@article {997, title = {Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning}, journal = {Medical Image Analysis}, volume = {94}, year = {2024}, pages = {103134}, abstract = {

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2*-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cram{\'e}r–Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.

}, keywords = {Brain, Diffusion-relaxation, Quantitative MRI, machine learning}, issn = {1361-8415}, doi = {https://doi.org/10.1016/j.media.2024.103134}, url = {https://www.sciencedirect.com/science/article/pii/S1361841524000598}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Maxime Descoteaux and Hugo Larochelle and Jana Hutter and Derek K. Jones and Chantal M.W. Tax} } @proceedings {988, title = {Comparison of data-driven and physics-informed learning approaches for optimising multi-contrast MRI acquisition protocols}, volume = {3701}, year = {2023}, month = {2023}, abstract = {

Multi-contrast MRI is used to assess the biological properties of tissues, but excessively long times are required to acquire high-quality datasets. To reduce acquisition time, physics-informed Machine Learning approaches were developed to select the optimal subset of measurements, decreasing the number of volumes by approximately 63\%, and predict the MRI signal and quantitative maps. These selection methods were compared to a full data-driven and two manual strategies. Synthetic and real 5D-Diffusion-T1-T2* data from five healthy participants were used. Feature selection via a combination of Machine Learning and physics modelling provides accurate estimation of quantitative parameters and prediction of MRI signal.

}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Descoteaux, Maxime and Aja-Fern{\'a}ndez, Santiago and Hutter, Jana and Jones, Derek K and Tax, Chantal M W} } @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} } @proceedings {984, title = {Tensors and Tracts at 64 mT}, volume = {104}, year = {2023}, month = {2023}, abstract = {

We present the first ever demonstration of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) including quantitative measures of mean diffusivity, fractional anisotropy, and successful tractographic reconstruction of projection and commissural pathways on a portable system operating at 64 mT.

}, author = {Plumley, Alix and Padormo, Francesco and Cercignani, Mara and O{\textquoteright}Halloran, Rafael and Teixeira, Rui and {\'A}lvaro Planchuelo-G{\'o}mez and Legouhy, Antoine and Luo, Tianrui and Jones, Derek K} } @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} } @article {980, title = {Viability of AMURA biomarkers from single-shell diffusion MRI in clinical studies}, journal = {Frontiers in Neuroscience}, volume = {17}, year = {2023}, pages = {1106350}, abstract = {

Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restrict the evaluation of complex structures. The objective of this study was to validate the reliability and robustness of complementary diffusion measures extracted with a novel approach, Apparent Measures Using Reduced Acquisitions (AMURA), with a typical diffusion MRI acquisition from a clinical context in comparison with DTI with application to clinical studies. Fifty healthy controls, 51 episodic migraine and 56 chronic migraine patients underwent single-shell diffusion MRI. Four DTI-based and eight AMURA-based parameters were compared between groups with tract-based spatial statistics to establish reference results. On the other hand, following a region-based analysis, the measures were assessed for multiple subsamples with diverse reduced sample sizes and their stability was evaluated with the coefficient of quartile variation. To assess the discrimination power of the diffusion measures, we repeated the statistical comparisons with a region-based analysis employing reduced sample sizes with diverse subsets, decreasing 10 subjects per group for consecutive reductions, and using 5,001 different random subsamples. For each sample size, the stability of the diffusion descriptors was evaluated with the coefficient of quartile variation. AMURA measures showed a greater number of statistically significant differences in the reference comparisons between episodic migraine patients and controls compared to DTI. In contrast, a higher number of differences was found with DTI parameters compared to AMURA in the comparisons between both migraine groups. Regarding the assessments reducing the sample size, the AMURA parameters showed a more stable behavior than DTI, showing a lower decrease for each reduced sample size or a higher number of regions with significant differences. However, most AMURA parameters showed lower stability in relation to higher coefficient of quartile variation values than the DTI descriptors, although two AMURA measures showed similar values to DTI. For the synthetic signals, there were AMURA measures with similar quantification to DTI, while other showed similar behavior. These findings suggest that AMURA presents favorable characteristics to identify differences of specific microstructural properties between clinical groups in regions with complex fiber architecture and lower dependency on the sample size or assessing technique than DTI.

