@proceedings {990, title = {Assessing the variability of brain diffusion MRI preprocessing pipelines using a Region-of-Interest analysis}, volume = {5015}, year = {2023}, month = {2023}, abstract = {

The lack of a standardized preprocessing pipeline is a significant source of variability that might lower the reproducibility of studies, especially across sites and with incomplete description of the preprocessing workflows. We evaluate the downstream impact of variability in preprocessing workflow by quantifying the reproducibility and variability of region-of-interest (ROI) analyses. While many pipelines achieve excellent reproducibility in most ROI, we observed a large variability in performance of preprocessing workflows to the extent that some pipelines are detrimental to the data quality and reproducibility.

}, author = {Veraart, Jelle and Winzeck, Stephan and {\'A}lvaro Planchuelo-G{\'o}mez and Fricke, Bj{\"o}rn and Kornaropoulos, Evgenios N and Merisaari, Harri and Pieciak, Tomasz and Zou, Yukai and Descoteaux, Maxime} } @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} } @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 {967, title = {Objective measurement of pain related to cardiac surgery: a study using algometry}, booktitle = {8th Congress of the European Academy of Neurology}, year = {2022}, month = {2022}, abstract = {

Background and aims: Algometry is a safe and objective technique to quantify pain, up to now used in headache research, but to a lesser extent to assess pain related to surgery. We aimed to analyze the demographic characteristics of pain related to cardiac surgery, assessed using static algometry.

Methods: Adult patients consecutively undergoing cardiac surgery were prospectively recruited. Pressure pain thresholds (PPT) were measured in both sides of sternum manubrium, body (five measures) and xiphoid process, preoperatively and on days 1, 3 and 7 postoperatively. Linear mixed-effects models were employed to assess the longitudinal changes and results were corrected for multiple comparisons following a false discovery rate procedure.

Results: We included 70 patients (41.4\% female) with a median age of 67.5 years (range 26-85). Regarding the baseline values, PPT were significantly lower in women and patients older than 65 years. After the surgery, there was a significant reduction of PPT in all assessed regions, which was partially compensated after seven days. Moreover, postoperatively, differences associated with age disappeared and those associated with sex were almost negligible. These differences related to age and sex increased after seven days of surgery, but this difference was lower in comparison with the baseline situation (Table 1, Figure 1). Postoperative pain perception was significantly higher (lower PPT) in both sexes.

Conclusion: Pain related to cardiac surgery can be measured with algometry, mainly during first postoperative days. Differences in pain sensitivity related to age and sex decrease after surgery.

Disclosure: No conflict of interest.

}, author = {Segura-M{\'e}ndez, B{\'a}rbara and {\'A}lvaro Planchuelo-G{\'o}mez and Sierra, {\'A}lvaro and Garc{\'\i}a-Azor{\'\i}n, David and Velasco-Garc{\'\i}a, E. and Fuentes-Mart{\'\i}n, {\'A}. and S{\'a}nchez, C. and V{\'a}zquez-Alarc{\'o}n de la Lastra, I. and {\'A}ngel L. Guerrero and Carrascal, Yolanda} } @article {957, title = {Perceived quality of life (QOLIE-31-P), depression (NDDI-E), anxiety (GAD-7), and insomnia in patients with epilepsy attended at a refractory epilepsy unit in real-life clinical practice}, journal = {Neurological Sciences}, volume = {43}, year = {2022}, month = {2022}, pages = {1955-1964}, doi = {https://doi.org/10.1007/s10072-021-05595-3}, url = {https://link.springer.com/article/10.1007/s10072-021-05595-3}, author = {Gonzalez-Martinez, Alicia and {\'A}lvaro Planchuelo-G{\'o}mez and Vieira Campos, Alba and Mart{\'\i}nez-Dubarbie, Francisco and Vivancos, Jos{\'e} and De Toledo-Heras, Mar{\'\i}a} } @conference {945, title = {Longitudinal reduction of quality of life in patients with epilepsy and no seizure increase during the COVID-19 pandemic}, booktitle = {7th Congress of the European Academy of Neurology}, year = {2021}, month = {2021}, publisher = {Wiley}, organization = {Wiley}, address = {Virtual Congress}, abstract = {

