Statistical methods for mapping kinetics together with associated uncertainties in long field of view dynamic PET studies

Simple item page
dc.contributor.advisorO'Sullivan, Finbarr
dc.contributor.advisorHuang, Jian
dc.contributor.authorWu, Qien
dc.contributor.funderUniversity College Corken
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderNational Cancer Instituteen
dc.date.accessioned2025-01-29T15:47:59Z
dc.date.available2025-01-29T15:47:59Z
dc.date.issued2024en
dc.date.submitted2024
dc.description.abstractPositron Emission Tomography (PET) is an essential diagnostic imaging technique in clinical care settings, as well as in medical research. It plays a crucial role in diagnosis, prognosis, treatment planning, and clinical decision-making. PET imaging, a well-established radio-tracer imaging technique, involves injecting a radio-tracer to analyze in-vivo metabolic processes. Dynamic PET scanning provides multiple time frames, offering more detailed metabolic information. However, traditional methods like Patlak and compartmental modeling are commonly used in data obtained from conventional scanners. The use of constant or exponential residue functions may be limited in complex environments, such as diverse tissues or multiple organs. This thesis aims to develop statistical approaches for enhancing and assessing parametric imaging from dynamic PET scans. The Non-Parametric Residue Mapping (NPRM) procedure is established as an entirely automatic process that integrates data-driven segmentation, non-parametric residue analysis, and voxel-level kinetic mapping. A model-based image-domain bootstrapping method is developed with the objective to generate reliable uncertainty estimates, which are crucial for accurate data interpretation and subsequent treatment decisions. This method uses an empirical distribution of re-scaled data and a non-parametric approach for analysis of the spatial correlation structure. Numerical simulations using both direct Filtered Backprojection (FBP) and iterative Maximum Likelihood (ML) reconstructions are considered. Illustrative examples on conventional scanners and Long Axial Field-of-View (LAFOV) PET scanners are conducted. A short-duration dynamic scanning protocol is proposed to enhance the quantitation of a shortened dataset specifically. This protocol utilizes NPRM and machine learning techniques to aim at making short dynamic acquisition protocols clinically feasible.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWu, Q. 2024. Statistical methods for mapping kinetics together with associated uncertainties in long field of view dynamic PET studies. PhD Thesis, University College Cork.
dc.identifier.endpage132
dc.identifier.urihttps://hdl.handle.net/10468/16923
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2024, Qi Wu.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDynamic PET imaging
dc.subjectKinetic modeling
dc.subjectTotal-body PET quantitation
dc.subjectLong axial field of view studies
dc.subjectImage-domin bootstrap
dc.subjectMachine learning
dc.subjectDynamic imaging protocol
dc.titleStatistical methods for mapping kinetics together with associated uncertainties in long field of view dynamic PET studies
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
WuQ_PhD2024.pdf
Size:
23.59 MB
Format:
Adobe Portable Document Format
Description:
Full Text E-thesis
Download
Thumbnail Image
Name:
WuQ_PhD2024_Submission for examination form.pdf
Size:
774.55 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
5.2 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Theses
College of Science, Engineering and Food Science - Doctoral Theses
Mathematical Sciences - Doctoral Theses