Subspace informed compressed sensing reconstruction techniques for diffusion tensor imaging
department or school: Science, Faculty of
department or school: Physics and Astronomy, Department of
data provider: DSpace
degree campus: UBCV
creator: Wiley, Neale
issued: 2024-08-21T15:18:45Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Magnetic resonance imaging (MRI) is a technique that allows non-invasive investigation of the properties of tissue through manipulation of the properties of the hyperfine splitting of the hydrogen atom in a magnetic field. Diffusion MRI is a variant of MRI that sensitises the imaging process to diffusion of water molecules in tissue through the use of strong gradient magnetic fields to dephase the signal of any protons undergoing diffusion, leading to a reduction in signal magnitude in that voxel. There is increasing use of iterative reconstruction algorithms of MRI data using the principles of compressed sensing (CS) that rely on the sparsity of information in the image domain to improve reconstructions of undersampled MRI data. Further refinements to the CS algorithm not only rely on the sparsity of single MRI images, but take advantage of low rank properties of some MRI data along a further dimension to permit even higher undersampling. Diffusion tensor imaging (DTI) acquires many sampled volumes of the same field of view encoded along different diffusion vectors. A reconstruction using low rank properties in the diffusion dimension could improve DTI image reconstructions. However, the random phase of diffusion imaging due to subject motion during the diffusion sensitising gradients of the acquisition destroys the low-rank nature of diffusion datasets. Phase correction of a randomly undersampled k-space is impossible, but the application of a phase correction factor to the sensitivity maps used in multicoil acquisitions can correct the phase error to restore low rank properties to the data. Alternatively, one can use a similar technique to perform a secondary reconstruction of diffusion tensor image data, as the magnitude images of a diffusion scan already discard all phase information, which includes the motion corruption. This secondary reconstruction efficiently denoises and simultaneously interpolates the images to a higher resolution by leveraging a projecti…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88985
note: Subspace informed compressed sensing reconstructiontechniques for diffusion tensor imagingbyNeale WileyA THESIS SUBMITTED IN PARTIAL FULFILMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Physics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2024© Neale Wiley, 2024The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Subspace informed compressed sensing reconstruction techniques for diffusion tensor imagingsubmitted by Neale Wiley in partial fulfilment of the requirements forthe degree of Master of Sciencein PhysicsExamining Committee:Shannon Kolind, Assistant Professor, Medicine (Neurology), UBCSupervisorAlex Mackay, Professor Emeritus, Physics and Astronomy, UBCSupervisory Committee MemberiiAbstractMagnetic resonance imaging (MRI) is a technique that allows non-invasive investigationof the properties of tissue through manipulation of the properties of the hyperfine splitting of thehydrogen atom in a magnetic field. Diffusion MRI is a variant of MRI that sensitises the imagingprocess to diffusion of water molecules in tissue through the use of strong gradient magneticfields to dephase the signal of any protons undergoing diffusion, leading to a reduction in signalmagnitude in that voxel.There is increasing use of iterative reconstruction algorithms of MRI data using theprinciples of compressed sensing (CS) that rely on the sparsity of information in the imagedomain to improve reconstructions of undersampled MRI data. Further refinements to the CSalgorithm not only rely on the sparsity of single MRI images, but take advantage of low rankproperties of some MRI data along a further dimension to permit even higher undersampling.Diffusion tensor imaging (DTI) acquires many sampled volumes of the same field of viewencoded along different diffusion vectors. A reconstruction using low rank properties in thed…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445110
language: eng
degree discipline: Physics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc/4.0/
scholar level: Graduate
contributor: Kolind, Shannon
type: Text
identifier URI: http://hdl.handle.net/2429/88985