Background and aims: In early 2020, the novel coronavirus disease (COVID-19) pandemic has impaired medical care of chronic neurological diseases, including epilepsy. The objective of this study is to evaluate the impact of the COVID-19 pandemic in the levels of anxiety, depression, somnolence and quality of life using validated scales in patients with epilepsy in real-life clinical practice.
Methods: Self-administered scales of anxiety disorders (GAD-7), depression (NDDI-E), somnolence (Epworth Sleepiness Scale; ESS) and quality of life (QOLIE-31-P) in patients with epilepsy treated in the Refractory Epilepsy Unit of a tertiary hospital were longitudinally analyzed with Generalized Linear Mixed Models. Data were collected before the beginning (December 2019-March 2020) and during the COVID-19 pandemic (September 2020-January 2021).
Results: 37 patients, 45.0+-17.3 years of age, 43.2\% women, epilepsy duration 23.0+-14.9 years, number of anti-epileptic drugs 2.1+-1.4, answered in the two periods. Significant longitudinal reduction of QOLIE-31-P scores (from 58.9+-19.7 to 56.2+-16.2, p=0.035) was identified. No statistically significant longitudinal changes in NDDI-E (from 12.3+-4.3 to 13.4+-4.4, p=0.293) or the number of seizures (from 0.9+-1.9 to 2.5+-6.2, p=0.125) were found. Significant higher ESS (from 4.9+-3.7 to 7.4+-4.9, p=0.001) and lower GAD-7 scores (from 8.8+-6.2 to 8.3+-5.9, corrected p=0.024 adjusted by refractory epilepsy and sleep disturbance) were found during the COVID-19 pandemic.
Conclusion: During the COVID-19 pandemic, quality of life was lower in patients with epilepsy, levels of anxiety were reduced and sleepiness levels were raised, without seizure change. Additional studies would be useful to adequately manage these comorbidities.
Disclosure: There is no disclosure.

}, url = {https://www.ean.org/fileadmin/user_upload/ean/congress-2021/EAN2021AbstractBook.pdf}, author = {Gonzalez-Martinez, Alicia and {\'A}lvaro Planchuelo-G{\'o}mez and Vieira, Alba and Mart{\'\i}nez-Dubarbie, Francisco and Vivancos, Jos{\'e} and De Toledo, Mar{\'\i}a} } @article {943, title = {Medium-term changes in patients with epilepsy during the COVID-19 pandemic}, journal = {Acta Neurologica Scandinavica}, volume = {144}, year = {2021}, pages = {450-459}, abstract = {

Objectives: The novel coronavirus disease (COVID-19) pandemic has led to social distancing measures and impaired medical care of chronic neurological diseases, including epilepsy, which may have adversely affected well-being and quality of life of patients with epilepsy (PWE). The objective of this study is to evaluate the impact of the COVID-19 pandemic in the levels of anxiety, depression, somnolence, and quality of life using validated scales in PWE in real-life clinical practice.

Materials \& Methods: Self-administered scales of anxiety disorders (GAD-7), depression (NDDI-E), somnolence (Epworth Sleepiness Scale; ESS), and quality of life (QOLIE-31-P) in PWE treated in a Refractory Epilepsy Unit were longitudinally analyzed. Data were collected before the beginning (December 2019-March 2020) and during the COVID-19 pandemic (September 2020-January 2021).

Results: 158 patients (85 from the first round and 73 from the second round) 45.0 +- 17.3 years of age, 43.2\% women, epilepsy duration 23.0 +- 14.9 years, number of antiepileptic drugs 2.1 +- 1.4, completed the survey. Significant longitudinal reduction of QOLIE-31-P (from 58.9 +- 19.7 to 56.2 +- 16.2, p = 0.035) and GAD-7 scores (from 8.8 +- 6.2 to 8.3 +- 5.9, corrected p = .024) was identified. No statistically significant longitudinal changes in the number of seizures (from 0.9 +- 1.9 to 2.5 +- 6.2, p = .125) or NDDI-E scores (from 12.3 +- 4.3 to 13.4 +- 4.4, p = .065) were found. Significant longitudinal increase of ESS (from 4.9 +- 3.7 to 7.4 +- 4.9, p = .001) was found.

Conclusions: During the COVID-19 pandemic, quality of life and anxiety levels were lower in PWE, and sleepiness levels were raised, without seizure change.

