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dc.creatorLori, Nicolás Francisco-
dc.creatorIbáñez Barassi, Agustín Mariano-
dc.creatorLavrador, Rui-
dc.creatorFonseca, Lucia-
dc.creatorSantos, Carlos-
dc.creatorTravasso, Rui-
dc.creatorPereira, Artur-
dc.creatorRossetti, Rosaldo-
dc.creatorSousa, Nuno-
dc.creatorAlves, Victor-
dc.date2018-06-15T14:57:31Z-
dc.date2018-06-15T14:57:31Z-
dc.date2016-11-
dc.date2018-06-06T19:41:55Z-
dc.date.accessioned2019-04-29T15:27:43Z-
dc.date.available2019-04-29T15:27:43Z-
dc.date.issued2018-06-15T14:57:31Z-
dc.date.issued2018-06-15T14:57:31Z-
dc.date.issued2016-11-
dc.date.issued2018-06-06T19:41:55Z-
dc.identifierLori, Nicolás Francisco; Ibáñez Barassi, Agustín Mariano; Lavrador, Rui; Fonseca, Lucia; Santos, Carlos; et al.; Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data; Springer; Journal Of Medical Systems; 40; 11; 11-2016; 1-8-
dc.identifier0148-5598-
dc.identifierhttp://hdl.handle.net/11336/48765-
dc.identifier1573-689X-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/294580-
dc.descriptionHigh Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal’s quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.-
dc.descriptionFil: Lori, Nicolás Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidade do Minho; Portugal. Universidad de Coimbra; Portugal. Instituto de Neurología Cognitiva; Argentina-
dc.descriptionFil: Ibáñez Barassi, Agustín Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Autónoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile. Australian Research Council; Australia-
dc.descriptionFil: Lavrador, Rui. Universidad de Coimbra; Portugal-
dc.descriptionFil: Fonseca, Lucia. Universidad de Coimbra; Portugal. Maastricht University; Países Bajos. Eindhoven University of Technology; Países Bajos-
dc.descriptionFil: Santos, Carlos. Universidad de Coimbra; Portugal-
dc.descriptionFil: Travasso, Rui. Universidad de Coimbra; Portugal-
dc.descriptionFil: Pereira, Artur. University of Aveiro; Portugal-
dc.descriptionFil: Rossetti, Rosaldo. Universidad de Porto; Portugal-
dc.descriptionFil: Sousa, Nuno. Universidade do Minho; Portugal-
dc.descriptionFil: Alves, Victor. Universidade do Minho; Portugal-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherSpringer-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1007/s10916-016-0594-2-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10916-016-0594-2-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.subjectAXONAL ODF-
dc.subjectDIFFUSION MRI-
dc.subjectMONTE CARLO SAMPLING METHODS-
dc.subjectOPTIMIZATION-
dc.subjectWHITE MATTER-
dc.subjectCiencias de la Computación-
dc.subjectCiencias de la Computación e Información-
dc.subjectCIENCIAS NATURALES Y EXACTAS-
dc.titleProcessing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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