Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/30686
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dc.contributor.authorBourgeat, Pierrick-
dc.contributor.authorDoré, Vincent-
dc.contributor.authorBurnham, Samantha C-
dc.contributor.authorBenzinger, Tammie-
dc.contributor.authorTosun, Duygu-
dc.contributor.authorLi, Shenpeng-
dc.contributor.authorGoyal, Manu-
dc.contributor.authorLaMontagne, Pamela-
dc.contributor.authorJin, Liang-
dc.contributor.authorRowe, Christopher C-
dc.contributor.authorWeiner, Michael W-
dc.contributor.authorMorris, John C-
dc.contributor.authorMasters, Colin L-
dc.contributor.authorFripp, Jurgen-
dc.contributor.authorVillemagne, Victor L-
dc.date2022-
dc.date.accessioned2022-08-09T07:01:20Z-
dc.date.available2022-08-09T07:01:20Z-
dc.date.issued2022-07-30-
dc.identifier.citationNeuroImage 2022; 262: 119527en
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/30686-
dc.description.abstractThe Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. All Aβ PET data in AIBL (N=3315), ADNI (N=3442) and OASIS3 (N=1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.en
dc.language.isoeng-
dc.subjectAmyloid PETen
dc.subjectCentiloiden
dc.subjectHarmonisationen
dc.titleβ-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3.en
dc.typeJournal Articleen
dc.identifier.journaltitleNeuroImageen
dc.identifier.affiliationDepartment of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, USA..en
dc.identifier.affiliationDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA..en
dc.identifier.affiliationCSIRO Health and Biosecurity, Brisbane, Australiaen
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Healthen
dc.identifier.affiliationMolecular Imaging and Therapyen
dc.identifier.affiliationKnight Alzheimer Disease Research Center, St. Louis, MO, USA..en
dc.identifier.affiliationSan Francisco Veterans Affairs Medical Center, San Francisco, CA, USA..en
dc.identifier.affiliationMallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA..en
dc.identifier.affiliationWashington University in St. Louis, St. Louis, MO, USA..en
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/35917917/en
dc.identifier.doi10.1016/j.neuroimage.2022.119527en
dc.type.contentTexten
dc.identifier.orcid0000-0003-1695-3300en
dc.identifier.orcid0000-0002-8051-0558en
dc.identifier.orcid0000-0003-3910-2453en
dc.identifier.orcid0000-0003-3072-7940en
dc.identifier.orcid0000-0002-5832-9875en
dc.identifier.pubmedid35917917-
local.name.researcherDoré, Vincent
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
crisitem.author.deptMolecular Imaging and Therapy-
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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