}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {951{\textendash}958}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @article {tristan2009estimation, title = {Estimation of fiber orientation probability density functions in high angular resolution diffusion imaging}, journal = {NeuroImage}, volume = {47}, number = {2}, year = {2009}, pages = {638{\textendash}650}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @article {aja2009noise, title = {Noise estimation in single-and multiple-coil magnetic resonance data based on statistical models}, journal = {Magnetic resonance imaging}, volume = {27}, number = {10}, year = {2009}, pages = {1397{\textendash}1409}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Carlos Alberola-Lopez} } @article {krissian2009noise, title = {Noise-driven anisotropic diffusion filtering of MRI}, journal = {Image Processing, IEEE Transactions on}, volume = {18}, number = {10}, year = {2009}, pages = {2265{\textendash}2274}, publisher = {IEEE}, author = {K Krissian and Santiago Aja-Fern{\'a}ndez} } @inbook {munoz2009quality, title = {Quality Assessment of Tensor Images}, booktitle = {Tensors in Image Processing and Computer Vision}, year = {2009}, pages = {79{\textendash}103}, publisher = {Springer London}, organization = {Springer London}, author = {Emma Mu{\~n}oz-Moreno and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez} } @book {aja2009tensors, title = {Tensors in image processing and computer vision}, year = {2009}, publisher = {Springer}, organization = {Springer}, author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Tao, Dacheng and Li, Xuelong} } @proceedings {tristn2008fuzzy, title = {Fuzzy regularisation of deformation fields in image registration}, year = {2008}, pages = {1223{\textendash}30}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @inbook {tristan2008joint, title = {Joint LMMSE estimation of DWI data for DTI processing}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2008}, year = {2008}, pages = {27{\textendash}34}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {tristan2008local, title = {Local similarity measures for demons-like registration algorithms}, booktitle = {Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on}, year = {2008}, pages = {1087{\textendash}1090}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {aja2008matrix, title = {Matrix modeling of hierarchical fuzzy systems}, journal = {Fuzzy Systems, IEEE Transactions on}, volume = {16}, number = {3}, year = {2008}, pages = {585{\textendash}599}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {aja2008noise, title = {Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach}, journal = {Image Processing, IEEE Transactions on}, volume = {17}, number = {8}, year = {2008}, pages = {1383{\textendash}1398}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {aja2008restoration, title = {Restoration of DWI data using a Rician LMMSE estimator}, journal = {Medical Imaging, IEEE Transactions on}, volume = {27}, number = {10}, year = {2008}, pages = {1389{\textendash}1403}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Niethammer, Marc and Kubicki, Marek and Martha E Shenton and Carl-Fredik Westin} } @conference {vegas2008strain, title = {Strain Rate Tensor estimation in cine cardiac MRI based on elastic image registration}, booktitle = {Computer Vision and Pattern Recognition Workshops, 2008. CVPRW{\textquoteright}08. IEEE Computer Society Conference on}, year = {2008}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega and Lucilio Cordero-Grande and Pablo Casaseca-de-la-Higuera and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {muoz2008methodology, title = {A methodology for quality assessment in tensor images}, booktitle = {Computer Vision and Pattern Recognition Workshops, 2008. CVPRW{\textquoteright}08. IEEE Computer Society Conference on}, year = {2008}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Emma Mu{\~n}oz-Moreno and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez} } @proceedings {aja2008unbiased, title = {An unbiased Non-Local Means scheme for DWI filtering}, year = {2008}, pages = {277{\textendash}284}, author = {Santiago Aja-Fern{\'a}ndez and K Krissian} } @conference {de2007p6d, title = {Analysis of Ultrasound Images Based on Local Statistics. Application to the Diagnosis of Developmental Dysplasia of the Hip}, booktitle = {Ultrasonics Symposium, 2007. IEEE}, year = {2007}, pages = {2531{\textendash}2534}, publisher = {IEEE}, organization = {IEEE}, author = {Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Rub{\'e}n C{\'a}rdenes-Almeida and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @inbook {niethammer2007outlier, title = {Outlier rejection for diffusion weighted imaging}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2007}, year = {2007}, pages = {161{\textendash}168}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Niethammer, Marc and Bouix, Sylvain and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin and Martha E Shenton} } @inbook {aja2007signal, title = {Signal LMMSE estimation from multiple samples in MRI and DT-MRI}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2007}, year = {2007}, pages = {368{\textendash}375}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin} } @conference {aja2007tissue, title = {Tissue identification in ultrasound images using rayleigh local parameter estimation}, booktitle = {Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on}, year = {2007}, pages = {1129{\textendash}1133}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @proceedings {cardenes2007usimagtool, title = {Usimagtool: an open source freeware software for ultrasound imaging and elastography}, year = {2007}, pages = {117{\textendash}127}, author = {Rub{\'e}n C{\'a}rdenes-Almeida and Antonio Trist{\'a}n-Vega and Ferrero, GVS and Santiago Aja-Fern{\'a}ndez} } @article {aja2006fuzzy, title = {Fuzzy feedback system analysis using transition matrices}, journal = {Fuzzy sets and systems}, volume = {157}, number = {4}, year = {2006}, pages = {516{\textendash}543}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @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 {aja2006estimation, title = {On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering}, journal = {Image Processing, IEEE Transactions on}, volume = {15}, number = {9}, year = {2006}, pages = {2694{\textendash}2701}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {aja2005fast, title = {Fast inference using transition matrices: An extension to nonlinear operators}, journal = {Fuzzy Systems, IEEE Transactions on}, volume = {13}, number = {4}, year = {2005}, pages = {478{\textendash}490}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {palacios2005group, title = {Group-Slicer: A collaborative extension of 3D-Slicer}, journal = {Journal of Biomedical Informatics}, volume = {38}, year = {2005}, pages = {431{\textendash}442}, author = {Federico Simmross-Wattenberg and Palacios-Camarero, Cristina and Pablo Casaseca-de-la-Higuera and Miguel Angel Martin-Fernandez and Santiago Aja-Fern{\'a}ndez and Juan Ruiz-Alzola and Carl-Fredik Westin and Carlos Alberola-Lopez} } @conference {aja2005matrix, title = {Matrix Inference in Fuzzy Decision Trees.}, booktitle = {EUSFLAT Conf.}, year = {2005}, pages = {979{\textendash}984}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {aja2004fast, title = {Fast inference in SAM fuzzy systems using transition matrices}, journal = {Fuzzy Systems, IEEE Transactions on}, volume = {12}, number = {2}, year = {2004}, pages = {170{\textendash}182}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {aja2004fuzzy, title = {Fuzzy Granules as a Basic Word Representation for Computing with Words}, booktitle = {9th Conference Speech and Computer}, year = {2004}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {aja2004hierarchical, title = {Hierarchical fuzzy systems with FITM}, booktitle = {Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on}, volume = {2}, year = {2004}, pages = {767{\textendash}772}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {aja2004computational, title = {A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach}, journal = {Journal of Biomedical Informatics}, volume = {37}, number = {2}, year = {2004}, pages = {99{\textendash}107}, publisher = {Academic Press}, author = {Santiago Aja-Fern{\'a}ndez 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} } @article {aja2003fuzzy, title = {A fuzzy-controlled Kalman filter applied to stereo-visual tracking schemes}, journal = {Signal Processing}, volume = {83}, number = {1}, year = {2003}, pages = {101{\textendash}120}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @article {aja2002fuzzy, title = {A fuzzy MHT algorithm applied to text-based information tracking}, journal = {Fuzzy Systems, IEEE Transactions on}, volume = {10}, number = {3}, year = {2002}, pages = {360{\textendash}374}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Cybenko, George V} } @proceedings {de2002neural, title = {A neural architecture for bone age assessment}, year = {2002}, pages = {161{\textendash}166}, author = {Rodrigo de Luis-Garc{\'\i}a and J I Arribas and Santiago Aja-Fern{\'a}ndez and Lopez, C Alberola} } @conference {aja2001fuzzy, title = {Fuzzy anisotropic diffusion for speckle filtering}, booktitle = {Acoustics, Speech, and Signal Processing, 2001. Proceedings.(ICASSP{\textquoteright}01). 2001 IEEE International Conference on}, volume = {2}, year = {2001}, pages = {1261{\textendash}1264}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Juan Ruiz-Alzola} }