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dc.contributor.advisorÇukur, Tolga
dc.contributor.authorÖzbey, Muzaffer
dc.date.accessioned2021-09-09T08:16:53Z
dc.date.available2021-09-09T08:16:53Z
dc.date.copyright2021-08
dc.date.issued2021-08
dc.date.submitted2021-09-08
dc.identifier.urihttp://hdl.handle.net/11693/76506
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 62-82).en_US
dc.description.abstractMagnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach to alleviate this limitation is to recover high-quality images from data undersampled across various dimensions, most commonly the Fourier domain or contrast sets. A primary distinction among recovery methods is whether the anatomy is processed per volume or per cross-section. Volumetric models offer enhanced capture of global contextual information, but they can suffer from sub-optimal learning due to elevated model complexity. Cross-sectional models with lower complexity offer improved learning behavior, yet they ignore contextual information across the longitudinal dimension of the volume. Here, we introduce a novel progressive volumetrization strategy for generative models (ProvoGAN) that serially decomposes complex volumetric image recovery tasks into succes-sive cross-sectional mappings task-optimally ordered across individual rectilinear dimensions. ProvoGAN effectively captures global context and recovers fine-structural details across all dimensions, while maintaining low model complexity and improved learning behaviour. Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior per-formance to state-of-the-art volumetric and cross-sectional models.en_US
dc.description.statementofresponsibilityby Muzaffer Özbeyen_US
dc.format.extentxxii, 82 leaves : illustrations (some color) ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMRIen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectSynthesisen_US
dc.subjectReconstructionen_US
dc.subjectModel complexityen_US
dc.titleDeep learning for accelerated 3D MRIen_US
dc.title.alternativeHızlandırılmış 3D MRG için derin öğrenmeen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB154114


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