@article {991, title = {HYDI-DSI revisited: constrained non-parametric EAP imaging without q-space re-gridding}, journal = {Medical Image Analysis}, volume = {84}, year = {2023}, month = {02/2023}, chapter = {102728}, abstract = {

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular ResolutionDiffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated.
We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.

}, keywords = {Diffusion Spectrum Imaging, Ensemble Average Propagator, Hybrid Diffusion Imaging, diffusion MRI}, doi = {102728}, url = {https://www.sciencedirect.com/science/article/pii/S1361841522003565}, author = {Antonio Trist{\'a}n-Vega and Tomasz Pieciak and Guillem Par{\'\i}s and Justino R. Rodr{\'\i}guez-Galv{\'a}n and Santiago Aja-Fern{\'a}ndez} } @proceedings {989, title = {Impact of free-water correction on white matter changes measured by diffusion tensor imaging in migraine}, volume = {4601}, year = {2023}, month = {2023}, abstract = {

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

}, author = {Guadilla, Irene and Fouto, Ana and {\'A}lvaro Planchuelo-G{\'o}mez and Trist{\'a}n-Vega, Antonio and Ruiz-Tagle, Amparo and Esteves, In{\^e}s and Caetano, Gina and Silva, Nuno and Vilela, Pedro and Gil-Gouveia, Raquel and Aja-Fern{\'a}ndez, Santiago and Figueiredo, Patr{\'\i}cia and Nunes, Rita} } @article {973, title = {Structural brain changes in patients with persistent headache after COVID-19 resolution}, journal = {Journal of Neurology}, volume = {270}, year = {2023}, pages = {13-31}, abstract = {

Headache is among the most frequently reported symptoms after resolution of COVID-19. We assessed structural brain changes using T1- and diffusion-weighted MRI processed data from 167 subjects: 40 patients who recovered from COVID-19 but suffered from persistent headache without prior history of headache (COV), 41 healthy controls, 43 patients with episodic migraine and 43 patients with chronic migraine. To evaluate gray matter and white matter changes, morphometry parameters and diffusion tensor imaging-based measures were employed, respectively. COV patients showed significant lower cortical gray matter volume and cortical thickness than healthy subjects (p\thinspace\<\thinspace0.05, false discovery rate corrected) in the inferior frontal and the fusiform cortex. Lower fractional anisotropy and higher radial diffusivity (p\thinspace\<\thinspace0.05, family-wise error corrected) were observed in COV patients compared to controls, mainly in the corpus callosum and left hemisphere. COV patients showed higher cortical volume and thickness than migraine patients in the cingulate and frontal gyri, paracentral lobule and superior temporal sulcus, lower volume in subcortical regions and lower curvature in the precuneus and cuneus. Lower diffusion metric values in COV patients compared to migraine were identified prominently in the right hemisphere. COV patients present diverse changes in the white matter and gray matter structure. White matter changes seem to be associated with impairment of fiber bundles. Besides, the gray matter changes and other white matter modifications such as axonal integrity loss seemed subtle and less pronounced than those detected in migraine, showing that persistent headache after COVID-19 resolution could be an intermediate state between normality and migraine.

}, issn = {1432-1459}, doi = {10.1007/s00415-022-11398-z}, url = {https://doi.org/10.1007/s00415-022-11398-z}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and Guerrero, {\'A}ngel L. and Rodr{\'\i}guez, Margarita and Aja-Fern{\'a}ndez, Santiago and de Luis-Garc{\'\i}a, Rodrigo} } @proceedings {985, title = {Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies}, volume = {2786}, year = {2023}, month = {2023}, abstract = {

This work gathers the results of the QuadD22 challenge, held in MICCAI 2022. We evaluate whether Deep Learning (DL) Techniques are able to improve the quality of diffusion MRI data in clinical studies. To that end, we focused on a real study on migraine, where the differences between groups are drastically reduced when using 21 gradient directions instead of 61. Thus, we asked the participants to augment dMRI data acquired with only 21 directions to 61 via DL. The results were evaluated using a real clinical study with TBSS in which we statistically compared episodic migraine to chronic migraine.

}, author = {Aja-Fernandez, Santiago and Martin-Martin, Carmen and Pieciak, Tomasz and {\'A}lvaro Planchuelo-G{\'o}mez and Faiyaz, Abrar and Uddin, Nasir and Tiwari, Abhishek and Shigwan, Saurabh J and Zheng, Tianshu and Cao, Zuozhen and Blumberg, Stefano B and Sen, Snigdha and Yigit Avci, Mehmet and Li, Zihan and Wang, Xinyi and Tang, Zihao and Rauland, Amelie and Merhof, Dorit and Manzano Maria, Renata and Campos, Vinicius P and HashemiazadehKolowri, SeyyedKazem and DiBella, Edward and Peng, Chenxu and Chen, Zan and Ullah, Irfan and Mani, Merry and Eckstrom, Samuel and Baete, Steven H and Scifitto, Scifitto and Singh, Rajeev Kumar and Wu, Dan and Goodwin-Allcock, Tobias and Slator, Paddy J and Bilgic, Berkin and Tian, Qiyuan and Cabezas, Mariano and Santini, Tales and Andrade da Costa Vieira, Marcelo and Shen, Zhimin and Abdolmotalleby, Hesam and Filipiak, Patryk and Tristan-Vega, Antonio and de Luis-Garcia, Rodrigo} } @article {995, title = {Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies}, journal = {NeuroImage: Clinical}, volume = {39}, year = {2023}, pages = {103483}, abstract = {

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

}, keywords = {Angular resolution, Artificial Intelligence, Deep learning, Diffusion tensor, diffusion MRI, machine learning}, issn = {2213-1582}, doi = {https://doi.org/10.1016/j.nicl.2023.103483}, url = {https://www.sciencedirect.com/science/article/pii/S2213158223001742}, author = {Santiago Aja-Fern{\'a}ndez and Carmen Mart{\'\i}n-Mart{\'\i}n and {\'A}lvaro Planchuelo-G{\'o}mez and Abrar Faiyaz and Md Nasir Uddin and Giovanni Schifitto and Abhishek Tiwari and Saurabh J. Shigwan and Rajeev Kumar Singh and Tianshu Zheng and Zuozhen Cao and Dan Wu and Stefano B. Blumberg and Snigdha Sen and Tobias Goodwin-Allcock and Paddy J. Slator and Mehmet Yigit Avci and Zihan Li and Berkin Bilgic and Qiyuan Tian and Xinyi Wang and Zihao Tang and Mariano Cabezas and Amelie Rauland and Dorit Merhof and Renata Manzano Maria and Vin{\'\i}cius Paran{\'\i}ba Campos and Tales Santini and Marcelo Andrade da Costa Vieira and SeyyedKazem HashemizadehKolowri and Edward DiBella and Chenxu Peng and Zhimin Shen and Zan Chen and Irfan Ullah and Merry Mani and Hesam Abdolmotalleby and Samuel Eckstrom and Steven H. Baete and Patryk Filipiak and Tanxin Dong and Qiuyun Fan and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Tomasz Pieciak} } @conference {975, title = {Comparing signal models for correcting diffusion-weighted MR images for free water partial volume effects}, booktitle = {ISMRM Workshop on Diffusion MRI: From Research to Clinic}, year = {2022}, address = {Amsterdam, The Netherlands}, author = {Guadilla, Irene and Fouto, Ana R. and {\'A}lvaro Planchuelo-G{\'o}mez and Trist{\'a}n-Vega, Antonio and Ruiz-Tagle, Amparo and Esteves, In{\^e}s and Caetano, Gina and Aja-Fern{\'a}ndez, Santiago and Figueiredo, Patr{\'\i}cia and Nunes, Rita G.} } @article {964, title = {Default mode network components and its relationship with anomalous self-experiences in schizophrenia: A rs-fMRI exploratory study}, journal = {Psychiatry Research: Neuroimaging}, volume = {324}, year = {2022}, pages = {111495}, abstract = {