}, issn = {1662-453X}, doi = {10.3389/fnins.2023.1106350}, url = {https://www.frontiersin.org/articles/10.3389/fnins.2023.1106350}, author = {Mart{\'\i}n-Mart{\'\i}n, Carmen and {\'A}lvaro Planchuelo-G{\'o}mez and Guerrero, {\'A}ngel L. and Garc{\'\i}a-Azor{\'\i}n, David and Trist{\'a}n-Vega, Antonio and de Luis-Garc{\'\i}a, Rodrigo and Aja-Fern{\'a}ndez, Santiago} } @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.} } @conference {974, title = {Data-driven and physics-informed learning of efficient acquisition protocols}, booktitle = {ISMRM Workshop on Diffusion MRI: From Research to Clinic}, year = {2022}, address = {Amsterdam, The Netherlands}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Descoteaux, Maxime and Aja-Fern{\'a}ndez, Santiago and Hutter, Jana and Jones, Derek K. and Tax, Chantal M.W.} } @article {954, title = {Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA}, journal = {Medical Image Analysis}, volume = {77}, year = {2022}, pages = {102356}, abstract = {

AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.

}, keywords = {AMURA, Diffusion anisotropy, Fast acquisition, diffusion MRI, white matter}, issn = {1361-8415}, doi = {https://doi.org/10.1016/j.media.2022.102356}, url = {https://www.sciencedirect.com/science/article/pii/S1361841522000093}, author = {Aja-Fern{\'a}ndez, Santiago and Pieciak, Tomasz and Mart{\'\i}n-Mart{\'\i}n, Carmen and {\'A}lvaro Planchuelo-G{\'o}mez and de Luis-Garc{\'\i}a, Rodrigo and Trist{\'a}n-Vega, Antonio} } @conference {976, title = {Tensors and Tracts at 64 mT}, booktitle = {ISMRM Workshop on Diffusion MRI: From Research to Clinic}, year = {2022}, address = {Amsterdam, The Netherlands}, author = {Plumley, Alix and Padormo, Francesco and Cercignani, Mara and O{\textquoteright}Halloran, Rafael and Teixeira, Rui and {\'A}lvaro Planchuelo-G{\'o}mez and Legouhy, Antoine and Luo, Tianrui and Jones, Derek K.} } @article {934, title = {Accurate free-water estimation in white matter from fast diffusion MRI acquisitions using the spherical means technique}, journal = {Magnetic Resonance in Medicine}, volume = {87}, year = {2021}, month = {2022}, pages = {1028-1035}, type = {Techncial Note}, abstract = {

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

}, keywords = {diffusion MRI, free water, spherical means, white matter}, doi = {https://doi.org/10.1002/mrm.28997}, author = {Antonio Trist{\'a}n-Vega and Guillem Par{\'\i}s and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez} } @article {899, title = {Apparent propagator anisotropy from single-shell diffusion MRI acquisitions}, journal = {Magnetic Resonance in Medicine}, volume = {85}, year = {2021}, month = {2021}, pages = {2869-2881}, chapter = {2869}, doi = {https://doi.org/10.1002/mrm.28620}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28620}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Derek K. Jones} } @article {900, title = {Efficient and accurate EAP imaging from multi-shell dMRI with Micro-Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT)}, journal = {NeuroImage}, volume = {227}, year = {2021}, month = {2021}, pages = {117616}, issn = {1053-8119}, doi = {https://doi.org/10.1016/j.neuroimage.2020.117616}, url = {http://www.sciencedirect.com/science/article/pii/S1053811920311010}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @article {947, title = {Magnetic Resonance Simulation in Education: Quantitative Evaluation of an Actual Classroom Experience}, journal = {Sensors}, volume = {21}, year = {2021}, pages = {6011}, abstract = {

Magnetic resonance is an imaging modality that implies a high complexity for radiographers. Despite some simulators having been developed for training purposes, we are not aware of any attempt to quantitatively measure their educational performance. The present study gives an answer to the question: Does an MRI simulator built on specific functional and non-functional requirements help radiographers learn MRI theoretical and practical concepts better than traditional educational method based on lectures? Our study was carried out in a single day by a total of 60 students of a main hospital in Madrid, Spain. The experiment followed a randomized pre-test post-test design with a control group that used a traditional educational method, and an experimental group that used our simulator. Knowledge level was assessed by means of an instrument with evidence of validity in its format and content, while its reliability was analyzed after the experiment. Statistical differences between both groups were measured. Significant statistical differences were found in favor of the participants who used the simulator for both the post-test score and the gain (difference between post-test and pre-test scores). The effect size turned out to be significant as well. In this work we evaluated a magnetic resonance simulation paradigm as a tool to help in the training of radiographers. The study shows that a simulator built on specific design requirements is a valuable complement to traditional education procedures, backed up with significant quantitative results.

