Statistical analysis of 11CO2 contamination correction in 11C-acetate PET imaging studies

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dc.contributor.advisorO'Sullivan, Finbarr
dc.contributor.advisorHuang, Jian
dc.contributor.authorLan, Taoen
dc.contributor.funderScience Foundation Ireland
dc.contributor.funderUniversity College Cork
dc.contributor.funderNational Cancer Institute
dc.date.accessioned2026-01-19T16:11:04Z
dc.date.available2026-01-19T16:11:04Z
dc.date.issued2025
dc.date.submitted2025
dc.description.abstractPositron Emission Tomography (PET) is a noninvasive imaging modality widely used in cancer management and medical research. It supports diagnosis, prognosis, treatment monitoring, and clinical decisions by tracking radiotracers labeled with positron-emitting isotopes. Tracer choice depends on the biological process of interest, such as tumor metabolism, cell proliferation, or blood flow. The most common tracer is fluorodeoxyglucose ([18F]FDG), which accumulates in tissues with high glucose metabolism (e.g., brain, heart, and many tumors), and is used for cancer detection, staging, and treatment monitoring, as well as neurological and cardiac assessment. Another key tracer is 11C-acetate, involved in cell membrane synthesis and oxidative metabolism. It complements FDG, especially in tissues with low or variable glucose uptake. However, its oxidative metabolism generates 11CO2, which is indistinguishable from the PET measured signal. The main goal of this thesis is to develop a statistical model to estimate and correct the contribution of metabolically produced 11CO2 in 11C-acetate PET imaging. The model is constructed using non-parametric residue mapping (NPRM) and the extended semi-parametric residue mapping (SPRM), based on a dynamic PET dataset acquired with 11CO2-11C-acetate dual injection. The correction performance is evaluated at the voxel and region-of-interest (ROI) levels. Furthermore, this thesis explores the feasibility of performing 11CO2 contamination correction using only 11C-acetate PET data, without requiring a separate 11CO2 scan. Promising results are presented to demonstrate the potential of this approach.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLan, T. 2025. Statistical analysis of 11CO2 contamination correction in 11C-acetate PET imaging studies. PhD Thesis, University College Cork.
dc.identifier.endpage123
dc.identifier.urihttps://hdl.handle.net/10468/18410
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectUniversity College Cork( School of Mathematical Sciences)
dc.rights© 2025, Tao Lan.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDynamic PETen
dc.subjectSemi-parametric residue mappingen
dc.subject11C-Acetate PET imagingen
dc.subject11CO2 correction in 11C-acetate PET imagingen
dc.subjectDual-injection scansen
dc.titleStatistical analysis of 11CO2 contamination correction in 11C-acetate PET imaging studies
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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