Anomalous self-experiences (ASEs) in schizophrenia have been under research for the last 20 years. However, no neuroimage studies have provided insight of the possible biological underpinning of ASEs. In this novel approach, the connectivity within the default mode network, calculated through a ROI-based analysis of functional magnetic resonance imaging data, was correlated to the ASEs scores assessed by the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE) in a sample of 22 schizophrenia patients. The Pearson{\textquoteright}s correlation coefficients between IPASE scores and intrahemispheric connectivity of the parahippocampal gyrus with the isthmus cingulate cortex in both hemispheres, and right parahippocampal gyrus with the right rostral anterior cingulate cortex were positive and significant suggesting a relation between hyperactive functional connectivity and anomalous self-experiences intensity. Prior literature reported these areas to have a role in self-processing and consciousness as well as being anatomically connected. Further research with larger sample size and comparison with controls are needed to confirm the relationship of this connectivity with anomalous self-experiences.

}, keywords = {Anterior cingulate cortex, Metacognition, Parahippocampal gyrus, Psychosis, functional magnetic resonance imaging}, issn = {0925-4927}, doi = {https://doi.org/10.1016/j.pscychresns.2022.111495}, url = {https://www.sciencedirect.com/science/article/pii/S0925492722000567}, author = {Roig-Herrero, Alejandro and {\'A}lvaro Planchuelo-G{\'o}mez and Hern{\'a}ndez-Garc{\'\i}a, Marta and de Luis-Garc{\'\i}a, Rodrigo and Fern{\'a}ndez-Linsenbarth, In{\'e}s and Be{\~n}o-Ruiz-de-la-Sierra, Rosa M. and Molina, Vicente} } @conference {968, title = {Long-term grey matter structural changes in the transition from chronic migraine to episodic migraine}, booktitle = {8th Congress of the European Academy of Neurology}, year = {2022}, month = {2022}, abstract = {

Background and aims: The objective was to assess grey matter longitudinal changes in patients with chronic migraine (CM) who reverse to episodic migraine (EM) compared to those who do not reverse.

Methods: High-resolution 3D brain T1-weighted Magnetic Resonance Imaging data were obtained twice from migraine patients. The first acquisition was performed immediately after the first visit to the Headache Unit, before taking preventive treatments. The second timepoint was at least three years after the first acquisition. From the longitudinal pipeline of FreeSurfer (v6.0), the mean values of cortical thickness, surface area and grey matter volume of 68 cortical, 14 subcortical regions and the cerebellum were extracted. Longitudinal changes between patients with CM and those who reversed to EM were assessed with linear
mixed-effects models, setting p\<0.05 (false discovery rate corrected) as threshold for statistical significance.

Results: 22 patients were included, and 10 of them (45.5\%) reversed to EM. No statistically significant differences of age (42.0+-9.0 years) and sex (21 women, 95.5\%) were found between patient groups. Higher statistically significant values of the three parameters in patients who reversed to EM were found in the pericalcarine, parietal, orbitofrontal cortex, and amygdala (Table 1, Figure 1). In contrast, lower values were detected in the cingulum, caudal middle frontal cortex, cerebellum, caudate nucleus and pallidum (Figure 2). In the insula, higher thickness but lower area was appreciated in patients who reversed.

Conclusion: Patients with CM who reverse to EM present distinct patterns of increased and decreased morphometric parameters propagated in the orbital frontal cortex and the cingulum, respectively.

Disclosure: Nothing to disclose.

}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Marchante-Re{\'\i}llo, Ginebra and Sierra, {\'A}lvaro and Garc{\'\i}a-Azor{\'\i}n, David and Mart{\'\i}n-Mart{\'\i}n, Carmen and de Luis-Garc{\'\i}a, Rodrigo and Aja-Fern{\'a}ndez, Santiago and Moro, Ra{\'u}l and Rodr{\'\i}guez, Margarita and Gonz{\'a}lez-Osorio, Y{\'e}sica and {\'A}ngel L. Guerrero} } @article {925, title = {A Clinically Viable Vendor-Independent and Device-Agnostic Solution for Accelerated Cardiac MRI Reconstruction}, journal = {Computer Methods and Programs in Biomedicine}, volume = {207}, year = {2021}, chapter = {106143}, issn = {0169-2607}, doi = {https://doi.org/10.1016/j.cmpb.2021.106143}, url = {https://www.sciencedirect.com/science/article/pii/S0169260721002182}, author = {Mart{\'\i}n-Gonz{\'a}lez, Elena and Elisa Moya-S{\'a}ez and Mench{\'o}n-Lara, Rosa-Mar{\'\i}a and J Royuela-del-Val and Palencia-de-Lara, C{\'e}sar and M. Rodr{\'\i}guez-Cayetano and Simmross-Wattenberg, Federico and Carlos Alberola-Lopez} } @article {960, title = {Fast 4D elastic group-wise image registration. Convolutional interpolation revisited}, journal = {Computer Methods and Programs in Biomedicine}, volume = {200}, year = {2021}, pages = {105812}, abstract = {

Background and Objective:This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. Methods:Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. Results:The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90\%, both in CPU and GPU executions, compared with the classical tensor product formulation. Conclusions:Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.

}, keywords = {B-splines, Convolution, Efficient implementation, Free-form deformation, Groupwise Registration, Non-rigid registration}, issn = {0169-2607}, doi = {https://doi.org/10.1016/j.cmpb.2020.105812}, url = {https://www.sciencedirect.com/science/article/pii/S016926072031645X}, author = {Rosa-Mar{\'\i}a Mench{\'o}n-Lara and Javier Royuela-del-Val and Federico Simmross-Wattenberg and Pablo Casaseca-de-la-Higuera and Marcos Mart{\'\i}n-Fern{\'a}ndez and Carlos Alberola-L{\'o}pez} } @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} } @article {953, title = {Search for schizophrenia and bipolar biotypes using functional network properties}, journal = {Brain and Behavior}, volume = {11}, year = {2021}, pages = {e2415}, abstract = {

Introduction: Recent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements.

