An exploration of the prognostic utility of shortened dynamic imaging protocols for PET-FDG scans

Show simple item record

dc.contributor.author Wu, Qi
dc.contributor.author O'Sullivan, Finbarr
dc.contributor.author Muzi, Mark
dc.contributor.author Mankoff, David
dc.date.accessioned 2020-09-25T12:43:05Z
dc.date.available 2020-09-25T12:43:05Z
dc.date.issued 2019-11
dc.identifier.citation Wu, Q., O’Sullivan, F., Muzi, M. and Mankoff, D. A. (2019) 'An exploration of the prognostic utility of shortened dynamic imaging protocols for PET-FDG scans'. 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 26 Oct.-2 Nov. 2019, 1-3. doi: 10.1109/NSS/MIC42101.2019.9059874 en
dc.identifier.startpage 1 en
dc.identifier.endpage 3 en
dc.identifier.isbn 978-1-7281-4164-0
dc.identifier.isbn 978-1-7281-4165-7
dc.identifier.uri http://hdl.handle.net/10468/10589
dc.identifier.doi 10.1109/NSS/MIC42101.2019.9059874 en
dc.description.abstract Standard whole-body clinical fluoro-deoxyglucose (FDG)-PET scans typically involve imaging for around 15 minutes about 60 minutes after tracer injection. The scan duration is often the critical constraint limiting patient through-put. Scans taken long after tracer injection restrict the ability to assess vascular and perfusion information that might be revealed by the early pattern of tracer uptake. On the other hand, early scanning may compromise the recovery of the late time uptake (SUV) which in many contexts has well established prognostic value. In this study, we explore the potential for short-duration dynamic scans, acquired immediately after tracer injection, to recover information that can predict late-stage uptake of FDG. The work involves re-analysis of existing series of dynamic brain and breast tumour imaging data to simulate the type of information that would arise from early and late scanning. Using a collection of machine learning techniques (including random forests, neural networks, gradient boosting), we find that short-duration clinical protocols, soon after the tracer injection, show significant potential to recover the late stage FDG flux information. en
dc.description.sponsorship Science Foundation Ireland (Grant No. PI-11/1027); National Institutes of Health (National CancerInstitute USA grants R33-CA225310 and R50-CA221270. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.uri https://ieeexplore.ieee.org/document/9059874
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.subject Blood vessels en
dc.subject Brain en
dc.subject Cancer en
dc.subject Haemorheology en
dc.subject Learning (artificial intelligence) en
dc.subject Medical image processing en
dc.subject Positron emission tomography en
dc.subject Tumours en
dc.subject Prognostic utility en
dc.subject PET-FDG scans en
dc.subject Whole-body clinical fluoro-deoxyglucose-PET scans en
dc.subject Tracer injection en
dc.subject Prognostic value en
dc.subject Short-duration dynamic scans en
dc.subject Dynamic brain en
dc.subject Breast tumour en
dc.subject Short-duration clinical protocols en
dc.subject FDG flux information en
dc.subject Dynamic imaging protocols en
dc.subject Critical constraint limiting patient en
dc.subject Neural network en
dc.subject Gradient boosting en
dc.subject Random forests en
dc.subject Tumors en
dc.subject Imaging en
dc.subject Blood en
dc.subject Kinetic theory en
dc.subject Breast cancer en
dc.subject Predictive models en
dc.title An exploration of the prognostic utility of shortened dynamic imaging protocols for PET-FDG scans en
dc.type Conference item en
dc.internal.authorcontactother Qi Wu, School Of Mathematical Sciences, University College Cork, Cork, Ireland. +353-21-490-3000 en
dc.internal.availability Full text available en
dc.date.updated 2020-09-25T12:34:58Z
dc.description.version Accepted Version en
dc.internal.rssid 537885679
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder National Institutes of Health en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Manchester, United Kingdom en
dc.internal.IRISemailaddress f.osullivan@ucc.ie en
dc.identifier.eissn 2577-0829


Files in this item

This item appears in the following Collection(s)

Show simple item record

This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement