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https://hdl.handle.net/1822/71244
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Campo DC | Valor | Idioma |
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dc.contributor.author | Pinto, Adriano | por |
dc.contributor.author | Pereira, Sergio | por |
dc.contributor.author | Meier, Raphael | por |
dc.contributor.author | Alves, Victor | por |
dc.contributor.author | Wiest, Roland | por |
dc.contributor.author | Silva, Carlos A. | por |
dc.date.accessioned | 2021-04-03T14:01:15Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Pinto A. et al. (2018) Enhancing Clinical MRI Perfusion Maps with Data-Driven Maps of Complementary Nature for Lesion Outcome Prediction. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11072. Springer, Cham. https://doi.org/10.1007/978-3-030-00931-1_13 | por |
dc.identifier.isbn | 978-3-030-00930-4 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/1822/71244 | - |
dc.description.abstract | Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient’s life. To perform the revascularization procedure, the decision making of physicians considers its risks and benefits based on multi-modal MRI and clinical experience. Therefore, automatic prediction of the ischemic stroke lesion outcome has the potential to assist the physician towards a better stroke assessment and information about tissue outcome. Typically, automatic methods consider the information of the standard kinetic models of diffusion and perfusion MRI (e.g. Tmax, TTP, MTT, rCBF, rCBV) to perform lesion outcome prediction. In this work, we propose a deep learning method to fuse this information with an automated data selection of the raw 4D PWI image information, followed by a data-driven deep-learning modeling of the underlying blood flow hemodynamics. We demonstrate the ability of the proposed approach to improve prediction of tissue at risk before therapy, as compared to only using the standard clinical perfusion maps, hence suggesting on the potential benefits of the proposed data-driven raw perfusion data modelling approach. | por |
dc.description.sponsorship | Adriano Pinto was supported by a scholarship from the Fundacao para a Ciencia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. | por |
dc.language.iso | eng | por |
dc.publisher | Springer, Cham | por |
dc.relation | UID/CEC/00319/2013. | por |
dc.rights | restrictedAccess | por |
dc.title | Enhancing clinical MRI perfusion maps with data-driven maps of complementary nature for lesion outcome prediction | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-00931-1_13 | por |
oaire.citationStartPage | 107 | por |
oaire.citationEndPage | 115 | por |
oaire.citationVolume | 11072 | por |
dc.identifier.doi | 10.1007/978-3-030-00931-1_13 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.eisbn | 978-3-030-00931-1 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Médica | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Lecture Notes in Computer Science | por |
sdum.conferencePublication | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, PT III | por |
oaire.version | CVoR | por |
dc.subject.ods | Saúde de qualidade | por |
Aparece nas coleções: | CMEMS - Artigos em livros de atas/Papers in proceedings |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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1806.04413.pdf Acesso restrito! | 1,91 MB | Adobe PDF | Ver/Abrir |