Methods: A clustering analysis was performed using the electroencephalogram-based network parameters derived from graph-theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons.

Results: We identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path-length, and lower small-world index compared to controls. The modulation of clustering coefficient and path-length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients{\textasciiacute} clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation.

Conclusion: These data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity.

}, keywords = {Biotypes, bipolar disorder, diffusion, electroencephalogram, network, schizophrenia}, doi = {https://doi.org/10.1002/brb3.2415}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/brb3.2415}, author = {Fern{\'a}ndez-Linsenbarth, In{\'e}s and {\'A}lvaro Planchuelo-G{\'o}mez and Be{\~n}o-Ruiz-de-la-Sierra, Rosa M. and D{\'\i}ez, Alvaro and Arjona, Antonio and P{\'e}rez, Adela and Rodr{\'\i}guez-Lorenzana, Alberto and del Valle, Pilar and de Luis-Garc{\'\i}a, Rodrigo and Mascialino, Guido and Holgado-Madera, Pedro and Segarra-Echevarr{\'\i}a, Rafael and Gomez-Pilar, Javier and N{\'u}{\~n}ez, Pablo and Bote-Boneaechea, Berta and Zambrana-G{\'o}mez, Antonio and Roig-Herrero, Alejandro and Molina, Vicente} } @article {924, title = {Temporal distribution of emergency room visits in patients with migraine and other headaches}, journal = {Expert Review of Neurotherapeutics}, volume = {21}, year = {2021}, pages = {599-605}, abstract = {

Background: Headache is a leading reason for presentation to the emergency department (ED) with migraine being the most frequently headache. To ensure the adequate staffing of healthcare providers during peak times of headache visits, we analyzed the temporal distribution of emergency department visits in patients presenting with headache and/or migraine.

Research design and methods: The authors conducted an ecological study, including all consecutive visits to the ED for headache. Patients were classified according to the IHS Classification. We analyzed circadian, circaseptan and circannual patterns for number of visits, comparing migraine patients with other headache patients.

Results: There were 2132 ED visits for headache, including primary headache in 1367 (64.1\%) cases; migraine in 963 (45.2\%); secondary headache in 404 (18.9\%); and unspecified headache in 366 (17.1\%). The circadian pattern showed peaks around 11:00{\textendash}13:00 and 17:00{\textendash}19:00, with visits during the night shift 45\% less frequent (p \< 0.001). The circaseptan pattern showed a peak on Monday-Tuesday and a low point on Sunday (p \< 0.007). The circannual pattern peaked in March and decreased in June.

Conclusions: ED visits for headache showed specific circadian, circaseptan and circannual variations. No differences were found in these patterns when comparing migraine patients to other headache patients.

}, doi = {10.1080/14737175.2021.1906222}, url = {https://doi.org/10.1080/14737175.2021.1906222}, author = {Garc{\'\i}a-Azor{\'\i}n, David and Abelaira-Freire, Jaime and Rodriguez-Adrada, Esther and Gonz{\'a}lez-Garc{\'\i}a, Nuria and {\'A}lvaro Planchuelo-G{\'o}mez and {\'A}ngel L. Guerrero and Porta-Etessam, Jes{\'u}s and Mart{\'\i}n-S{\'a}nchez, Francisco J} } @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 {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} } @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 {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 {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 {853, title = {Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction}, journal = {Entropy}, volume = {22}, year = {2020}, pages = {687}, doi = {https://doi.org/10.3390/e22060687}, url = {https://www.mdpi.com/1099-4300/22/6/687}, author = {Mart{\'\i}n-Gonz{\'a}lez, Elena and Sevilla, Teresa and Revilla-Orodea, Ana and Pablo Casaseca-de-la-Higuera and Alberola-L{\'o}pez, Carlos} } @article {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 {805, title = {Air Infiltration Monitoring using Thermography and Neural Networks}, journal = {Energy and Buildings}, volume = {191}, year = {2019}, month = {05/2019}, pages = {187-199}, chapter = {187}, author = {A Royuela and M A Padilla-Marcos and A Meiss and Pablo Casaseca-de-la-Higuera and J Feij{\'o}-Mu{\~n}oz} } @article {862, title = {An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video}, journal = {Agronomy}, volume = {9}, year = {2019}, abstract = {The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (Kc) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88\%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies.}, doi = {https://doi.org/10.3390/agronomy9020084}, url = {https://www.mdpi.com/2073-4395/9/2/84}, author = {S Sabzi and Yousef Abbaspour-Gilandeh and G Garcia-Mateos and A Ruiz-Canales and J M Molina-Martinez and J I Arribas} } @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 {807, title = {OpenCLIPER: An OpenCL-Based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices}, journal = {IEEE Journal of Biomedical and Health Informatics}, volume = {23}, year = {2019}, month = {July}, pages = {1702-1709}, abstract = {

Medical image processing is often limited by the computational cost of the involved algorithms. Whereas dedicated computing devices (GPUs in particular) exist and do provide significant efficiency boosts, they have an extra cost of use in terms of housekeeping tasks (device selection and initialization, data streaming, synchronization with the CPU, and others), which may hinder developers from using them. This paper describes an OpenCL-based framework that is capable of handling dedicated computing devices seamlessly and that allows the developer to concentrate on image processing tasks. The framework handles automatically device discovery and initialization, data transfers to and from the device and the file system and kernel loading and compiling. Data structures need to be defined only once independently of the computing device; code is unique, consequently, for every device, including the host CPU. Pinned memory/buffer mapping is used to achieve maximum performance in data transfers. Code fragments included in the paper show how the computing device is almost immediately and effortlessly available to the users algorithms, so they can focus on productive work. Code required for device selection and initialization, data loading and streaming and kernel compilation is minimal and systematic. Algorithms can be thought of as mathematical operators (called processes), with input, output and parameters, and they may be chained one after another easily and efficiently. Also for efficiency, processes can have their initialization work split from their core workload, so process chains and loops do not incur in performance penalties. Algorithm code is independent of the device type targeted.

}, keywords = {C++, C++ languages, Data structures, GPU, Graphics processing units, Image reconstruction, Informatics, Kernel, Libraries, Medical imaging, OpenCL}, issn = {2168-2194}, doi = {10.1109/JBHI.2018.2869421}, author = {Federico Simmross-Wattenberg and M. Rodr{\'\i}guez-Cayetano and J Royuela-del-Val and E. Mart{\'\i}n-Gonz{\'a}lez and E. Moya-S{\'a}ez and M. Mart{\'\i}n-Fern{\'a}ndez and C. Alberola-L{\'o}pez} } @article {795, title = {Space-time variant weighted regularization in compressed sensing cardiac cine MRI}, journal = {Magnetic Resonance Imaging}, volume = {58}, year = {2019}, pages = {44 - 55}, abstract = {

Purpose: To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI.
Methods: k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain.
Results: The proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach.
Conclusions: Usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.

