Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/71244

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dc.contributor.authorPinto, Adrianopor
dc.contributor.authorPereira, Sergiopor
dc.contributor.authorMeier, Raphaelpor
dc.contributor.authorAlves, Victorpor
dc.contributor.authorWiest, Rolandpor
dc.contributor.authorSilva, Carlos A.por
dc.date.accessioned2021-04-03T14:01:15Z-
dc.date.issued2018-
dc.identifier.citationPinto 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_13por
dc.identifier.isbn978-3-030-00930-4-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://hdl.handle.net/1822/71244-
dc.description.abstractStroke 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.sponsorshipAdriano 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.isoengpor
dc.publisherSpringer, Champor
dc.relationUID/CEC/00319/2013.por
dc.rightsrestrictedAccesspor
dc.titleEnhancing clinical MRI perfusion maps with data-driven maps of complementary nature for lesion outcome predictionpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-00931-1_13por
oaire.citationStartPage107por
oaire.citationEndPage115por
oaire.citationVolume11072por
dc.identifier.doi10.1007/978-3-030-00931-1_13por
dc.date.embargo10000-01-01-
dc.identifier.eisbn978-3-030-00931-1-
dc.subject.fosEngenharia e Tecnologia::Engenharia Médicapor
dc.subject.wosScience & Technologypor
sdum.journalLecture Notes in Computer Sciencepor
sdum.conferencePublicationMEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, PT IIIpor
oaire.versionCVoRpor
dc.subject.odsSaúde de qualidadepor
Aparece nas coleções:CMEMS - Artigos em livros de atas/Papers in proceedings

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