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

Positron 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.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2024

Author dc:creator

• Wu, Qi

Advisors dc:contributor.advisor

• O'Sullivan, Finbarr
• Huang, Jian

• Dynamic PET imaging
• Kinetic modeling
• Total-body PET quantitation
• Long axial field of view studies
• Image-domin bootstrap
• Machine learning
• Dynamic imaging protocol

Statement dc:rights

• © 2024, Qi Wu.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/16923

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/16923