}, keywords = {Cine cardiac MRI, Space-time variant regularization, compressed sensing, k-t SPARSE-SENSE}, issn = {0730-725X}, doi = {https://doi.org/10.1016/j.mri.2019.01.005}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X18301978}, author = {Alejandro Godino-Moya and J Royuela-del-Val and Muhammad Usman and Rosa-Mar{\'\i}a Mench{\'o}n-Lara and Marcos Mart{\'\i}n-Fern{\'a}ndez and Claudia Prieto and Carlos Alberola-L{\'o}pez} } @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 {772, title = {Characterisation of Received Signal Strength Perturbations using Allan Variance}, journal = {IEEE Transactions on Aerospace and Electronic Systems}, volume = {54}, year = {2018}, pages = {873-889}, chapter = {873}, author = {Luo, Chunbo and Pablo Casaseca-de-la-Higuera and McClean, Sally and Parr, Gerard and Ren, Peng} } @conference {791, title = {On the Construction of Non Linear Adjoint Operators: Application to L1-Penalty Dynamic Image Reconstruction}, booktitle = {Congreso Anual de Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2018}, month = {11/2018}, address = {Ciudad Real, Espa{\~n}a}, abstract = {

The purpose of this work is to develop a methodology for the adjoint operators application in non linear optimization problems. The use of adjoint operators is very popular for numerical control theory; one of its main applications is devised for image reconstruction. Most of these reconstruction techniques are limited to linear L1-constraints whose adjoints are well-defined. We aim to extend these image reconstruction techniques allowing the terms involved to be non linear. For these purpose, we have generalized the concept of adjoint operator under the basis of Taylor{\textquoteright}s formula, using Gateaux derivatives in order to construct a linearised adjoint operator associated to the non linear operator. The proposed approach has been validated in a Magnetic Resonance Imaging (MRI) reconstruction framework with Cartesian subsampled k-space data using Compressed Sensing based techniques and a groupwise registration algorithm for motion compensation.
The proposed algorithm has shown to be able to effectively deal with the presence of both physiological motion and subsampling artefacts, increasing accuracy and robustness of the reconstruction as compared with its linear counterpart.

}, author = {Santiago Sanz-Est{\'e}banez and Elisa Moya-S{\'a}ez and J Royuela-del-Val and Carlos Alberola-L{\'o}pez} } @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} } @article {761, title = {Topography of activation deficits in schizophrenia during P300 task related to cognition and structural connectivity}, journal = {European Archives of Psychiatry and Clinical Neuroscience}, year = {2018}, month = {Feb}, issn = {1433-8491}, doi = {10.1007/s00406-018-0877-3}, url = {https://doi.org/10.1007/s00406-018-0877-3}, author = {Vicente Molina and Bachiller, Alejandro and Rodrigo de Luis-Garc{\'\i}a and Lubeiro, Alba and Poza, Jes{\'u}s and Hornero, Roberto and Alonso, Joan Francesc and Ma{\~n}anas, Miguel Angel and Marqu{\'e}s, Patricia and Romero, Sergio} } @article {749, title = {Vortical Features for Myocardial Rotation Assessment in Hypertrophic Cardiomyopathy using Cardiac Tagged Magnetic Resonance}, journal = {Medical Image Analysis}, volume = {In Press}, year = {2018}, month = {04/2018}, type = {Original Article}, abstract = {

Left ventricular rotational motion is a feature of normal and diseased cardiac function. However, classical torsion and twist measures rely on the definition of a rotational axis which may not exist. This paper reviews global and local rotation descriptors of myocardial motion and introduces new curl-based (vortical) features built from tensorial magnitudes, intended to provide better comprehension about fibrotic tissue characteristics mechanical properties. Fifty-six cardiomyopathy patients and twenty-two healthy volunteers have been studied using tagged magnetic resonance by means of harmonic phase analysis. Rotation descriptors are built, with no assumption about a regular geometrical model, from different approaches. The extracted vortical features have been tested by means of a sequential cardiomyopathy classification procedure; they have proven useful for the regional characterization of the left ventricular function by showing great separability not only between pathologic and healthy patients but also, and specifically, between heterogeneous phenotypes within cardiomyopathies.

}, doi = {10.1016/j.media.2018.03.005}, author = {Santiago Sanz-Est{\'e}banez and Lucilio Cordero-Grande and T. Sevilla-Ruiz and A. Revilla-Orodea and Rodrigo de Luis-Garc{\'\i}a and M Martin-Fernandez and Carlos Alberola-Lopez} } @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} } @article {698, title = {Abnormal Capillary Vasodynamics Contribute to Ictal Neurodegeneration in Epilepsy}, journal = {Scientific Reports}, volume = {7}, year = {2017}, abstract = {

Seizure-driven brain damage in epilepsy accumulates over time, especially in the hippocampus, which can lead to sclerosis, cognitive decline, and death. Excitotoxicity is the prevalent model to explain ictal neurodegeneration. Current labeling technologies cannot distinguish between excitotoxicity and hypoxia, however, because they share common molecular mechanisms. This leaves open the possibility that undetected ischemic hypoxia, due to ictal blood flow restriction, could contribute to neurodegeneration previously ascribed to excitotoxicity. We tested this possibility with Confocal Laser Endomicroscopy (CLE) and novel stereological analyses in several models of epileptic mice. We found a higher number and magnitude of NG2+ mural-cell mediated capillary constrictions in the hippocampus of epileptic mice than in that of normal mice, in addition to spatial coupling between capillary constrictions and oxidative stressed neurons and neurodegeneration. These results reveal a role for hypoxia driven by capillary blood flow restriction in ictal neurodegeneration. {\textcopyright} 2017 The Author(s).

}, doi = {10.1038/srep43276}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014072909\&doi=10.1038\%2fsrep43276\&partnerID=40\&md5=e9d3567266bdc360a7addc92be350c8d}, author = {Leal-Campanario, R. and Alarcon-Martinez, L. and Rieiro, H. and Martinez-Conde, S. and Alarcon-Martinez, T. and Zhao, X. and LaMee, J. and Popp, P.J. and Calhoun, M.E. and J I Arribas and Schlegel, A.A. and Di Stasi, L.L. and Rho, J.M. and Inge, L. and Otero-Millan, J. and Treiman, D.M. and Macknik, S.L.} } @article {743, title = {Libstable: Fast, parallel, and high-precision computation of α-stable distributions in R, C/C++, and MATLAB}, journal = {Journal of Statistical Software}, volume = {78}, year = {2017}, pages = {1-25}, doi = {10.18637/jss.v078.i01}, author = {J Royuela-del-Val and Federico Simmross-Wattenberg and Carlos Alberola-Lopez} } @article {773, title = {Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device}, journal = {Biomedical signal processing and control}, volume = {33}, year = {2017}, pages = {96{\textendash}108}, author = {Gibson, Ryan M and Amira, Abbes and Ramzan, Naeem and Pablo Casaseca-de-la-Higuera and Pervez, Zeeshan} } @conference {600, title = {An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy}, booktitle = {IWBBIO 2016 (4th International Work-Conference on Bioinformatics and Biomedical Engineering)}, volume = {1}, year = {2016}, month = {2016}, pages = {1-12}, edition = {LNBI 9656}, address = {Granada, Spain}, abstract = {

