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dc.contributor.authorGurney-Champion, Oliver J.
dc.contributor.authorLandry, Guillaume
dc.contributor.authorRedalen, Kathrine
dc.contributor.authorThorwarth, Daniela
dc.date.accessioned2023-01-26T10:16:15Z
dc.date.available2023-01-26T10:16:15Z
dc.date.created2022-08-23T13:08:17Z
dc.date.issued2022
dc.identifier.citationSeminars in Radiation Oncology. 2022, .en_US
dc.identifier.issn1053-4296
dc.identifier.urihttps://hdl.handle.net/11250/3046545
dc.description.abstractQuantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed for different qMRI tasks. Therefore, this article discusses the current state-of-the-art and potential future opportunities as well as challenges related to the use of deep learning in qMRI for target contouring, quantitative parameter estimation and also the generation of synthetic computerized tomography (CT) data based on MRI in personalized RT.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.titlePotential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapyen_US
dc.title.alternativePotential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.journalSeminars in Radiation Oncologyen_US
dc.identifier.doi10.1016/j.semradonc.2022.06.007
dc.identifier.cristin2045326
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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