Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/12406
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dc.contributor.authorBourgeat, Pierrick-
dc.contributor.authorVillemagne, Victor L-
dc.contributor.authorDore, Vincent-
dc.contributor.authorBrown, Belinda-
dc.contributor.authorMacaulay, S Lance-
dc.contributor.authorMartins, Ralph N-
dc.contributor.authorMasters, Colin L-
dc.contributor.authorAmes, David-
dc.contributor.authorEllis, Kathryn A-
dc.contributor.authorRowe, Christopher C-
dc.contributor.authorSalvado, Olivier-
dc.contributor.authorFripp, Jurgen-
dc.date.accessioned2015-05-16T02:06:05Z
dc.date.available2015-05-16T02:06:05Z
dc.date.issued2014-08-27-
dc.identifier.citationNeurobiology of Aging 2014; 36 Suppl 1(): S159-66en
dc.identifier.otherPUBMEDen
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/12406en
dc.description.abstract(11)C-Pittsburgh compound B (PiB) is a positron emission tomography (PET) tracer designed to bind to amyloid-β (Aβ) plaques, one of the hallmarks of Alzheimer's disease (AD). The potential of PiB as an early marker of AD led to the increasing use of PiB in clinical research studies and development of several F-18-labeled Aβ radiotracers. Automatic quantification of PiB images requires an accurate parcellation of the brain's gray matter (GM). Typically, this relies on a coregistered magnetic resonance imaging (MRI) to extract the cerebellar GM, compute the standardized uptake value ratio (SUVR), and provide parcellation and segmentation for quantification of regional and global SUVR. However, not all subjects can undergo MRI, in which case, an MR-less method is desirable. In this study, we assess 3 PET-only quantification methods: a mean atlas, an adaptive atlas, and a multi-atlas approaches on a database of 237 subjects having been imaged with both PiB PET and MRI. The PET-only methods were compared against MR-based SUVR quantification and evaluated in terms of correlation, average error, and performance in classifying subjects with low and high Aβ deposition. The mean atlas method suffered from a significant bias between the estimated neocortical SUVR and the PiB status, resulting in an overall error of 5.6% (R(2) = 0.98), compared with the adaptive and multi-atlas approaches that had errors of 3.06% and 2.74%, respectively (R(2) = 0.98), and no significant bias. In classifying PiB-negative from PiB-positive subjects, the mean atlas had 10 misclassified subjects compared with 0 for the adaptive and 1 for the multi-atlas approach. Overall, the adaptive and the multi-atlas approaches performed similarly well against the MR-based quantification and would be a suitable replacements for PiB quantification when no MRI is available.en
dc.language.isoenen
dc.subject.otherAlzheimer's diseaseen
dc.subject.otherPET quantificationen
dc.subject.otherPittsburgh compound Ben
dc.titleComparison of MR-less PiB SUVR quantification methods.en
dc.typeJournal Articleen
dc.identifier.journaltitleNeurobiology of agingen
dc.identifier.affiliationCentre of Excellence for Alzheimer's Disease Research & Care, Edith Cowan University, Joondalup, Perth, Western Australiaen
dc.identifier.affiliationNational Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australiaen
dc.identifier.affiliationPreventative Health Flagship, CSIRO Materials Science & Engineering Flagship, Parkville, Victoria, Australiaen
dc.identifier.affiliationThe Mental Health Research Institute, University of Melbourne, Parkville, Victoria, Australiaen
dc.identifier.affiliationDepartment of Psychiatry, University of Melbourne, Parkville, Victoria, Australiaen
dc.identifier.affiliationDepartment of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationPreventative Health Flagship, CSIRO Digital Productivity and Services Flagship, Herston, Queensland, Australiaen
dc.identifier.doi10.1016/j.neurobiolaging.2014.04.033en
dc.description.pagesS159-66en
dc.relation.urlhttps://pubmed.ncbi.nlm.nih.gov/25257985en
dc.contributor.corpauthorAIBL Research Groupen
dc.type.contentTexten
dc.type.austinJournal Articleen
local.name.researcherMasters, Colin L
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
crisitem.author.deptMolecular Imaging and Therapy-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
crisitem.author.deptMolecular Imaging and Therapy-
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