An illustration of the use of model-based bootstrapping for evaluation of uncertainty in kinetic information derived from dynamic PET

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dc.contributor.authorGu, Fengyun
dc.contributor.authorWu, Qi
dc.contributor.authorO'Sullivan, Finbarr
dc.contributor.authorHuang, Jian
dc.contributor.authorMuzi, Mark
dc.contributor.authorMankoff, David A.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderNational Institutes of Healthen
dc.date.accessioned2020-09-24T11:37:22Z
dc.date.available2020-09-24T11:37:22Z
dc.date.issued2019-11
dc.date.updated2020-09-24T11:03:48Z
dc.description.abstractKinetic mapping via mixture analysis[8], [10] involves comprehensive voxel-level analysis of dynamic PET data. Bootstrapping from the fitted mixture model gives the ability to directly simulate statistical copies of the 4-D PET data, and following suitable analysis, subsequent simulations of the associated kinetic maps. This gives the ability to numerically evaluate uncertainties in inferences associated with kinetic information. We provide a simple introduction to the concept of the model-based bootstrap and an illustration of the use of the approach for kinetic mapping from dynamic PET using results from recent work in Huang et al.[4]. The illustration is from a PET flow-metabolism imaging study in a breast cancer patient. It involves separate dynamic PET imaging following injections of O-15 H2O and F-18 FDG. The bootstrapped data is created in the image domain rather than the projection domain, so there is no reconstruction requirement involved.en
dc.description.sponsorshipScience Foundation Ireland (Grant No. PI-11/1027); National Institutes of Health (National Cancer Institute USA (grant R33-CA225310 and NIH/NCI R50-CA211270))en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGu, F., Wu, Q., O’Sullivan, F., Huang, J., Muzi, M. and Mankoff, D. A. (2019) 'An Illustration of the Use of Model-Based Bootstrapping for Evaluation of Uncertainty in Kinetic Information Derived from Dynamic PET', 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Manchester, United Kingdom, 26 Oct.-2 Nov. doi: 10.1109/NSS/MIC42101.2019.9059736en
dc.identifier.doi10.1109/NSS/MIC42101.2019.9059736en
dc.identifier.endpage3en
dc.identifier.isbn978-1-7281-4164-0
dc.identifier.isbn978-1-7281-4165-7
dc.identifier.issn2577-0829
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/10581
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urihttps://ieeexplore.ieee.org/document/9059736
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectCanceren
dc.subjectMedical image processingen
dc.subjectMixture modelsen
dc.subjectPositron emission tomographyen
dc.subjectStatistical analysisen
dc.subject4-D PET dataen
dc.subjectKinetic mappingen
dc.subjectPET flow-metabolism imaging studyen
dc.subjectDynamic PET imagingen
dc.subjectBootstrapped dataen
dc.subjectModel-based bootstrappingen
dc.subjectKinetic informationen
dc.subjectMixture analysisen
dc.subjectComprehensive voxel-level analysisen
dc.subjectFitted mixture modelen
dc.subjectStatistical copiesen
dc.subjectBreast cancer patienten
dc.subjectImage domainen
dc.subjectProjection domainen
dc.subjectData modelsen
dc.subjectKinetic theoryen
dc.subjectImagingen
dc.subjectAnalytical modelsen
dc.subjectNumerical modelsen
dc.subjectStandardsen
dc.subjectUncertaintyen
dc.subjectSpatial autocorrelationen
dc.subjectModel-based bootstrapen
dc.subjectStandard errorsen
dc.subjectSimulationen
dc.titleAn illustration of the use of model-based bootstrapping for evaluation of uncertainty in kinetic information derived from dynamic PETen
dc.typeConference itemen
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