Cardiovascular diseases are the leading cause of death globally. Therefore, classi cation tools play a major role in prevention and\ treatment of these diseases. Statistical learning theory applied to magnetic resonance imaging has led to the diagnosis of a variety of cardiomyopathies states.\ We propose a two-stage classi cation scheme capable of\ distinguishing between heterogeneous groups of hypertrophic cardiomyopathies and healthy patients.\ A multimodal processing pipeline is employed to estimate robust tensorial descriptors of myocardial mechanical\ properties for both short-axis and long-axis magnetic resonance tagged\ images using the least absolute deviation method. A homomorphic ltering procedure is used to align the cine segmentations to the tagged sequence and provides 3D tensor information in meaningful areas.\ Results\ have shown that the proposed pipeline provides tensorial measurements\ on which classi ers for the study of hypertrophic cardiomyopathies can\ be built with acceptable performance even for reduced samples sets.

}, keywords = {Fuzzy clustering, HARmonic Phase, Homomorphic Filtering, Hypertrophic Cardiomyopathy, Least Absolute Deviation, Magnetic Resonance Tagging, Support Vector Machines}, doi = {10.1007/978-3-319-31744-1 17}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and S. Merino-Caviedes and A. Revilla-Orodea and T. Sevilla-Ruiz and Martin-Fernandez, M and Carlos Alberola-Lopez} } @conference {663, title = {Cardio-respiratory motion estimation for compressed sensing reconstruction of free-breathing 2D cine MRI}, booktitle = {Modulated/Incomplete Data 2016, SFB Workshop.}, year = {2016}, month = {2016}, publisher = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center}, organization = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center}, address = {Graz, Austria}, abstract = {

Respiratory motion is still an issue in MRI of the heart despite the introduction of Compressed Sensing (CS) techniques, which significantly accelerate acquisition [1]. Recently [2], a double-binning scheme was introduced in which k-space data is split according both to the cardiac and respiratory phases (Fig. 1); at reconstruction, sparsity along both dimensions is exploited. Other methods introduce motion estimation and compensation in CS (MC-CS) either to correct the respiratory motion [3] or to promote sparsity for reconstruction improvement [4]. In this work, we propose a technique to jointly estimate the respiratory and cardiac motions within a double-binning scheme, enabling the MC-CS reconstruction of respiratory resolved free-breathing 2D CINE MRI. Preliminary results on synthetic, highly undersampled (x16) Cartesian setup are shown.

[1] Lustig et al. MRM 2007, [2] Feng et al. MRM 2015, [3] Usman et al. MRM 2013. [4]\ Royuela-del-Val et al. MRM 2015.

}, author = {J Royuela-del-Val and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Carlos Alberola-Lopez} } @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 {602, title = {Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI}, journal = {Magnetic Resonance in Medicine}, year = {2016}, month = {2016}, abstract = {

Purpose:\ To eliminate the need of spatial intraframe regularization in a recently reported dynamic MRI compressed-sensing-based reconstruction method with motion compensation and to increase its performance.

Theory and Methods: We propose a new regularization metric based on the introduction of a spatial weighting measure given by the Jacobian of the estimated deformations. It shows convenient discretization properties and, as a byproduct, it also provides a theoretical support to a result reported by others based on an intuitive design. The method has been applied to the reconstruction of both short and long axis views of the heart of four healthy volunteers. Quantitative image quality metrics as well as straightforward visual assessment are reported.

Results: Short and long axis reconstructions of cardiac cine MRI sequences have shown superior results than previously reported methods both in terms of quantitative metrics and of visual assessment. Fine details are better preserved due to the lack of additional intraframe regularization, with no significant image artifacts even for an acceleration factor of 12.

Conclusions: The proposed Jacobian Weighted temporal Total Variation results in better reconstructions of highly undersampled cardiac cine MRI than previously proposed methods and sets a theoretical ground for forward and backward predictors used elsewhere.

}, keywords = {compressed sensing, dynamic MRI reconstruction, group-wise registration, motion estimation}, doi = {10.1002/mrm.26198}, url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.26198}, author = {J Royuela-del-Val and Lucilio Cordero-Grande and Federico Simmross-Wattenberg and Mart{\'\i}n-Fern{\'a}ndez, M and Carlos Alberola-Lopez} } @article {597, title = {Multi-oriented windowed harmonic phase reconstruction for robust cardiac strain imaging}, journal = {Medical Image Analysis}, volume = {29}, year = {2016}, month = {2016}, pages = {1-11}, chapter = {1}, author = {Lucilio Cordero-Grande and J Royuela-del-Val and Santiago Sanz-Est{\'e}banez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @article {776, title = {Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic}, journal = {Applied Soft Computing}, volume = {39}, year = {2016}, pages = {94{\textendash}103}, author = {Gibson, Ryan M and Amira, Abbes and Ramzan, Naeem and Pablo Casaseca-de-la-Higuera and Pervez, Zeeshan} } @conference {603, title = {Multiresolution Reconstruction of Real-Time MRI with Motion Compensated Compressed Sensing: Application to 2D Free-Breathing Cardiac MRI}, booktitle = {International Symposium on Biomedical Engineering: From Nano to Macro}, year = {2016}, month = {2016}, publisher = {IEEE Signal Processing Society}, organization = {IEEE Signal Processing Society}, address = {Prague, Check Republic}, abstract = {

Real-time MRI is a novel noninvasive imaging technique that allows the visualization of physiological processes with both good spatial and temporal resolutions. However, the reconstruction of images from highly undersampled data, needed to perform real-time imaging, remains challenging. Recently, the combination of Compressed Sensing theory with motion compensation techniques has shown to achieve better results than previous methods. In this work we describe a real-time MRI algorithm based on the acquisition of the k-space data following a Golden Radial trajectory, Compressed Sensing reconstruction and a groupwise temporal registration algorithm for the estimation and compensation of the motion in the image, all this embedded within a temporal multiresolution scheme. We have applied the proposed method to the reconstruction of free-breathing acquisition of short axis views of the heart, achieving a temporal resolution of 25ms, corresponding to an acceleration factor of 28 with respect to fully sampled Cartesian acquisitions.