}, author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Matute-Teresa, F{\'a}tima and Bote-Lorenzo, Miguel L and G{\'o}mez-S{\'a}nchez, Eduardo and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos} } @article {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.} } @proceedings {856, title = {AMURA with standard single-shell acquisition can detect changes beyond the Diffusion Tensor: a migraine clinical study}, volume = {4549}, year = {2020}, month = {2020}, abstract = {AMURA (Apparent Measures Using Reduced Acquisitions) is an alternative formulation to drastically reduce the number of samples needed for the estimation of diffusion properties related to the Ensemble Average diffusion Propagator (EAP). Although these measures were initially intended for medium-to-high b-values, in this work we evaluate their performance in DTI-like acquisitions. Fifty healthy controls, 54 episodic migraine (EM) and 56 chronic migraine (CM) patients were compared, using a single-shell diffusion scheme at b=1000 s/mm2. We compare AMURA measures (return-to-origin, return-to-axis and return-to-plane probabilities) to traditional DTI measures. Differences between EM and controls were only detectable using the return-to-origin probability.}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez} } @inbook {895, title = {Alternative Diffusion Anisotropy Metric from Reduced MRI Acquisitions}, booktitle = {Computational Diffusion MRI}, year = {2020}, pages = {13{\textendash}24}, publisher = {Springer, Cham}, organization = {Springer, Cham}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Rodrigo de Luis-Garc{\'\i}a and Derek K. Jones} } @article {904, title = {Deep Phenotyping of Headache in Hospitalized COVID-19 Patients via Principal Component Analysis}, journal = {Frontiers in Neurology}, volume = {11}, year = {2020}, pages = {1751}, abstract = {

Objectives: Headache is a common symptom in systemic infections, and one of the symptoms of the novel coronavirus disease 2019 (COVID-19). The objective of this study was to characterize the phenotype of COVID-19 headache via machine learning.Methods: We performed a cross-sectional study nested in a retrospective cohort. Hospitalized patients with COVID-19 confirmed diagnosis who described headache were included in the study. Generalized Linear Models and Principal Component Analysis were employed to detect associations between intensity and self-reported disability caused by headache, quality and topography of headache, migraine features, COVID-19 symptoms, and results from laboratory tests.Results: One hundred and six patients were included in the study, with a mean age of 56.6 {\textpm} 11.2, including 68 (64.2\%) females. Higher intensity and/or disability caused by headache were associated with female sex, fever, abnormal platelet count and leukocytosis, as well as migraine symptoms such as aggravation by physical activity, pulsating pain, and simultaneous photophobia and phonophobia. Pain in the frontal area (83.0\% of the sample), pulsating quality, higher intensity of pain, and presence of nausea were related to lymphopenia. Pressing pain and lack of aggravation by routine physical activity were linked to low C-reactive protein and procalcitonin levels.Conclusion: Intensity and disability caused by headache attributed to COVID-19 are associated with the disease state and symptoms. Two distinct headache phenotypes were observed in relation with COVID-19 status. One phenotype seems to associate migraine symptoms with hematologic and inflammatory biomarkers of severe COVID-19; while another phenotype would link tension-type headache symptoms to milder COVID-19.

}, issn = {1664-2295}, doi = {10.3389/fneur.2020.583870}, url = {https://www.frontiersin.org/article/10.3389/fneur.2020.583870}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Trigo, Javier and Rodrigo de Luis-Garc{\'\i}a and {\'A}ngel L. Guerrero and Porta-Etessam, Jes{\'u}s and Garc{\'\i}a-Azor{\'\i}n, David} } @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} } @article {854, title = {Integration of an Intelligent Tutoring System in a Magnetic Resonance Simulator for Education: Technical Feasibility and User Experience}, journal = {Computer Methods and Programs in Biomedicine}, year = {2020}, pages = {105634}, doi = {https://doi.org/10.1016/j.cmpb.2020.105634}, url = {https://authors.elsevier.com/a/1bM7z_3sJeWiZh}, author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Bote-Lorenzo, Miguel L and G{\'o}mez-S{\'a}nchez, Eduardo and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos} } @article {844, title = {Micro-structure diffusion scalar measures from reduced MRI acquisitions}, journal = {PLOS ONE}, volume = {15}, year = {2020}, month = {2020}, pages = {1-25}, abstract = {

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

}, doi = {10.1371/journal.pone.0229526}, url = {https://doi.org/10.1371/journal.pone.0229526}, author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Maryam Afzali and Molendowska, Malwina and Tomasz Pieciak and Antonio Trist{\'a}n-Vega} } @article {841, title = {A Web-Based Educational Magnetic Resonance Simulator: Design, Implementation and Testing}, journal = {Journal of Medical Systems}, volume = {44}, year = {2020}, month = {2020}, pages = {9}, author = {Trece{\~n}o-Fern{\'a}ndez, Daniel and Calabia-del-Campo, Juan and Bote-Lorenzo, Miguel L and S{\'a}nchez, Eduardo G{\'o}mez and Rodrigo de Luis-Garc{\'\i}a and Alberola-L{\'o}pez, Carlos} } @inbook {818, title = {Return-to-Axis Probability Calculation from Single-Shell Acquisitions}, booktitle = {Computational Diffusion MRI}, year = {2019}, pages = {29-41}, publisher = {Springer}, organization = {Springer}, isbn = {978-3-030-05830-2}, doi = {10.1007/978-3-030-05831-9_3}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Molendowska, Malwina and Tomasz Pieciak and Luis-Garc{\'\i}a, Rodrigo} } @conference {815, title = {Single-Shell Return-to-the-Origin Probability Diffusion Mri Measure Under a Non-Stationary Rician Distributed Noise}, booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, year = {2019}, publisher = {IEEE}, organization = {IEEE}, author = {Tomasz Pieciak and Bogusz, Fabian and Antonio Trist{\'a}n-Vega and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez} } @conference {800, title = {Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps}, booktitle = {Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018}, year = {2018}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Hoyos-Barcel{\'o}, Carlos and S. Merino-Caviedes and Wang, Qi and Luo, Chunbo and Wang, Xinheng and Wang, Zhi} } @proceedings {759, title = {Return-to-the-origin probability calculation in single shell acquisitions}, year = {2018}, pages = {1414}, address = {Paris, France}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Malwina Molendowska and Tomasz Pieciak and Rodrigo de Luis-Garc{\'\i}a} } @article {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} } @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 {627, title = {Adjugate Diffusion Tensors for Geodesic Tractography in White Matter}, journal = {Journal of Mathematical Imaging and Vision}, volume = {54}, year = {2015}, pages = {1{\textendash}14}, abstract = {

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for geodesic tractography and connectivity analysis in white matter. We propose a metric tensor given by the adjugate rather than the previously proposed inverse diffusion tensor. The adjugate metric can also be employed in the sharpening framework. Tractography experiments on synthetic and real brain diffusion data show improvement for high-curvature tracts and in the vicinity of isotropic diffusion regions relative to most results for inverse (sharpened) diffusion tensors, and especially on real data. In addition, adjugate tensors are shown to be more robust to noise.

}, issn = {1573-7683}, doi = {10.1007/s10851-015-0586-8}, url = {http://dx.doi.org/10.1007/s10851-015-0586-8}, author = {Andrea Fuster and Tom Dela-Haije and Antonio Trist{\'a}n-Vega and Birgit Plantinga and Carl-Fredik Westin and Luc Florack} } @article {628, title = {Efficient and Robust Image Restoration Using Multiple-Feature L2-Relaxed Sparse Analysis Priors}, journal = {IEEE Transactions on Image Processing}, volume = {24}, year = {2015}, month = {Dec}, pages = {5046-5059}, abstract = {

We propose a novel formulation for relaxed analysis-based sparsity in multiple dictionaries as a general type of prior for images, and apply it for Bayesian estimation in image restoration problems. Our formulation of a l2 -relaxed l0 pseudo-norm prior allows for an especially simple maximum a posteriori estimation iterative marginal optimization algorithm, whose convergence we prove. We achieve a significant speedup over the direct (static) solution by using dynamically evolving parameters through the estimation loop. As an added heuristic twist, we fix in advance the number of iterations, and then empirically optimize the involved parameters according to two performance benchmarks. The resulting constrained dynamic method is not just fast and effective, it is also highly robust and flexible. First, it is able to provide an outstanding tradeoff between computational load and performance, in visual and objective, mean square error and structural similarity terms, for a large variety of degradation tests, using the same set of parameter values for all tests. Second, the performance benchmark can be easily adapted to specific types of degradation, image classes, and even performance criteria. Third, it allows for using simultaneously several dictionaries with complementary features. This unique combination makes ours a highly practical deconvolution method.

