Development of advanced denoising and analysis algorithms for applications in hybrid PET/MRI brain imaging
department or school: Science, Faculty of
department or school: Physics and Astronomy, Department of
data provider: DSpace
degree campus: UBCV
creator: Bevington, Connor William James
issued: 2023-07-12T21:03:51Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Hybrid PET/MRI scanners are becoming more common in research and clinical settings, in particular for their ability to simultaneously acquire unique functional and structural information to probe the healthy and diseased brain. Data from both modalities need to be thoroughly processed to enhance signal-to-noise ratios (SNR), and the development and optimization of analysis algorithms is required to extract meaningful physiological quantities for medical research applications. This work provides significant contributions to these fields through the development, testing, and validation of four algorithms. PET data are intrinsically noisy due to the Poisson nature of positron emission events and the relatively low fraction of events detected by scanners, so we first improve upon an existing PET denoising algorithm to overcome limitations in quantitative accuracy in precision. This proves important in task-based PET imaging, where tasks presented to a subject during scanning result in a small temporal signal change that is difficult to distinguish from noise. The developed PET denoising algorithm is thus coupled with an advanced PET detection algorithm which provides a significant improvement in the detection sensitivity of task-induced signal changes. Motivated by the success of existing PET denoising algorithms, we then develop an MRI denoising algorithm for low SNR functional MRI data, which demonstrates significant improvements in quantitative accuracy and precision of downstream modeling metrics. Finally, we propose a pattern analysis extension of an existing joint PET/MRI analysis algorithm to identify disease-related alterations to simultaneously acquired multimodal data. We identify immediate applications of each algorithm in novel medical research studies, and proof-of-concept testing on human data is performed when available.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85231
note: Development of advanced denoisingand analysis algorithms forapplications in hybrid PET/MRI brainimagingbyConnor William James BevingtonB.Sc., The University of Waterloo, 2017M.Sc., The University of British Columbia, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Physics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)July 2023© Connor William James Bevington, 2023The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the dissertation entitled:Development of advanced denoising and analysis algorithms for applications in hybridPET/MRI brain imagingsubmitted by Connor William James Bevington in partial fulfilment of the requirements forthe degree of Doctor of Philosophyin PhysicsExamining Committee:Vesna Sossi, Professor, Physics, UBCSupervisorArman Rahmim, Professor, Physics, UBCSupervisory Committee MemberAlexander Rauscher, Associate Professor, Physics, UBCSupervisory Committee MemberJane Z. Wang, Professor, Department of Electrical and Computer Engineering, UBCUniversity ExaminerStefan Reinsberg, Associate Professor, Physics, UBCUniversity ExaminerRichard E. Carson, Professor, Biomedical Engineering & Radiology and Biomedical Imaging,Yale UniversityExternal ExaminerAdditional Supervisory Committee Members:Ludovic van Waerbeke, Professor, Physics, UBCSupervisory Committee MemberiiAbstractHybrid PET/MRI scanners are becoming more common in research and clinical settings, inparticular for their ability to simultaneously acquire unique functional and structural informa-tion to probe the healthy and diseased brain. Data from both modalities need to be thoroughlyprocessed to enhance signal-to-noise ratios (SNR), and the development and optimization ofanalysis algorithms is required to extract meaningful physiological quantities for medical re-search applications. This work provides signifi…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0434197
language: eng
degree discipline: Physics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Sossi, Vesna
type: Text
identifier URI: http://hdl.handle.net/2429/85231