@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} } @proceedings {856, title = {AMURA with standard single-shell acquisition can detect changes beyond the Diffusion Tensor: a migraine clinical study}, volume = {4549}, year = {2020}, month = {2020}, abstract = {AMURA (Apparent Measures Using Reduced Acquisitions) is an alternative formulation to drastically reduce the number of samples needed for the estimation of diffusion properties related to the Ensemble Average diffusion Propagator (EAP). Although these measures were initially intended for medium-to-high b-values, in this work we evaluate their performance in DTI-like acquisitions. Fifty healthy controls, 54 episodic migraine (EM) and 56 chronic migraine (CM) patients were compared, using a single-shell diffusion scheme at b=1000 s/mm2. We compare AMURA measures (return-to-origin, return-to-axis and return-to-plane probabilities) to traditional DTI measures. Differences between EM and controls were only detectable using the return-to-origin probability.}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Santiago Aja-Fern{\'a}ndez} } @inbook {895, title = {Alternative Diffusion Anisotropy Metric from Reduced MRI Acquisitions}, booktitle = {Computational Diffusion MRI}, year = {2020}, pages = {13{\textendash}24}, publisher = {Springer, Cham}, organization = {Springer, Cham}, author = {Santiago Aja-Fern{\'a}ndez and Antonio Trist{\'a}n-Vega and Rodrigo de Luis-Garc{\'\i}a and Derek K. Jones} } @article {891, title = {Alternative Microstructural Measures to Complement Diffusion Tensor Imaging in Migraine Studies with Standard MRI Acquisition}, journal = {Brain Sciences}, volume = {10}, year = {2020}, month = {2020}, pages = {711}, abstract = {The white matter state in migraine has been investigated using diffusion tensor imaging (DTI) measures, but results using this technique are conflicting. To overcome DTI measures, we employed ensemble average diffusion propagator measures obtained with apparent measures using reduced acquisitions (AMURA). The AMURA measures were return-to-axis (RTAP), return-to-origin (RTOP) and return-to-plane probabilities (RTPP). Tract-based spatial statistics was used to compare fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity from DTI, and RTAP, RTOP and RTPP, between healthy controls, episodic migraine and chronic migraine patients. Fifty healthy controls, 54 patients with episodic migraine and 56 with chronic migraine were assessed. Significant differences were found between both types of migraine, with lower axial diffusivity values in 38 white matter regions and higher RTOP values in the middle cerebellar peduncle in patients with a chronic migraine (p \< 0.05 family-wise error corrected). Significantly lower RTPP values were found in episodic migraine patients compared to healthy controls in 24 white matter regions (p \< 0.05 family-wise error corrected), finding no significant differences using DTI measures. The white matter microstructure is altered in a migraine, and in chronic compared to episodic migraine. AMURA can provide additional results with respect to DTI to uncover white matter alterations in migraine.}, issn = {2076-3425}, doi = {10.3390/brainsci10100711}, url = {https://www.mdpi.com/2076-3425/10/10/711}, author = {{\'A}lvaro Planchuelo-G{\'o}mez and Garc{\'\i}a-Azor{\'\i}n, David and {\'A}ngel L. Guerrero and Rodrigo de Luis-Garc{\'\i}a and Rodr{\'\i}guez, Margarita and Santiago Aja-Fern{\'a}ndez} } @conference {740, title = {ADC-Weighted Joint Registration-Estimation for Cardiac Diffusion Magnetic Resonance Imaging}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2017}, month = {12/2017}, abstract = {

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

}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and Jordi Broncano-Cabrero and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {448, title = {Anisotropic Diffusion Filter with Memory based on Speckle Statistics for Ultrasound Images}, journal = {IEEE Transactions on image processing}, volume = {24}, year = {2015}, chapter = {345}, doi = {http://dx.doi.org/10.1109/TIP.2014.2371244}, author = {Gabriel Ramos-Llorden and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Santiago Aja-Fern{\'a}ndez} } @article {de2014attention, title = {Attention Deficit/Hyperactivity Disorder and Medication with Stimulants in Young Children: A DTI Study}, journal = {Progress in Neuro-Psychopharmacology and Biological Psychiatry}, volume = {57}, year = {2015}, publisher = {Elsevier}, chapter = {176}, doi = {http://dx.doi.org/10.1016/j.pnpbp.2014.10.014}, author = {Rodrigo de Luis-Garc{\'\i}a and Cab{\'u}s-Pi{\~n}ol, Gemma and Imaz-Roncero, Carlos and Daniel Argibay-Qui{\~n}ones and Gonzalo Barrio-Arranz and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {vegas2013anisotropic, title = {Anisotropic diffusion filtering for correlated multiple-coil MRI}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {2956{\textendash}2959}, publisher = {IEEE}, organization = {IEEE}, author = {Gonzalo Vegas-S{\'a}nchez-Ferrero and Gabriel Ramos-Llorden and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez} } @conference {gonzalez2013applying, title = {Applying a parametric approach for the task of nonstationary noise removal with missing information}, booktitle = {Computational Cybernetics (ICCC), 2013 IEEE 9th International Conference on}, year = {2013}, pages = {23{\textendash}28}, publisher = {IEEE}, organization = {IEEE}, author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {de2013atlas, title = {Atlas-based segmentation of white matter structures from DTI using tensor invariants and orientation}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}, year = {2013}, pages = {503{\textendash}506}, publisher = {IEEE}, organization = {IEEE}, author = {Rodrigo de Luis-Garc{\'\i}a and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @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} } @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} } @article {aja2009automatic, title = {Automatic noise estimation in images using local statistics. Additive and multiplicative cases}, journal = {Image and Vision Computing}, volume = {27}, number = {6}, year = {2009}, pages = {756{\textendash}770}, publisher = {Elsevier}, author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {de2007p6d, title = {Analysis of Ultrasound Images Based on Local Statistics. Application to the Diagnosis of Developmental Dysplasia of the Hip}, booktitle = {Ultrasonics Symposium, 2007. IEEE}, year = {2007}, pages = {2531{\textendash}2534}, publisher = {IEEE}, organization = {IEEE}, author = {Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Rub{\'e}n C{\'a}rdenes-Almeida and Marcos Martin-Fernandez and Carlos Alberola-Lopez} }