Statistical considerations in the kinetic analysis of PET-FDG brain tumour studies

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dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNoen
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dc.contributor.advisorO'Sullivan, Finbarren
dc.contributor.authorHawe, David
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2016-06-24T11:47:10Z
dc.date.available2016-06-24T11:47:10Z
dc.date.issued2016
dc.date.submitted2016
dc.description.abstractDynamic positron emission tomography (PET) imaging can be used to track the distribution of injected radio-labelled molecules over time in vivo. This is a powerful technique, which provides researchers and clinicians the opportunity to study the status of healthy and pathological tissue by examining how it processes substances of interest. Widely used tracers include 18F-uorodeoxyglucose, an analog of glucose, which is used as the radiotracer in over ninety percent of PET scans. This radiotracer provides a way of quantifying the distribution of glucose utilisation in vivo. The interpretation of PET time-course data is complicated because the measured signal is a combination of vascular delivery and tissue retention effects. If the arterial time-course is known, the tissue time-course can typically be expressed in terms of a linear convolution between the arterial time-course and the tissue residue function. As the residue represents the amount of tracer remaining in the tissue, this can be thought of as a survival function; these functions been examined in great detail by the statistics community. Kinetic analysis of PET data is concerned with estimation of the residue and associated functionals such as ow, ux and volume of distribution. This thesis presents a Markov chain formulation of blood tissue exchange and explores how this relates to established compartmental forms. A nonparametric approach to the estimation of the residue is examined and the improvement in this model relative to compartmental model is evaluated using simulations and cross-validation techniques. The reference distribution of the test statistics, generated in comparing the models, is also studied. We explore these models further with simulated studies and an FDG-PET dataset from subjects with gliomas, which has previously been analysed with compartmental modelling. We also consider the performance of a recently proposed mixture modelling technique in this study.en
dc.description.sponsorshipScience Foundation Ireland (SFI Grant 11/PI/1027; MI 2007)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHawe, D. 2016. Statistical considerations in the kinetic analysis of PET-FDG brain tumour studies. PhD Thesis, University College Cork.en
dc.identifier.endpage168en
dc.identifier.urihttps://hdl.handle.net/10468/2779
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2016, David Hawe.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectStatisticsen
dc.subjectMathematicsen
dc.subjectPETen
dc.subjectGliomaen
dc.subjectFDGen
dc.subjectDifferential equationsen
dc.subjectCompartmental modelsen
dc.subjectMedical imagingen
dc.subjectNonparametric modellingen
dc.subjectKinetic analysisen
dc.thesis.opt-outfalse
dc.titleStatistical considerations in the kinetic analysis of PET-FDG brain tumour studiesen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisorf.osullivan@ucc.ie
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