}, keywords = {Anxiety, COVID-19, Sleep, epilepsy, pandemic, quality of life}, doi = {https://doi.org/10.1111/ane.13481}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ane.13481}, author = {Gonzalez-Martinez, Alicia and {\'A}lvaro Planchuelo-G{\'o}mez and Vieira Campos, Alba and Mart{\'\i}nez-Dubarbie, Francisco and Vivancos, Jos{\'e} and De Toledo-Heras, Mar{\'\i}a} } @article {951, title = {Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy}, journal = {Scientific Reports}, volume = {11}, year = {2021}, month = {2021}, url = {https://www.nature.com/articles/s41598-021-97399-w}, author = {S. Merino-Caviedes and Guti{\'e}rrez, L. and Alfonso-Almaz{\'a}n, J. and Santiago Sanz-Est{\'e}banez and Lucilio Cordero-Grande and Quintanilla, J. and S{\'a}nchez-Gonz{\'a}lez, J. and Marina-Breysse, M. and Gal{\'a}n-Arriola, C. and Enr{\'\i}quez-V{\'a}zquez, D. and Torres, C. and Pizarro, G. and Ib{\'a}{\~n}ez, B. and Peinado, R. and Merino, J. and P{\'e}rez-Villacast{\'\i}n, J. and Jalife. J and L{\'o}pez-Yunta, M. and V{\'a}zquez, M. and Aguado-Sierra, J. and Gonz{\'a}lez-Ferrer, J. and P{\'e}rez-Castellano, N. and Mart{\'\i}n-Fern{\'a}ndez, M. and Alberola-L{\'o}pez, C and Filgueiras-Rama, D.} } @conference {917, title = {The utility of the GAD-7 anxiety, NDDI-E depression, Epworth sleepiness and QOLIE-31-P quality of life scales in patients with epilepsy in real clinical practice (2379)}, booktitle = {Proceedings of the American Academy of Neurology 2021 Virtual Annual Meeting}, year = {2021}, publisher = {Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology}, organization = {Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology}, abstract = {

Objective: The objective of this project is to study the presence of psychiatric comorbidity (anxiety and depression), somnolence and quality of life using validated scales in patients with epilepsy in real clinical practice, and its relationship with other clinical and demographic variables.

Background: Previous studies have shown that psychiatric comorbidity, specially anxiety and depression, as well as sleep disorders are more prevalent in patients with epilepsy than in the general population.

Design/Methods: Cross-sectional descriptive observational study using validated scales of anxiety disorders(GAD-7), depression(NDDI-E), sleep disorders(Epworth) and quality of life(QOLIE-31-P) in patients with epilepsy treated in the Refractory Epilepsy Unit of a tertiary hospital.

Results: We recruited 84 patients, age 44.3 {\textpm} 17.4 years, 48.2\% women, duration of epilepsy 21.5 {\textpm} 15.9 years, number of antiepileptic drugs 1.9 {\textpm} 1.2. We found severe anxiety(GAD-7\> 14) in 14.3\%, depression(NDDI-E\> 15) in 20.2\%; abnormal sleepiness(Epworth\> 10) in 14.3\% of patients, and QOLIE-31-P 62.0 {\textpm} 19.2. Each more point in GAD-7 is 21\% more likely to suffer from anxiety(OR 1.21; 95\% CI 1.09{\textendash}1.36; p = 0.0008), NDDI-E scores\<=15 represent 85 \% less chance of having depression(OR 0.15; 95\% CI 0.04{\textendash}0.51]; p = 0.002). We found a positive association between depression according to NDDI-E with seizure frequency(p = 0.017) and number of drugs(p = 0.019); and severe anxiety according to GAD-7 and number of drugs(p = 0.019). We found a negative correlation between QOLIE-31-P with NDDI-E(r = -0.68; p \<0.0001) and GAD-7(r = -0.76; p \<0.0001).

Conclusions: Validated scales in epilepsy for the detection of anxiety(GAD-7) and depression(NDDI-E) are useful in the detection of these disorders in real clinical practice. The assessment of the presence of anxiety-depressive psychiatric comorbidity is especially relevant in patients with a higher frequency of seizures, a greater number of drugs and a poorer quality of life.

