@proceedings {989, title = {Impact of free-water correction on white matter changes measured by diffusion tensor imaging in migraine}, volume = {4601}, year = {2023}, month = {2023}, abstract = {
Menstrual migraine affects about 25\% of female migraine patients. However, the diagnosis of migraine is particularly difficult because the brain changes associated with migraine are challenging to detect with imaging techniques. Diffusion-weighted MRI (dMRI) permits the detection of alterations in the microenvironment of the brain tissues. We investigate whether removing the contribution of the free water component from the diffusion-signal can provide increased sensitivity to identify white matter changes in migraine using diffusion tensor metrics.
}, author = {Guadilla, Irene and Fouto, Ana and {\'A}lvaro Planchuelo-G{\'o}mez and Trist{\'a}n-Vega, Antonio and Ruiz-Tagle, Amparo and Esteves, In{\^e}s and Caetano, Gina and Silva, Nuno and Vilela, Pedro and Gil-Gouveia, Raquel and Aja-Fern{\'a}ndez, Santiago and Figueiredo, Patr{\'\i}cia and Nunes, Rita} } @article {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.} } @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} } @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} } @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 {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} } @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 {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 = {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} } @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} }