}, keywords = {Bayes methods, Bayesian estimation, Convergence, Dictionaries, Estimation, Kernel, L2-relaxed L0 pseudo norm, L2-relaxed L0 pseudo-norm prior, L2-relaxed sparse analysis priors, Maximum likelihood estimation, Optimization, Redundancy, computational load, constrained dynamic method, deconvolution, deconvolution method, dynamically evolving parameters, estimation loop, fast constrained dynamic algorithm, image restoration, iterative marginal optimization, iterative methods, maximum a posteriori estimation, mean square error, mean square error methods, multiple representations, multiple-feature L2-relaxed sparse analysis priors, optimisation, robust tunable parameters, structural similarity terms}, issn = {1057-7149}, doi = {10.1109/TIP.2015.2478405}, author = {Javier Portilla and Antonio Trist{\'a}n-Vega and Ivan W. Selesnick} } @article {567, title = {Impact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach}, journal = {PLoS ONE}, volume = {10}, year = {2015}, pages = {e0137905}, doi = {10.1371/journal.pone.0137905}, url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0137905}, author = {Gonzalo Barrio-Arranz and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Marcos Mart{\'\i}n-Fern{\'a}ndez and Santiago Aja-Fern{\'a}ndez} } @article {532, title = {Improving GRAPPA reconstruction by frequency discrimination in the ACS lines}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {10}, year = {2015}, month = {2015}, pages = {1699-1710}, chapter = {1699}, abstract = {
Purpose
GRAPPA is a well-known parallel imaging method that recovers the MR magnitude image from aliasing by using a weighted interpolation of the data in k-space. To estimate the optimal reconstruction weights, GRAPPA uses a band along the center of the k-space where the signal is sampled at the Nyquist rate, the so-called autocalibrated (ACS) lines. However, while the subsampled lines usually belong to the medium- to high-frequency areas of the spectrum, the ACS lines include the low-frequency areas around the DC component. The use for estimation and reconstruction of areas of the k-space with very different features may negatively affect the final reconstruction quality. We propose a simple, yet powerful method to eliminate reconstruction artifacts, based on the discrimination of the low-frequency spectrum.
Methods
The proposal to improve the estimation of the weights lays on a proper selection of the coefficients within the ACS lines, which advises discarding those points around the DC component. A simple approach is the elimination of a square window in the center of the k-space, although more developed approaches can be used.
Results
The method is tested using real multiple-coil MRI acquisitions. We empirically show this approach achieves great enhancement rates, while keeping the same complexity of the original GRAPPA and reducing the g-factor. The reconstruction is even more accurate when combined with other reconstruction methods. Improvement rates of 35\ \% are achieved for 32 ACS and acceleration rate of 3.
Conclusions
The method proposed highly improves the accuracy of the GRAPPA coefficients and therefore the final image reconstruction. The method is fully compatible with the original GRAPPA formulation and with other optimization methods proposed in literature, and it can be easily implemented into the commercial scanning software.
}, doi = {10.1007/s11548-015-1172-7}, author = {Santiago Aja-Fern{\'a}ndez and Daniel Garc{\'\i}a-Mart{\'\i}n and Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {568, title = {Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization}, journal = {PLoS ONE}, volume = {10}, year = {2015}, pages = {e0138910}, doi = {10.1371/journal.pone.0138910}, url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0138910}, author = {Canales-Rodr{\'\i}guez, Erick J. and Daducci, Alessandro and Stamatios N. Sotiropoulos and Caruyer, Emmanuel and Santiago Aja-Fern{\'a}ndez and Radua, Joaquim and Yurramendi Mendizabal, Jes{\'u}s M. and Iturria-Medina, Yasser and Melie-Garc{\'\i}a, Lester and Alem{\'a}n-G{\'o}mez, Yasser and J-P Thiran and Sarr{\'o}, Salvador and Pomarol-Clotet, Edith and Salvador, Raymond} } @article {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} } @article {aja2013effective, title = {Effective noise estimation and filtering from correlated multiple-coil MR data}, journal = {Magnetic resonance imaging}, volume = {31}, number = {2}, year = {2013}, pages = {272{\textendash}285}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and V{\'e}ronique Brion and Antonio Trist{\'a}n-Vega} } @inbook {garcia2013homeomorphic, title = {Homeomorphic Geometrical Transform for Collision Response in Surgical Simulation}, booktitle = {Pattern Recognition and Image Analysis}, year = {2013}, pages = {433{\textendash}440}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {tristan2013merging, title = {Merging squared-magnitude approaches to DWI denoising: An adaptive Wiener filter tuned to the anatomical contents of the image}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {507{\textendash}510}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and V{\'e}ronique Brion and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {brion2013noise, title = {Noise correction for HARDI and HYDI data obtained with multi-channel coils and Sum of Squares reconstruction: An anisotropic extension of the LMMSE}, journal = {Magnetic resonance imaging}, volume = {31}, number = {8}, year = {2013}, pages = {1360{\textendash}1371}, publisher = {Elsevier}, author = {V{\'e}ronique Brion and Poupon, Cyril and Riff, Olivier and Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Mangin, Jean-Fran{\c c}ois and Le Bihan, Denis and Poupon, Fabrice} } @conference {aja2013noise, title = {Noise estimation in magnetic resonance SENSE reconstructed data}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {1104{\textendash}1107}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega} } @conference {vegas2012anisotropic, title = {Anisotropic LMMSE denoising of MRI based on statistical tissue models}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {1519{\textendash}1522}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Marcos Martin-Fernandez and Cesar Palencia and Deriche, Rachid} } @conference {tristan2012deblurring, title = {Deblurring of probabilistic ODFs in quantitative diffusion MRI}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {932{\textendash}935}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {tristan2012efficient, title = {Efficient and robust nonlocal means denoising of MR data based on salient features matching}, journal = {Computer methods and programs in biomedicine}, volume = {105}, number = {2}, year = {2012}, pages = {131{\textendash}144}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {aja2012influence, title = {Influence of noise correlation in multiple-coil statistical models with sum of squares reconstruction}, journal = {Magnetic Resonance in Medicine}, volume = {67}, number = {2}, year = {2012}, pages = {580{\textendash}585}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega} } @article {tristan2012least, title = {Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals}, journal = {NeuroImage}, volume = {59}, number = {4}, year = {2012}, pages = {4032{\textendash}4043}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {casaseca2012optimal, title = {Optimal real-time estimation in diffusion tensor imaging}, journal = {Magnetic resonance imaging}, volume = {30}, number = {4}, year = {2012}, pages = {506{\textendash}517}, publisher = {Elsevier}, author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin and Raul San Jose-Estepar} } @article {Trist{\'a}nVega2011586, title = {Comments on: A locally constrained radial basis function for registration and warping of images}, journal = {Pattern Recognition Letters}, volume = {32}, number = {4}, year = {2011}, pages = {586 - 589}, keywords = {Interpolation kernels}, issn = {0167-8655}, doi = {http://dx.doi.org/10.1016/j.patrec.2010.11.012}, url = {http://www.sciencedirect.com/science/article/pii/S0167865510003788}, author = {Antonio Trist{\'a}n-Vega and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez} } @conference {aja2011noise, title = {Noise estimation in MR GRAPPA reconstructed data}, booktitle = {Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on}, year = {2011}, pages = {1815{\textendash}1818}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @inbook {brion2011parallel, title = {Parallel MRI noise correction: an extension of the LMMSE to non central $\chi$ distributions}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2011}, year = {2011}, pages = {226{\textendash}233}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {V{\'e}ronique Brion and Poupon, Cyril and Riff, Olivier and Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Mangin, Jean-Fran{\c c}ois and Le Bihan, Denis and Poupon, Fabrice} } @article {aja2011statistical, title = {Statistical noise analysis in GRAPPA using a parametrized noncentral Chi approximation model}, journal = {Magnetic resonance in medicine}, volume = {65}, number = {4}, year = {2011}, pages = {1195{\textendash}1206}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and W Scott Hoge} } @article {aja2010background, title = {About the background distribution in MR data: a local variance study}, journal = {Magnetic resonance imaging}, volume = {28}, number = {5}, year = {2010}, pages = {739{\textendash}752}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Antonio Trist{\'a}n-Vega} } @conference {aja2010dwi, title = {DWI acquisition schemes and diffusion tensor estimation: a simulation-based study}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE}, year = {2010}, pages = {3317{\textendash}3320}, publisher = {IEEE}, organization = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Pablo Casaseca-de-la-Higuera} } @article {tristan2010dwi, title = {DWI filtering using joint information for DTI and HARDI}, journal = {Medical Image Analysis}, volume = {14}, number = {2}, year = {2010}, pages = {205{\textendash}218}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {garcia2010nurbs, title = {NURBS for the geometrical modeling of a new family of Compact-Supported Radial Basis Functions for elastic registration of medical images}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE}, year = {2010}, pages = {5947{\textendash}5950}, publisher = {IEEE}, organization = {IEEE}, author = {Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @article {cardenes2010saturn, title = {Saturn: A software application of tensor utilities for research in neuroimaging}, journal = {Computer methods and programs in biomedicine}, volume = {97}, number = {3}, year = {2010}, pages = {264{\textendash}279}, publisher = {Elsevier}, author = {Rub{\'e}n C{\'a}rdenes-Almeida and Emma Mu{\~n}oz-Moreno and Antonio Trist{\'a}n-Vega and Marcos Martin-Fernandez} } @proceedings {aja2010statistical, title = {Statistical noise model in GRAPPA-reconstructed images}, year = {2010}, pages = {3859}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and W Scott Hoge} } @article {tristan2010new, title = {A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk{\textendash}Radon transform}, journal = {NeuroImage}, volume = {49}, number = {2}, year = {2010}, pages = {1301{\textendash}1315}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @article {martin2009addendum, title = {Addendum to {\textquotedblleft}Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data{\textquotedblright}[Medical Image Analysis 13 (2009) 19{\textendash}35]}, journal = {Medical image analysis}, volume = {13}, number = {6}, year = {2009}, pages = {910}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Emma Mu{\~n}oz-Moreno and Cammoun, Leila and J-P Thiran and Carl-Fredik Westin and Carlos Alberola-Lopez} } @inbook {tristan2009bias, title = {Bias of least squares approaches for diffusion tensor estimation from array coils in DT{\textendash}MRI}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {919{\textendash}926}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @inbook {tristan2009blurring, title = {On the Blurring of the Funk{\textendash}Radon Transform in Q{\textendash}Ball Imaging}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {415{\textendash}422}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @inbook {tristan2009design, title = {Design and construction of a realistic DWI phantom for filtering performance assessment}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {951{\textendash}958}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @article {tristan2009estimation, title = {Estimation of fiber orientation probability density functions in high angular resolution diffusion imaging}, journal = {NeuroImage}, volume = {47}, number = {2}, year = {2009}, pages = {638{\textendash}650}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @article {aja2009noise, title = {Noise estimation in single-and multiple-coil magnetic resonance data based on statistical models}, journal = {Magnetic resonance imaging}, volume = {27}, number = {10}, year = {2009}, pages = {1397{\textendash}1409}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Carlos Alberola-Lopez} } @article {martin2009sequential, title = {Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data}, journal = {Medical image analysis}, volume = {13}, number = {1}, year = {2009}, pages = {19{\textendash}35}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Emma Mu{\~n}oz-Moreno and Cammoun, Leila and J-P Thiran and Carl-Fredik Westin and Carlos Alberola-Lopez} } @book {aja2009tensors, title = {Tensors in image processing and computer vision}, year = {2009}, publisher = {Springer}, organization = {Springer}, author = {Santiago Aja-Fern{\'a}ndez and Rodrigo de Luis-Garc{\'\i}a and Tao, Dacheng and Li, Xuelong} } @proceedings {tristn2008fuzzy, title = {Fuzzy regularisation of deformation fields in image registration}, year = {2008}, pages = {1223{\textendash}30}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @inbook {tristan2008joint, title = {Joint LMMSE estimation of DWI data for DTI processing}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2008}, year = {2008}, pages = {27{\textendash}34}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {tristan2008local, title = {Local similarity measures for demons-like registration algorithms}, booktitle = {Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on}, year = {2008}, pages = {1087{\textendash}1090}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @proceedings {simmross2008modelling, title = {Modelling Network Traffic as alpha-Stable Stochastic Processes: An Approach Towards Anomaly Detection}, year = {2008}, pages = {25{\textendash}32}, author = {Federico Simmross-Wattenberg and Antonio Trist{\'a}n-Vega and Pablo Casaseca-de-la-Higuera and Juan Ignacio Asensio-P{\'e}rez and Marcos Martin-Fernandez and Yannis A Dimitriadis and Carlos Alberola-Lopez} } @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} } @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} } @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 {433, title = {On the estimation of joint probability density functions for multi-modal registration of medical images}, volume = {26}, year = {2008}, pages = {13-16}, publisher = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, address = {Valladolid, Spain}, author = {Antonio Trist{\'a}n-Vega and Federico Simmross-Wattenberg and Emma Mu{\~n}oz-Moreno and Pablo Casaseca-de-la-Higuera and Marcos Martin-Fernandez} } @article {422, title = {A radius and ulna TW3 bone age assessment system}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {55}, year = {2008}, pages = {1463-1476}, abstract = {