}, keywords = {Compressive sensing \& sampling, Image reconstruction {\textendash} analytical \& iterative methods, Magnetic resonance imaging (MRI)}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and M. Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez} } @conference {690, title = {Whole-heart single breath-hold cardiac cine: A robust motion-compensated compressed sensing reconstruction method}, booktitle = {International Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO/MICCAI) }, year = {2016}, month = {2016}, address = {Athens, Greece}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez} } @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} } @conference {598, title = {Cardiac Strain Assessment for Fibrotic Myocardial Tissue Detection in Left Ventricular Hypertrophic Cardiomyopathy}, booktitle = {XXXIII Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica.}, year = {2015}, month = {2015}, address = {Madrid, Spain.}, abstract = {

This work proposes an image processing methodology to\ distinguish fibrotic from normal tissue by the assessment of the\ local mechanical properties of the myocardium in magnetic\ resonance tagging images. The procedure uses the information\ provided by short axis images of the above mentioned modality\ to estimate the Green-Lagrange strain tensor; a modified\ method based on the Harmonic Phase is employed for motion\ estimation. The method has been applied to the analysis of the\ local deformation patterns in a set of patients affected by\ hypertrophic cardiomyopathy in order to find the agreement\ between hyperenhanced zones in late enhancement images and\ areas in the myocardium with abnormal tensor values (both the\ radial and the circumferential components as well as the\ shearing component have been accounted for). The agreement is\ measured taken as ground truth manual segmentation of late\ enhancement images carried out by two cardiologists. Finally, a\ set of example images illustrate the agreement between both\ techniques.

}, author = {Santiago Sanz-Est{\'e}banez and S. Merino-Caviedes and T. Sevilla-Ruiz and A. Revilla-Orodea and Mart{\'\i}n-Fern{\'a}ndez, M and Carlos Alberola-Lopez} } @proceedings {574, title = {Effect of Downsampling and Compressive Sensing on Audio-based Continuous Cough Monitoring}, volume = {37}, year = {2015}, pages = {6231-6235}, address = {Milan, Italy}, author = {Pablo Casaseca-de-la-Higuera and Lesso, Paul and Mckinstry, Brian and Pinnock, Hilary and Rabinovich, Roberto and McCloughan, Lucy and Monge-{\'A}lvarez, Jes{\'u}s} } @article {573, title = {Multiple Comparator Classifier Framework for Accelerometer-Based Fall Detection and Diagnostic}, journal = {Applied Soft Computing}, year = {2015}, month = {In press}, author = {Gibson, Ryan M and Amira, Abbes and Ramzan, Naeem and Pablo Casaseca-de-la-Higuera and Pervez, Zeeshan} } @conference {588, title = {Multiresolution Reconstruction of Real-Time MRI with Motion Compensated Compressed Sensing: Application to 2D Free-Breathing Cardiac MRI}, booktitle = {XXXIII Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2015}, month = {11/2015}, address = {Madrid}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Marcos Martin-Fernandez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez} } @article {533, title = {Non-Rigid Groupwise Registration for Motion Estimation and Compensation in Compressed Sensing Reconstruction of Breath-Hold Cardiac Cine MRI}, journal = {Magnetic Resonance in Medicine}, year = {2015}, doi = {10.1002/mrm.25733}, author = {J Royuela-del-Val and Lucilio Cordero-Grande and Federico Simmross-Wattenberg and Marcos Mart{\'\i}n-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {520, title = {Single Breath Hold Whole Heart Cine MRI With Iterative Groupwise Cardiac Motion Compensation and Sparse Regularization (kt-WiSE)}, booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine 23}, year = {2015}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Federico Simmross-Wattenberg and Marcos Martin-Fern{\'a}ndez and Claudia Prieto and Carlos Alberola-Lopez} } @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} } @conference {589, title = {Tissue and Label Modelling for Segmentation of Scar with Contour Correction in Cardiac DE-CMR Volumes}, booktitle = {XXXIII Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2015}, month = {11/2015}, address = {Madrid, Spain}, abstract = {Most scar segmentation methods for cardiac DE-CMR images are constrained by a previous myocardial segmentation that provides borders for the scar identification. However, the automation of these methods rely on an existing myocardial segmentation from CINE-CMR that is registered to the DE-CMR volume; in this step, however, alignment errors are usually introduced and they carry over to the labeling operation. These errors typically remain unchanged after tissue labeling, so inconsistencies between the two (alignment and labeling) may exist. We explore this issue and present a method that, with the same inputs, identifies the healthy and scarred tissue and selectively corrects the endocardial and epicardial contours depending on the image edges, the estimated probabilistic distributions and the proximity to the aligned myocardial borders. For this, we model the posterior probability of each ROI label with a Bayesian approach that unifies the prior tissue probabilities and the myocardial labels. The maximum a posteriori criterion is used to compute a first DE-CMR label map, which is afterwards refined by a connected component analysis. Preliminary results show better accuracy for the endocardial and epicardial contours, and the segmented scar compares favorably with respect to state of the art methods.}, author = {S. Merino-Caviedes and Lucilio Cordero-Grande and P{\'e}rez Rodr{\'\i}guez, M. T. and T. Sevilla-Ruiz and A. Revilla-Orodea and Mart{\'\i}n-Fern{\'a}ndez, M. and Carlos Alberola-Lopez} } @conference {656, title = {Tissue and Label Modelling for Segmentation of Scar with Contour Correction in Cardiac DE-CMR Volumes}, booktitle = {XXXIII Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2015}, month = {2015}, address = {Madrid, Spain}, author = {S. Merino-Caviedes and Lucilio Cordero-Grande and P{\'e}rez Rodr{\'\i}guez, M Teresa and T. Sevilla-Ruiz and A. Revilla-Orodea and Mart{\'\i}n-Fern{\'a}ndez, Marcos and Carlos Alberola-Lopez} } @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} } @conference {426, title = {MOWHARP: Multi-Oriented Windowed HARP Reconstruction for Robust Strain Imaging}, booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine 22}, year = {2014}, author = {Lucilio Cordero-Grande and J Royuela-del-Val and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @article {445, title = {Multi-stencil streamline fast marching: a general 3D framework to determine myocardial thickness and transmurality in late enhancement images}, journal = {Medical Imaging, IEEE Transactions on}, volume = {33}, year = {2014}, pages = {23{\textendash}37}, author = {S. Merino-Caviedes and Lucilio Cordero-Grande and A. Revilla-Orodea and Perez, M. T. and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @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} } @conference {570, title = {An efficient user-customisable multiresolution classifier fall detection and diagnostic system}, booktitle = {26th IEEE International Conference on Microelectronics (ICM 2014)}, year = {2014}, address = {Doha, Qatar}, author = {Gibson, Ryan M and Amira, Abbes and Pablo Casaseca-de-la-Higuera and Ramzan, Naeem and Pervez, Zeeshan} } @article {ruiz2013advanced, title = {Advanced signal processing methods for biomedical imaging}, journal = {International journal of biomedical imaging}, volume = {2013}, year = {2013}, publisher = {Hindawi Publishing Corporation}, author = {Juan Ruiz-Alzola and Carlos Alberola-Lopez and Carl-Fredik Westin} } @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 {444, title = {Assessment of the fibrotic myocardial tissue mechanics by image processing}, booktitle = {Computing in Cardiology Conference (CinC), 2013}, year = {2013}, publisher = {IEEE}, organization = {IEEE}, author = {Lucilio Cordero-Grande and T. Sevilla-Ruiz and Revilla, Ana and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @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 {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 {434, title = {Dise{\~n}o e implementaci{\'o}n de plugins en el entorno GIMIAS para procesado y visualizaci{\'o}n de se{\~n}ales electrofisiol{\'o}gicas asociadas a problemas cardiovasculares}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2012}, publisher = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, organization = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, address = {San Sebasti{\'a}n, Espa{\~n}a}, author = {Mart{\'\i}n-Hern{\'a}ndez, Noelia and J Royuela-del-Val and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Federico Simmross-Wattenberg and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @proceedings {581, title = {TDAH en atenci{\'o}n primaria: Registro de la Actividad mediante actimetr{\'\i}a ambulatoria. Mesa Redonda. Ponencia Invitada}, volume = {61}, year = {2012}, pages = {34-39}, address = {Granada, Spain}, author = {Susana Alberola-Lopez and Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Irene Casares-Alonso and Isabel P{\'e}rez-Garc{\'\i}a and Alfredo Cano-Garcinu{\~n}o and L{\'o}pez-Villalobos, Jos{\'e} Antonio and Ruiz, F.C. and Jesus Maria Andres-de-Llano and Carlos Alberola-Lopez and Julio Ardura-Fernández} } @proceedings {576, title = {Automatic diagnosis of ADHD based on nonlinear analysis of actimetry registries}, volume = {32}, year = {2011}, pages = {685-688}, address = {Prague, Czech Rep.}, keywords = {ADHD, ADHD automatic diagnosis, Activity/Rest Detection, Attention-Deficit Hyperactivity Disorder, Automatic Diagnosis System, Central Tendency Measure, Feature extraction, Histograms, Indexes, Noise, Pediatrics, Regularity Assessment, Sleep, USA Councils, Wrist, actimetry registries, adolescence, automatic activity/rest detection filter, biomedical measurement, childhood, feature extraction module, medical disorders, medical signal processing, mental health problem, neurophysiology, nonlinear analysis, nonlinear regularity quantification, paediatrics, pathology, signal processing methods}, author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Susana Alberola-Lopez and L{\'o}pez-Villalobos, J.A. and Ruiz, F.C. and Jesus Maria Andres-de-Llano and Jose Ramon Garmendia-Leiza and Julio Ardura-Fernández} } @conference {585, title = {Intra Heartbeat Variability as a Tool for Cardiovascular Diagnosis and Monitoring}, booktitle = {XXIX Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, volume = {29}, year = {2011}, pages = {343-346}, address = {C{\'a}ceres, Spain}, author = {Daniel Ruiz-Aguado and Marcos Martin-Fern{\'a}ndez and J Royuela-del-Val and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @article {424, title = {Leaf classification in sunflower crops by computer vision and neural networks}, journal = {Computers and Electronics in Agriculture}, volume = {78}, year = {2011}, pages = {9-18}, abstract = {

