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https://hdl.handle.net/1822/52844
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Campo DC | Valor | Idioma |
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dc.contributor.author | Lori, Nicolas Francisco | por |
dc.contributor.author | Lavrador, Rui | por |
dc.contributor.author | Fonseca, Lucia | por |
dc.contributor.author | Santos, Carlos | por |
dc.contributor.author | Travasso, Rui | por |
dc.contributor.author | Pereira, Artur | por |
dc.contributor.author | Rossetti, Rosaldo | por |
dc.contributor.author | Sousa, Nuno | por |
dc.contributor.author | Alves, Victor | por |
dc.date.accessioned | 2018-03-19T16:14:50Z | - |
dc.date.issued | 2016 | - |
dc.identifier.isbn | 978-3-319-31306-1 | - |
dc.identifier.issn | 2194-5357 | por |
dc.identifier.uri | https://hdl.handle.net/1822/52844 | - |
dc.description.abstract | Diffusion MRI (dMRI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter (WM) anatomy using tractography, thus being an important component of health informatics. In clinical settings, the computation time is critical, and so finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI) dMRI data processing is extremely relevant. We analyse here a method for reducing the computation of the dMRI-based axonal orientation distribution function h by using a Monte Carlo sampling-based methods for voxel selection, and so obtained a reduction in required data sampling of about 20%. In this work we show that the convergence to the correct value in this type of dMRI data-processing is linear and not exponential, implying that the Monte Carlo approach in this type of dMRI data processing improves its speed, but further improvements are needed. | por |
dc.description.sponsorship | We thank the financial support by QREN, FEDER, COMPETE, Investigador FCT, FCT Ciencia 2007, FCT PTDC/SAU-BEB/100147/2008, FCT Project Scope UID/CEC/00319/2013, and the ERASMUS projects (FCT stands for “Fundação para a Ciência e Tecnologia”). We are thankful the relevamt scientific conversations with Alard Roebroeck, Rainer Goebel, Van Wedeen, ReducingComputation Time byMonte Carlo Method ...103 and Gina Caetano. Data collection for this work was in part from the ”Human Connectome Project” (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876-PPCDTI/100147/PT | por |
dc.rights | restrictedAccess | por |
dc.subject | Axonal ODF | por |
dc.subject | Diffusion MRI | por |
dc.subject | Monte Carlo sampling methods | por |
dc.subject | Optimization | por |
dc.subject | White Matter | por |
dc.title | Reducing computation time by Monte Carlo method: an application in determining axonal orientation distribution function | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-31307-8_10 | por |
oaire.citationConferenceDate | 22 - 24 mar. 2016 | - |
sdum.event.location | Recife, Pernambuco, Brazil | - |
sdum.event.title | World Conference on Information Systems and Technologies (WorldCIST'16) | - |
oaire.citationStartPage | 95 | por |
oaire.citationEndPage | 105 | por |
oaire.citationVolume | 2 | por |
dc.date.updated | 2018-03-05T14:29:43Z | - |
dc.identifier.doi | 10.1007/978-3-319-31307-8_10 | por |
dc.identifier.eisbn | 978-3-319-31307-8 | - |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 4235 | - |
sdum.journal | Advances in Intelligent Systems and Computing | por |
sdum.conferencePublication | New Advances in Information Systems and Technologies | por |
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