An end-to-end system to automate the well-known Tanner - Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well-founded feature selection mathematical criteria, based on the ideas of maximizing the classes{\textquoteright} separability. Third, bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed Posterior Probability Model Selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple-class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses. {\^A}{\textcopyright} 2006 IEEE.

}, keywords = {Age Determination by Skeleton, Aging, Algorithms, Artificial Intelligence, Automated, Bone, Bone age assessment, Clustering algorithms, Computer-Assisted, Humans, Model selection, Neural networks, Pattern recognition, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity and Specificity, Skeletal maturity, algorithm, article, artificial neural network, automation, bone age, bone maturation, childhood, instrumentation, radius, ulna}, issn = {00189294}, doi = {10.1109/TBME.2008.918554}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-42249094547\&partnerID=40\&md5=2cecfea5f75a61b048611f2391b00aed}, author = {Antonio Trist{\'a}n-Vega and J I Arribas} } @proceedings {aksoy2007dti, title = {DTI Application with Haptic Interfaces}, volume = {7}, year = {2007}, pages = {1{\textendash}9}, author = {Aksoy, Murat and Avcu, Neslehan and S. Merino-Caviedes and Diktas, Engin Deniz and Miguel Angel Martin-Fernandez and Girgin, S{\i}la and Marras, Ioannis and Emma Mu{\~n}oz-Moreno and Tekeli, Erkin and Acar, Burak} } @conference {479, title = {Ultrasound Based Intraoperative Brain Shift Correction}, booktitle = {Ultrasonics Symposium, 2007. IEEE}, year = {2007}, publisher = {IEEE}, organization = {IEEE}, author = {Gonz{\'a}lez, Javier and Dario Sosa-Cabrera and Ortega, Mario and Gil, Jose Antonio and Antonio Trist{\'a}n-Vega and Emma Mu{\~n}oz-Moreno and Rodrigo de Luis-Garc{\'\i}a} } @proceedings {cardenes2007usimagtool, title = {Usimagtool: an open source freeware software for ultrasound imaging and elastography}, year = {2007}, pages = {117{\textendash}127}, author = {Rub{\'e}n C{\'a}rdenes-Almeida and Antonio Trist{\'a}n-Vega and Ferrero, GVS and Santiago Aja-Fern{\'a}ndez} } @article {419, title = {A fast B-spline pseudo-inversion algorithm for consistent image registration}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {4673 LNCS}, year = {2007}, pages = {768-775}, abstract = {