}, url = {https://n.neurology.org/content/96/15_Supplement/2379}, author = {Gonzalez-Martinez, Alicia and {\'A}lvaro Planchuelo-G{\'o}mez and Mart{\'\i}nez-Dubarbie, Francisco and Vieira Campos, Alba and Vivancos, Jos{\'e} and De Toledo, Mar{\'\i}a} } @article {887, title = {Factors associated with the presence of headache in hospitalized COVID-19 patients and impact on prognosis: a retrospective cohort study}, journal = {The Journal of Headache and Pain}, volume = {21}, year = {2020}, month = {Jul}, pages = {94}, abstract = {Headache is one of the most frequent neurologic manifestations in COVID-19. We aimed to analyze which symptoms and laboratory abnormalities were associated with the presence of headache and to evaluate if patients with headache had a higher adjusted in-hospital risk of mortality.}, issn = {1129-2377}, doi = {10.1186/s10194-020-01165-8}, url = {https://doi.org/10.1186/s10194-020-01165-8}, author = {Trigo, Javier and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}lvaro Planchuelo-G{\'o}mez and Mart{\'\i}nez-P{\'\i}as, Enrique and Talavera, Blanca and Hern{\'a}ndez-P{\'e}rez, Isabel and Valle-Pe{\~n}acoba, Gonzalo and Sim{\'o}n-Campo, Paula and de Lera, Mercedes and Chavarr{\'\i}a-Miranda, Alba and L{\'o}pez-Sanz, Cristina and Guti{\'e}rrez-S{\'a}nchez, Mar{\'\i}a and Mart{\'\i}nez-Velasco, Elena and Pedraza, Mar{\'\i}a and Sierra, {\'A}lvaro and G{\'o}mez-Vicente, Beatriz and Juan F Arenillas and {\'A}ngel L. Guerrero} } @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} } @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 {781, title = {Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers}, journal = {Magnetic Resonance Imaging}, volume = {54}, year = {2018}, month = {2018}, pages = {194 - 213}, issn = {0730-725X}, doi = {https://doi.org/10.1016/j.mri.2018.03.001}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X18300262}, author = {Santiago Aja-Fern{\'a}ndez and Tomasz Pieciak and Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Vicente Molina and Rodrigo de Luis-Garc{\'\i}a} } @inbook {757, title = {Techniques for tracking: image registration}, booktitle = {Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video}, year = {2018}, publisher = {IET}, organization = {IET}, chapter = {15}, issn = {978-1-78561-290-9}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {616, title = {Influence of ultrasound speckle tracking strategies for motion and strain estimation}, journal = {Medical Image Analysis}, volume = {32}, year = {2016}, month = {2016}, pages = {184 - 200}, abstract = {

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

}, issn = {1361-8415}, doi = {http://dx.doi.org/10.1016/j.media.2016.04.002}, url = {http://www.sciencedirect.com/science/article/pii/S1361841516300202}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {654, title = {Spatially-variant noise filtering in Magnetic Resonance Imaging: A Consensus-based approach}, journal = {Knowledge-Based Systems}, year = {2016}, month = {2016}, doi = {http://dx.doi.org/10.1016/j.knosys.2016.05.053}, url = {http://www.sciencedirect.com/science/article/pii/S0950705116301575}, author = {Luis Gonz{\'a}lez-Jaime and Gonzalo Vegas-S{\'a}nchez-Ferrero and Etienne E. Kerre and Santiago Aja-Fern{\'a}ndez} } @book {660, title = {Statistical Analysis of Noise in MRI. Modeling, Filtering and Estimation}, year = {2016}, pages = {327}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Switzerland}, abstract = {

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

}, issn = {978-3-319-39933-1}, doi = {http://dx.doi.org/10.1007/978-3-319-39934-8}, url = {http://link.springer.com/book/10.1007/978-3-319-39934-8}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @conference {655, title = {Variance Stabilization of Noncentral-Chi Data: Application to Noise Estimation in MRI}, booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 2016}, year = {2016}, month = {2016}, address = {Prague, Czech Republic}, author = {Tomasz Pieciak and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {448, title = {Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images}, journal = {IEEE Transactions on image processing}, volume = {24}, year = {2015}, chapter = {345}, doi = {http://dx.doi.org/10.1109/TIP.2014.2371244}, author = {Gabriel Ramos-Llorden and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Santiago Aja-Fern{\'a}ndez} } @proceedings {543, title = {Blind Estimation of Spatially Variant Noise in GRAPPA MRI}, year = {2015}, pages = {SuAT7.4}, abstract = {

