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dc.contributor.advisorÇukur, Tolga
dc.contributor.authorDar, Salman Ul Hassan
dc.date.accessioned2021-03-29T07:48:45Z
dc.date.available2021-03-29T07:48:45Z
dc.date.copyright2021-02
dc.date.issued2021-02
dc.date.submitted2021-03-24
dc.identifier.urihttp://hdl.handle.net/11693/76014
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 177-201).en_US
dc.description.abstractMagnetic resonance imaging is a non-invasive imaging modality that enables multi-contrast acquisition of an underlying anatomy, thereby supplementing mul-titude of information for diagnosis. However, prolonged scan duration may pro-hibit its practical use. Two mainstream frameworks for accelerating MR image acquisitions are reconstruction and synthesis. In reconstruction, acquisitions are accelerated by undersampling in k-space, followed by reconstruction algorithms. Lately deep neural networks have offered significant improvements over tradi-tional methods in MR image reconstruction. However, deep neural networks rely heavily on availability of large datasets which might not be readily available for some applications. Furthermore, a caveat of the reconstruction framework in general is that the performance naturally starts degrading towards higher accel-eration factors where fewer data samples are acquired. In the alternative syn-thesis framework, acquisitions are accelerated by acquiring a subset of desired contrasts, and recovering the missing ones from the acquired ones. Current syn-thesis methods are primarily based on deep neural networks, which are trained to minimize mean square or absolute loss functions. This can bring about loss of intermediate-to-high spatial frequency content in the recovered images. Fur-thermore, the synthesis performance in general relies on similarity in relaxation parameters between source and target contrasts, and large dissimilarities can lead to artifactual synthesis or loss of features. Here, we tackle issues associated with reconstruction and synthesis approaches. In reconstruction, the data scarcity is-sue is addressed by pre-training a network on large readily available datasets, and fine-tuning on just a few samples from target datasets. In synthesis, the loss of intermediate-to-high spatial frequency is catered for by adding adversarial and high-level perceptual losses on top of traditional mean absolute error. Fi-nally, a joint reconstruction and synthesis approach is proposed to mitigate the issues associated with both reconstruction and synthesis approaches in general. Demonstrations on MRI brain datasets of healthy subjects and patients indicate superior performance of the proposed techniques over the current state-of-the art ones.en_US
dc.description.statementofresponsibilityby Salman Ul Hassan Daren_US
dc.format.extentxxxiii, 201 leaves : color illustrations, charts, tables ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectReconstructionen_US
dc.subjectSynthesisen_US
dc.subjectDeep neural networksen_US
dc.subjectTransfer learningen_US
dc.subjectGenerative adversarial networksen_US
dc.titleDeep learning for accelerated MR imagingen_US
dc.title.alternativeHızlandırılmış MR görüntüleme için derin öğrenmeen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US
dc.identifier.itemidB156775


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