Recently, the concept of consistent image registration has been introduced to refer to a set of algorithms that estimate both the direct and inverse deformation together, that is, they exchange the roles of the target and the scene images alternatively; it has been demonstrated that this technique improves the registration accuracy, and that the biological significance of the obtained deformations is also improved. When dealing with free form deformations, the inversion of the transformations obtained becomes computationally intensive. In this paper, we suggest the parametrization of such deformations by means of a cubic B-spline, and its approximated inversion using a highly efficient algorithm. The results show that the consistency constraint notably improves the registration accuracy, especially in cases of a heavy initial misregistration, with very little computational overload. {\^A}{\textcopyright} Springer-Verlag Berlin Heidelberg 2007.

}, keywords = {Approximation algorithms, Computational overload, Consistent registration, Constraint theory, Image registration, Inverse problems, Inverse transformation, Parameterization}, isbn = {9783540742715}, issn = {03029743}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149022572\&partnerID=40\&md5=627751cd7654872cbd9ee74a249752eb}, author = {Antonio Trist{\'a}n-Vega and J I Arribas} } @conference {417, title = {A radius and ulna skeletal age assessment system}, booktitle = {2005 IEEE Workshop on Machine Learning for Signal Processing}, year = {2005}, address = {Mystic, CT}, abstract = {

An end to end system to partially automate the TW3 bone age assessment procedure is proposed. The system comprises the detailed analysis of the two more important bones in TW3: the radius and ulna wrist bones. First, a generalization of K-means algorithm is presented to semi-automatically segment the contour of the bones and thus extract up to 89 features describing shapes and textures from bones. Second, a well-founded feature selection criterion based on the statistical properties of data is used in order to properly choose the most relevant features. Third, bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) Neural Network (NN) whose optimal complexity is estimated via the Posterior Probability Model Selection (PPMS) algorithm. We can then predict the different development stages in both radius and ulna, from which we are able to score and estimate the bone age of a patient in years and finally we compare the NN results with those from the pediatrician expert discrepancies. {\^A}{\textcopyright} 2005 IEEE.

}, keywords = {Algorithms, Bone, Feature extraction, Generalized Softmax Perceptron (GSP), Living systems studies, Neural networks, Probability Model Selection (PPMS), Skeletal age assessment system}, isbn = {0780395174; 9780780395176}, doi = {10.1109/MLSP.2005.1532903}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749052083\&partnerID=40\&md5=eefa29ac09f4efa304b613cf07ab8d10}, author = {Antonio Trist{\'a}n-Vega and J I Arribas} } @article {tardon2004novel, title = {A novel Markovian formulation of the correspondence problem in stereo vision}, journal = {Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on}, volume = {34}, number = {3}, year = {2004}, pages = {428{\textendash}436}, publisher = {IEEE}, author = {Lorenzo J Tard{\'o}n-Garc{\'\i}a and Javier Portillo-Garcia and Carlos Alberola-Lopez} } @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 {tardon1999markov, title = {Markov random fields and the disparity gradient constraint applied to stereo correspondence}, booktitle = {Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on}, volume = {3}, year = {1999}, pages = {901{\textendash}905}, publisher = {IEEE}, organization = {IEEE}, author = {Lorenzo J Tard{\'o}n-Garc{\'\i}a and Javier Portillo-Garcia and Carlos Alberola-Lopez} } @article {portillo1998efficient, title = {Efficient multispectral texture segmentation using multivariate statistics}, journal = {IEE Proceedings-Vision, Image and Signal Processing}, volume = {145}, number = {5}, year = {1998}, pages = {357{\textendash}364}, publisher = {IEE}, author = {Javier Portillo-Garcia and Juan I Trueba-Santander and de Miguel-Vela, G and Carlos Alberola-Lopez} } @conference {tardon1996hypothesis, title = {Hypothesis testing for coarse region estimation and stable point determination applied to Markovian texture segmentation}, booktitle = {Image Processing, 1996. Proceedings., International Conference on}, volume = {3}, year = {1996}, pages = {169{\textendash}172}, publisher = {IEEE}, organization = {IEEE}, author = {Lorenzo J Tard{\'o}n-Garc{\'\i}a and Javier Portillo-Garcia and Carlos Alberola-Lopez and Juan I Trueba-Santander} }