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

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

}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {532, title = {Improving GRAPPA reconstruction by frequency discrimination in the ACS lines}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {10}, year = {2015}, month = {2015}, pages = {1699-1710}, chapter = {1699}, abstract = {
Purpose
GRAPPA is a well-known parallel imaging method that recovers the MR magnitude image from aliasing by using a weighted interpolation of the data in k-space. To estimate the optimal reconstruction weights, GRAPPA uses a band along the center of the k-space where the signal is sampled at the Nyquist rate, the so-called autocalibrated (ACS) lines. However, while the subsampled lines usually belong to the medium- to high-frequency areas of the spectrum, the ACS lines include the low-frequency areas around the DC component. The use for estimation and reconstruction of areas of the k-space with very different features may negatively affect the final reconstruction quality. We propose a simple, yet powerful method to eliminate reconstruction artifacts, based on the discrimination of the low-frequency spectrum.
Methods
The proposal to improve the estimation of the weights lays on a proper selection of the coefficients within the ACS lines, which advises discarding those points around the DC component. A simple approach is the elimination of a square window in the center of the k-space, although more developed approaches can be used.
Results
The method is tested using real multiple-coil MRI acquisitions. We empirically show this approach achieves great enhancement rates, while keeping the same complexity of the original GRAPPA and reducing the g-factor. The reconstruction is even more accurate when combined with other reconstruction methods. Improvement rates of 35\ \% are achieved for 32 ACS and acceleration rate of 3.
Conclusions
The method proposed highly improves the accuracy of the GRAPPA coefficients and therefore the final image reconstruction. The method is fully compatible with the original GRAPPA formulation and with other optimization methods proposed in literature, and it can be easily implemented into the commercial scanning software.
}, doi = {10.1007/s11548-015-1172-7}, author = {Santiago Aja-Fern{\'a}ndez and Daniel Garc{\'\i}a-Mart{\'\i}n and Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {541, title = {A Maximum Likelihood Approach to Diffeomorphic Speckle Tracking for 3D Strain Estimation in Echocardiography}, journal = {Medical Image Analysis}, year = {2015}, pages = {-}, abstract = {

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

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

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


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

}, url = {http://www.insight-journal.org/browse/publication/955}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {513, title = {Spatially variant noise estimation in MRI: A homomorphic approach}, journal = {Medical Image Analysis}, volume = {20}, year = {2015}, pages = {184 - 197}, doi = {http://dx.doi.org/10.1016/j.media.2014.11.005}, author = {Santiago Aja-Fern{\'a}ndez and Tomasz Pieciak and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {534, title = {A local fuzzy thresholding methodology for multiregion image segmentation}, journal = {Knowledge-Based Systems}, volume = {83}, year = {2015}, month = {07/2015}, pages = {1-12}, abstract = {