In this article, we present an automatic leaves image classification system for sunflower crops using neural networks, which could be used in selective herbicide applications. The system is comprised of four main stages. First, a segmentation based on rgb color space is performed. Second, many different features are detected and then extracted from the segmented image. Third, the most discriminable set of features are selected. Finally, the Generalized Softmax Perceptron (GSP) neural network architecture is used in conjunction with the recently proposed Posterior Probability Model Selection (PPMS) algorithm for complexity selection in order to select the leaves in an image and then classify them either as sunflower or non-sunflower. The experimental results show that the proposed system achieves a high level of accuracy with only five selected discriminative features obtaining an average Correct Classification Rate of 85\% and an area under the receiver operation curve over 90\%, for the test set. {\^A}{\textcopyright} 2011 Elsevier B.V.

}, keywords = {Classification rates, Computer vision, Crops, Discriminative features, Generalized softmax perceptron, Helianthus, Herbicide application, Herbicides, Image classification, Image classification systems, Leaf classification, Learning machines, Model selection, Network architecture, Neural networks, Posterior probability, RGB color space, Segmented images, Sunflower, Test sets, accuracy assessment, agricultural technology, algorithm, artificial neural network, automation, dicotyledon, experimental study, herbicide, segmentation}, issn = {01681699}, doi = {10.1016/j.compag.2011.05.007}, url = {https://www.sciencedirect.com/science/article/pii/S0168169911001220}, author = {J I Arribas and G V Sanchez-Ferrero and G Ruiz-Ruiz and Jaime Gomez-Gil} } @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} } @article {cordero2011unsupervised, title = {Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model}, journal = {Medical image analysis}, volume = {15}, number = {3}, year = {2011}, pages = {283{\textendash}301}, publisher = {Elsevier}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Alberto San-Rom{\'a}n-Calvar, J and A. Revilla-Orodea and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @article {cardenes2010analysis, title = {Analysis of the pyramidal tract in tumor patients using diffusion tensor imaging}, journal = {NeuroImage}, volume = {50}, number = {1}, year = {2010}, pages = {27{\textendash}39}, publisher = {Elsevier}, author = {Rub{\'e}n C{\'a}rdenes-Almeida and Emma Mu{\~n}oz-Moreno and Sarabia-Herrero, Rosario and Rodr{\'\i}guez-Velasco, Margarita and Juan Jos{\'e} Fuertes-Alija and Marcos Martin-Fernandez} } @article {cardenes2010fast, title = {Fast and accurate geodesic distance transform by ordered propagation}, journal = {Image and Vision Computing}, volume = {28}, number = {3}, year = {2010}, pages = {307{\textendash}316}, publisher = {Elsevier}, author = {Rub{\'e}n C{\'a}rdenes-Almeida and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @proceedings {sosa2008strain, title = {Strain index: a new visualizing parameter for US elastography}, year = {2008}, pages = {69200W{\textendash}69200W}, publisher = {International Society for Optics and Photonics}, author = {Dario Sosa-Cabrera and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @proceedings {sosa2008strain, title = {Strain index: a new visualizing parameter for US elastography}, volume = {6920}, year = {2008}, pages = {6920}, publisher = {International Society for Optical Engineering; 1999}, author = {Dario Sosa-Cabrera and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @article {martin2007sequential, title = {Sequential anisotropic Wiener filtering applied to 3D MRI data}, journal = {Magnetic resonance imaging}, volume = {25}, number = {2}, year = {2007}, pages = {278{\textendash}292}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Carl-Fredik Westin} } @inbook {westin2006tensor, title = {Tensor field regularization using normalized convolution and markov random fields in a bayesian framework}, booktitle = {Visualization and Processing of Tensor Fields}, year = {2006}, pages = {381{\textendash}398}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Carl-Fredik Westin and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Knutsson, Hans} } @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} } @article {ruiz2005kriging, title = {Kriging filters for multidimensional signal processing}, journal = {Signal Processing}, volume = {85}, number = {2}, year = {2005}, pages = {413{\textendash}439}, publisher = {Elsevier}, author = {Juan Ruiz-Alzola and Carlos Alberola-Lopez and Carl-Fredik Westin} } @inbook {ruiz2005landmark, title = {Landmark-Based Registration of Medical-Image Data}, booktitle = {Medical Image Analysis Methods, Edited by Lena Costaridou, CRC Press}, year = {2005}, author = {Juan Ruiz-Alzola and Suarez-Santana, E and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {luis2005poster, title = {Poster Presentations 2-Pattern Recognition, Image Processing, and Applications-Tensor Processing for Texture and Colour Segmentation}, journal = {Lecture Notes in Computer Science}, volume = {3540}, year = {2005}, pages = {1117{\textendash}1127}, publisher = {Berlin: Springer-Verlag, 1973-}, author = {Rodrigo de Luis-Garc{\'\i}a and Deriche, Rachid and Rousson, Mikael and Carlos Alberola-Lopez} } @inbook {de2005tensor, title = {Tensor processing for texture and colour segmentation}, booktitle = {Image Analysis}, year = {2005}, pages = {1117{\textendash}1127}, publisher = {Springer}, organization = {Springer}, author = {Rodrigo de Luis-Garc{\'\i}a and Deriche, Rachid and Rousson, Mikael and Carlos Alberola-Lopez} } @article {alberola2004comments, title = {Comments on: A methodology for evaluation of boundary detection algorithms on medical images}, journal = {Medical Imaging, IEEE Transactions on}, volume = {23}, number = {5}, year = {2004}, pages = {658{\textendash}660}, publisher = {IEEE}, author = {Carlos Alberola-Lopez and Marcos Martin-Fernandez and Juan Ruiz-Alzola} } @article {de2003biometric, title = {Biometric identification systems}, journal = {Signal Processing}, volume = {83}, number = {12}, year = {2003}, pages = {2539{\textendash}2557}, publisher = {Elsevier}, author = {Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez and Aghzout, Otman and Juan Ruiz-Alzola} } @inbook {ruiz2003geostatistical, title = {Geostatistical medical image registration}, booktitle = {Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003}, year = {2003}, pages = {894{\textendash}901}, publisher = {Springer}, organization = {Springer}, author = {Juan Ruiz-Alzola and Suarez, Eduardo and Carlos Alberola-Lopez and Warfield, Simon K and Carl-Fredik Westin} } @inbook {martin2003regularization, title = {Regularization of Diffusion Tensor Maps Using a Non-Gaussian Markov Random Field Approach}, booktitle = {Lecture Notes in Computer Science}, volume = {2879}, year = {2003}, pages = {92{\textendash}100}, publisher = {Berlin: Springer-Verlag}, organization = {Berlin: Springer-Verlag}, author = {Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Carl-Fredik Westin} } @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 {san2003theoretical, title = {A theoretical framework to three-dimensional ultrasound reconstruction from irregularly sampled data}, journal = {Ultrasound in medicine \& biology}, volume = {29}, number = {2}, year = {2003}, pages = {255{\textendash}269}, publisher = {Elsevier}, author = {Raul San Jose-Estepar and Marcos Martin-Fernandez and Caballero-Mart{\'\i}nez, P Pablo and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @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} } @conference {martin2001multiresolution, title = {Multiresolution compression schemes for medical image and volume data}, booktitle = {Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE}, volume = {3}, year = {2001}, pages = {2437{\textendash}2440}, publisher = {IEEE}, organization = {IEEE}, author = {Miguel Angel Martin-Fernandez and Carlos Alberola-Lopez and Sanz de Acedo, Jorge and Juan Ruiz-Alzola} } @conference {san2001reshaping, title = {Reshaping polygonal meshes with smoothed normals extracted from ultrasound volume data: An optimization approach}, booktitle = {Medical Imaging 2001}, year = {2001}, pages = {462{\textendash}472}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Raul San Jose-Estepar and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @article {martín2001topology, title = {A topology-based filling algorithm}, journal = {Computers \& Graphics}, volume = {25}, number = {3}, year = {2001}, pages = {493{\textendash}509}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Miguel Angel Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @conference {martin2000coding, title = {Coding technique with progressive reconstruction based on VQ and entropy coding applied to medical images}, booktitle = {International Symposium on Optical Science and Technology}, year = {2000}, pages = {171{\textendash}181}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Marcos Martin-Fernandez and Carlos Alberola-Lopez and Guerrero-Rodriguez, David and Juan Ruiz-Alzola} } @conference {martin2000energy, title = {Energy functions for the segmentation of ultrasound volume data using active rays}, booktitle = {Acoustics, Speech, and Signal Processing, 2000. ICASSP{\textquoteright}00. Proceedings. 2000 IEEE International Conference on}, volume = {6}, year = {2000}, pages = {2274{\textendash}2277}, publisher = {IEEE}, organization = {IEEE}, author = {Marcos Martin-Fernandez and Rodriguez, E and Tejada, D and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @conference {estepar2000kalman, title = {Kalman filter technique applied to surface reconstruction and visualization from noisy volume data}, booktitle = {Medical Imaging 2000}, year = {2000}, pages = {396{\textendash}407}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Raul San Jose-Estepar and Rivera, Alberto and Pinacho, Marco and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @article {ruiz2000model, title = {Model-based stereo-visual tracking: Covariance analysis and tracking schemes}, journal = {Signal Processing}, volume = {80}, number = {1}, year = {2000}, pages = {23{\textendash}43}, publisher = {Elsevier}, author = {Juan Ruiz-Alzola and Carlos Alberola-Lopez and Corredera, Jose-Ram{\'o}n Casar} } @inbook {alberola2000disnei, title = {disnei: A collaborative environment for medical images analysis and visualization}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2000}, year = {2000}, publisher = {Springer}, organization = {Springer}, author = {Carlos Alberola-Lopez and Rub{\'e}n C{\'a}rdenes-Almeida and Marcos Martin-Fernandez and Miguel Angel Martin-Fernandez and Rodr{\'\i}guez-Florido, Miguel and Juan Ruiz-Alzola} } @conference {martin1999novel, title = {A novel error criterion for multiresolution volume data compression}, booktitle = {[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint}, volume = {2}, year = {1999}, pages = {1159{\textendash}vol}, publisher = {IEEE}, organization = {IEEE}, author = {Miguel Angel Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola} } @conference {450, title = {A Comparison of CFAR Strategies for Blob Detection in Textured Images}, booktitle = {European Signal Processing Conference (EUSIPCO), 1996}, year = {1996}, publisher = {Elsevier}, organization = {Elsevier}, author = {Carlos Alberola-Lopez and Casar-Corredera, Jos{\'e} Ramon and Juan Ruiz-Alzola} } @proceedings {alberola1996region, title = {A Region Oriented CFAR Approach to the Detection of Extensive Targets in Textured Images}, year = {1996}, pages = {195}, publisher = {Elsevier Science Ltd}, author = {Carlos Alberola-Lopez and Casar-Corredera, Jos{\'e} Ramon and Juan Ruiz-Alzola} }