Rapid reconstruction for parallel magnetic resonance imaging with non-Cartesian variable-density sampling trajectories

Simple item page
buir.advisorÇukur, Tolga
dc.contributor.authorŞenel, Celal Furkan
dc.date.accessioned2020-02-19T06:21:34Z
dc.date.available2020-02-19T06:21:34Z
dc.date.copyright2020-01
dc.date.issued2020-01
dc.date.submitted2020-02-18
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, 2020.en_US
dc.descriptionIncludes bibliographical references (leaves 33-37).en_US
dc.description.abstractDue to long acquisition times, the use of magnetic resonance imaging (MRI) remains challenging in some applications. Variable-density acquisitions enable scan acceleration while maintaining a desirable trade-off between signal-to-noise ratio (SNR) and spatial resolution. Several image-domain and k-space algorithms were previously proposed for parallel-imaging reconstructions of variabledensity acquisitions. However, these methods involve iterative procedures for non-Cartesian data, resulting in substantial computational burden in particular for three-dimensional (3D) reconstructions. An efficient method based on partially parallel imaging with localized sensitivities (PILS) was recently proposed for fast reconstructions of 2D non-Cartesian data. This thesis introduces a generalized image-domain implementation for 3D non-Cartesian variable-density data, and compares it against conventional gridding, PILS, and ESPIRiT (iterative self-consistent parallel imaging reconstruction using eigenvector maps) reconstructions on brain and knee data accelerated at R=2.5 to 4.2. The results indicate that the proposed 3D variable-FOV method outperforms SOS (sum of squares) and PILS methods, and performs equally or better than ESPIRiT reconstruction at less than half of the processing time required by ESPIRiT. Thus, the proposed method provides fast, high-SNR, artifact-suppressed reconstructions.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2020-02-19T06:21:34Z No. of bitstreams: 1 thesis.pdf: 8273285 bytes, checksum: 8ff9c1995a2ee502b3a0753ba12fac44 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-02-19T06:21:34Z (GMT). No. of bitstreams: 1 thesis.pdf: 8273285 bytes, checksum: 8ff9c1995a2ee502b3a0753ba12fac44 (MD5) Previous issue date: 2020-02en
dc.description.statementofresponsibilityby Celal Furkan Şenelen_US
dc.embargo.release2020-08-18
dc.format.extentxii, 37 leaves : illustrations (some color) ; 30 cm.en_US
dc.identifier.itemidB159916
dc.identifier.urihttp://hdl.handle.net/11693/53421
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectParallel imagingen_US
dc.subjectVariable densityen_US
dc.titleRapid reconstruction for parallel magnetic resonance imaging with non-Cartesian variable-density sampling trajectoriesen_US
dc.title.alternativeDeğişken yoğunluklu kartezyen olmayan paralel manyetik rezonans görüntülemede hızlı geriçatımen_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis.pdf
Size:
7.89 MB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Graduate School of Engineering and Science