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

}, issn = {0950-7051}, doi = {http://dx.doi.org/10.1016/j.knosys.2015.02.029}, url = {http://www.sciencedirect.com/science/article/pii/S095070511500129X}, author = {Santiago Aja-Fern{\'a}ndez and Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {curiale2014fully, title = {Fully Automatic Detection of Salient Features in 3-D Transesophageal Images}, journal = {Ultrasound in medicine \& biology}, volume = {40}, year = {2014}, month = {07/2014}, pages = {2868-2884}, publisher = {Elsevier}, chapter = {2868}, author = {Ariel H. Curiale and Haak, Alexander and Gonzalo Vegas-S{\'a}nchez-Ferrero and Ren, Ben and Santiago Aja-Fern{\'a}ndez and Johan G. Bosch} } @inbook {vegas2014gamma, title = {A Gamma Mixture Model for IVUS Imaging}, booktitle = {Multi-Modality Atherosclerosis Imaging and Diagnosis}, year = {2014}, pages = {155{\textendash}171}, publisher = {Springer New York}, organization = {Springer New York}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Sanches, J Miguel} } @article {vegas2014gamma, title = {Gamma mixture classifier for plaque detection in intravascular ultrasonic images}, journal = {Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions on}, volume = {61}, number = {1}, year = {2014}, pages = {44{\textendash}61}, publisher = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Seabra, Jose and Rodriguez-Leor, Oriol and Serrano-Vida, Angel and Santiago Aja-Fern{\'a}ndez and Palencia, C and Marcos Martin-Fernandez and Sanches, J} } @article {aja2014noise, title = {Noise estimation in parallel MRI: GRAPPA and SENSE}, journal = {Magnetic resonance imaging}, volume = {32}, number = {3}, year = {2014}, pages = {281{\textendash}290}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @article {aja2014statistical, title = {Statistical Noise Analysis in SENSE Parallel MRI}, journal = {arXiv preprint arXiv:1402.4067}, year = {2014}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @conference {vegas2013anisotropic, title = {Anisotropic diffusion filtering for correlated multiple-coil MRI}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {2956{\textendash}2959}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Gabriel Ramos-Llorden and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {gonzalez2013applying, title = {Applying a parametric approach for the task of nonstationary noise removal with missing information}, booktitle = {Computational Cybernetics (ICCC), 2013 IEEE 9th International Conference on}, year = {2013}, pages = {23{\textendash}28}, publisher = {IEEE}, organization = {IEEE}, author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {de2013atlas, title = {Atlas-based segmentation of white matter structures from DTI using tensor invariants and orientation}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {503{\textendash}506}, publisher = {IEEE}, organization = {IEEE}, author = {Rodrigo de Luis-Garc{\'\i}a and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @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} } @conference {tristan2013merging, title = {Merging squared-magnitude approaches to DWI denoising: An adaptive Wiener filter tuned to the anatomical contents of the image}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {507{\textendash}510}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and V{\'e}ronique Brion and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {aja2013noise, title = {Noise estimation in magnetic resonance SENSE reconstructed data}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {1104{\textendash}1107}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega} } @inbook {gonzalez2013parametric, title = {Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering}, booktitle = {Pattern Recognition and Image Analysis}, year = {2013}, pages = {358{\textendash}365}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {aja2013robust, title = {Robust estimation of MRI myocardial perfusion parameters}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {4382{\textendash}4385}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez} } @inbook {curiale2013speckle, title = {Speckle tracking in interpolated echocardiography to estimate heart motion}, booktitle = {Functional Imaging and Modeling of the Heart}, year = {2013}, pages = {325{\textendash}333}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {curiale2013strain, title = {Strain rate tensor estimation from echocardiography for quantitative assessment of functional mitral regurgitation}, booktitle = {Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on}, year = {2013}, pages = {788{\textendash}791}, publisher = {IEEE}, organization = {IEEE}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Teresa P{\'e}rez-Sanz and Santiago Aja-Fern{\'a}ndez} } @article {cordero2013magnetic, title = {A magnetic resonance software simulator for the evaluation of myocardial deformation estimation}, journal = {Medical engineering \& physics}, volume = {35}, number = {9}, year = {2013}, pages = {1331{\textendash}1340}, publisher = {Elsevier}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {cordero20123d, title = {3D fusion of cine and late-enhanced cardiac magnetic resonance images}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {286{\textendash}289}, publisher = {IEEE}, organization = {IEEE}, author = {Lucilio Cordero-Grande and S. Merino-Caviedes and Alba, X{\`e}nia and Figueras i Ventura, RM and Frangi, Alejandro F and Carlos Alberola-Lopez} } @conference {vegas2012anisotropic, title = {Anisotropic LMMSE denoising of MRI based on statistical tissue models}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {1519{\textendash}1522}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Cesar Palencia and Deriche, Rachid} } @conference {435, title = {Caracterizaci{\'o}n de speckle con modelos de cola pesada}, 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 = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Federico Simmross-Wattenberg and Marcos Martin-Fernandez and Palencia-de-Lara, C{\'e}sar and Carlos Alberola-Lopez} } @article {vegas2012generalized, title = {A Generalized Gamma Mixture Model for Ultrasonic Tissue Characterization}, journal = {Computational and mathematical methods in medicine}, volume = {2012}, year = {2012}, publisher = {Hindawi Publishing Corporation}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Cesar Palencia and Marcos Martin-Fernandez} } @article {cordero2012markov, title = {A Markov random field approach for topology-preserving registration: Application to object-based tomographic image interpolation}, journal = {Image Processing, IEEE Transactions on}, volume = {21}, number = {4}, year = {2012}, pages = {2047{\textendash}2061}, publisher = {IEEE}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @article {vegas2012direct, title = {A direct calculation of moments of the sample variance}, journal = {Mathematics and Computers in Simulation}, volume = {82}, number = {5}, year = {2012}, pages = {790{\textendash}804}, publisher = {North-Holland}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Palencia, C{\'e}sar} } @conference {cordero2011improving, title = {Improving Harmonic Phase Imaging by the Windowed Fourier Transform}, booktitle = {Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on}, year = {2011}, pages = {520{\textendash}523}, publisher = {IEEE}, organization = {IEEE}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @conference {aja2011noise, title = {Noise estimation in MR GRAPPA reconstructed data}, booktitle = {Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on}, year = {2011}, pages = {1815{\textendash}1818}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @conference {vegas2011realistic, title = {Realistic log-compressed law for ultrasound image recovery}, booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on}, year = {2011}, pages = {2029{\textendash}2032}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Lucilio Cordero-Grande and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Cesar Palencia} } @inbook {cordero2011topology, title = {Topology-preserving registration: a solution via graph cuts}, booktitle = {Combinatorial Image Analysis}, year = {2011}, pages = {420{\textendash}431}, publisher = {Springer}, organization = {Springer}, author = {Lucilio Cordero-Grande and Gonzalo Vegas-S{\'a}nchez-Ferrero and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} } @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 {aja2010background, title = {About the background distribution in MR data: a local variance study}, journal = {Magnetic resonance imaging}, volume = {28}, number = {5}, year = {2010}, pages = {739{\textendash}752}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @proceedings {515, title = {Characterization of activity epochs in actimetric registries for infantile colic diagnosis: Identification and feature extraction based on wavelets and symbolic dynamics}, volume = {32}, year = {2010}, pages = {2383-2386}, publisher = {IEEE}, author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Gonzalo Vegas-S{\'a}nchez-Ferrero and Lucilio Cordero-Grande and Jesus Maria Andres-de-Llano and Jose Ramon Garmendia-Leiza and Julio Ardura-Fernández} } @inbook {vegas2010probabilistic, title = {Probabilistic-driven oriented speckle reducing anisotropic diffusion with application to cardiac ultrasonic images}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2010}, year = {2010}, pages = {518{\textendash}525}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Frangi, Alejandro F and Cesar Palencia} } @conference {aja2010soft, title = {Soft thresholding for medical image segmentation}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE}, year = {2010}, pages = {4752{\textendash}4755}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Fernandez, Martin} } @conference {vegas2010influence, title = {On the influence of interpolation on probabilistic models for ultrasonic images}, booktitle = {Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on}, year = {2010}, pages = {292{\textendash}295}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Diego Mart{\'\i}n-Mart{\'\i}nez and Santiago Aja-Fern{\'a}ndez and Cesar Palencia} } @conference {merino2010variationally, title = {A variationally based weighted re-initialization method for geometric active contours}, booktitle = {Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on}, year = {2010}, pages = {908{\textendash}911}, publisher = {IEEE}, organization = {IEEE}, author = {S. Merino-Caviedes and Gonzalo Vegas-S{\'a}nchez-Ferrero and P{\'e}rez, M Teresa and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez} } @article {aja2009automatic, title = {Automatic noise estimation in images using local statistics. Additive and multiplicative cases}, journal = {Image and Vision Computing}, volume = {27}, number = {6}, year = {2009}, pages = {756{\textendash}770}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {tristan2008local, title = {Local similarity measures for demons-like registration algorithms}, booktitle = {Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on}, year = {2008}, pages = {1087{\textendash}1090}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {vegas2008strain, title = {Strain Rate Tensor estimation in cine cardiac MRI based on elastic image registration}, booktitle = {Computer Vision and Pattern Recognition Workshops, 2008. CVPRW{\textquoteright}08. IEEE Computer Society Conference on}, year = {2008}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega and Lucilio Cordero-Grande and Pablo Casaseca-de-la-Higuera and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {428, title = {The opportunity of grid services for CSCL-application development}, booktitle = {Parallel, Distributed and Network-Based Processing, 2005. PDP 2005. 13th Euromicro Conference on}, year = {2005}, publisher = {IEEE}, organization = {IEEE}, address = {Lausanne, Switzerland}, author = {Vaquero-Gonz{\'a}lez, Luis M and Hern{\'a}ndez-Leo, D and Federico Simmross-Wattenberg and Bote-Lorenzo, Miguel L and Juan Ignacio Asensio-P{\'e}rez and Yannis A Dimitriadis and G{\'o}mez-S{\'a}nchez, Eduardo and Vega-Gorgojo, Guillermo} }