Phase-correcting denoising for diffusion magnetic resonance imaging

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buir.advisorÇukur, Emine Ülkü Sarıtaş
dc.contributor.authorKafalı, Sevgi Gökçe
dc.date.accessioned2018-05-18T07:30:48Z
dc.date.available2018-05-18T07:30:48Z
dc.date.copyright2018-04
dc.date.issued2018-04
dc.date.submitted2018-05-16
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2018en_US
dc.descriptionIncludes bibliographical references (leaves 61-68).en_US
dc.description.abstractDiffusion magnetic resonance imaging (MRI) is a low signal-to-noise ratio (SNR) acquisition technique when compared to anatomical MRI. Multiple acquisitions have to be averaged to overcome this SNR problem. However, subject motion and/or local pulsations during diffusion sensitizing gradients create varying phase offsets and k-space shifts between repeated acquisitions, prohibiting direct complex averaging due to local signal cancellations in the resultant images. When multiple acquisitions are magnitude averaged, these phase issues are avoided at the expense of noise accumulation. This thesis proposes a reconstruction routine to overcome the local signal cancellations, while increasing the SNR. First, a global phase correction algorithm is employed, followed by a partial Fourier reconstruction algorithm. Then, a novel phase-correcting non-local means (PC-NLM) filtering is proposed to denoise the images without losing structural details. The proposed PC-NLM takes advantage of the shared structure of the multiple acquisitions as they should only differ in terms of phase issues and noise. The proposed PC-NLM technique is rst employed on diffusion-weighted imaging (DWI) of the spinal cord, and is then modi ed to capture the joint information from different diffusion sensitizing directions in diffusion-tensor imaging (DTI). The results are demonstrated with extensive simulations and in vivo DWI and DTI of the spinal cord. These results show that the proposed PC-NLM provides high image quality without any local signal cancellations, while preserving the integrity of quantitative measures such as apparent diffusion coefficients (ADC) and fractional anisotropy (FA) maps. This reconstruction routine can be especially beneficial for the imaging of small body parts that require high resolution.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Sevgi Gökçe Kafalı.en_US
dc.embargo.release2019-05-14
dc.format.extentxxii, 68 leaves : illustrations, charts ; 30 cmen_US
dc.identifier.itemidB158359
dc.identifier.urihttp://hdl.handle.net/11693/46940
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDiffusion-Weighted İmagingen_US
dc.subjectDiffusion-Tensor İmagingen_US
dc.subjectMotion, Denoisingen_US
dc.subjectNon-Local Meansen_US
dc.subjectPhase Correctionen_US
dc.subjectSpinal Corden_US
dc.titlePhase-correcting denoising for diffusion magnetic resonance imagingen_US
dc.title.alternativeDifüzyon manyetik rezonans görüntülemede faz düzeltmeli gürültü giderimien_US
dc.typeThesisen_US

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