dc.contributor.advisor | Çukur, Emine Ülkü Sarıtaş | |
dc.contributor.author | Kafalı, Sevgi Gökçe | |
dc.date.accessioned | 2018-05-18T07:30:48Z | |
dc.date.available | 2018-05-18T07:30:48Z | |
dc.date.copyright | 2018-04 | |
dc.date.issued | 2018-04 | |
dc.date.submitted | 2018-05-16 | |
dc.identifier.uri | http://hdl.handle.net/11693/46940 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2018 | en_US |
dc.description | Includes bibliographical references (leaves 61-68). | en_US |
dc.description.abstract | Diffusion 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.statementofresponsibility | by Sevgi Gökçe Kafalı. | en_US |
dc.format.extent | xxii, 68 leaves : illustrations, charts ; 30 cm | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Diffusion-Weighted İmaging | en_US |
dc.subject | Diffusion-Tensor İmaging | en_US |
dc.subject | Motion, Denoising | en_US |
dc.subject | Non-Local Means | en_US |
dc.subject | Phase Correction | en_US |
dc.subject | Spinal Cord | en_US |
dc.title | Phase-correcting denoising for diffusion magnetic resonance imaging | en_US |
dc.title.alternative | Difüzyon manyetik rezonans görüntülemede faz düzeltmeli gürültü giderimi | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B158359 | |
dc.embargo.release | 2019-05-14 | |