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dc.contributor.authorCanales Rodríguez, Erick Jorge
dc.contributor.authorDaducci, Alessandro
dc.contributor.authorSotiropoulos, Stamatios N.
dc.contributor.authorCaruyer, Emmanuel
dc.contributor.authorAja Fernández, Santiago
dc.contributor.authorRadua, Joaquim
dc.contributor.authorYurramendi Mendizabal, Jesús María
dc.contributor.authorIturria Medina, Yasser
dc.contributor.authorMelie García, Lester
dc.contributor.authorAlemán Gómez, Yasser
dc.contributor.authorThiran, Jean-Philippe
dc.contributor.authorSarró, Salvador
dc.contributor.authorPomarol-Clotet, Edith
dc.contributor.authorSalvador, Raymond
dc.date.accessioned2019-04-12T08:21:34Z
dc.date.available2019-04-12T08:21:34Z
dc.date.issued2015-10-15
dc.identifier.citationPLOS ONE 10(10) : (2015) // Article ID e0138910es_ES
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10810/32446
dc.description.abstractSpherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.es_ES
dc.description.sponsorshipThis work was supported by the Catalonian Government (2014SGR1573), several grants from the Plan Nacional de I+D+i and co-funded by the Instituto de Salud Carlos III-Subdireccion General de Evaluacion y Fomento de la Investigacion and the European Regional Development Fund (FEDER): Miguel Servet Research Contracts (CP10/00596 to EP-C, CP13/00018 to RS and CP14/00041 to JR) and Research Project Grants (PI14/00292 to JR, PI14/01148 to EP-C and PI14/01151 to RS). The funding organizations played no role in the study design, data collection and analysis, or manuscript approval.es_ES
dc.language.isoenges_ES
dc.publisherBiomed Centrales_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectfiber orientation distributionses_ES
dc.subjectweighted mries_ES
dc.subjectsquares reconstructiones_ES
dc.subjectq-spacees_ES
dc.subjectresolutiones_ES
dc.subjecttensores_ES
dc.subjectballes_ES
dc.subjectimageses_ES
dc.subjectmicrostructurees_ES
dc.subjecttractographyes_ES
dc.titleSpherical Deconvolution of MultichannelDiffusion MRI Data with Non-Gaussian NoiseModels and Spatial Regularizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder2015 Canales-Rodríguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedes_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138910es_ES
dc.identifier.doi10.1371/journal.pone.0138910
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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2015 Canales-Rodríguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Except where otherwise noted, this item's license is described as 2015 Canales-Rodríguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited