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dc.contributor.authorZhou, Lupingen
dc.contributor.authorSalvado, Olivieren
dc.contributor.authorDore, Vincenten
dc.contributor.authorBourgeat, Pierricken
dc.contributor.authorRaniga, Parneshen
dc.contributor.authorMacaulay, S Lanceen
dc.contributor.authorAmes, Daviden
dc.contributor.authorMasters, Colin Len
dc.contributor.authorEllis, Kathryn Aen
dc.contributor.authorVillemagne, Victor Len
dc.contributor.authorRowe, Christopher Cen
dc.contributor.authorFripp, Jurgenen
dc.date.accessioned2015-05-16T01:41:13Z
dc.date.available2015-05-16T01:41:13Z
dc.date.issued2014-01-10en
dc.identifier.citationPlos One 2014; 9(1): e84777en
dc.identifier.govdoc24427295en
dc.identifier.otherPUBMEDen
dc.identifier.urihttp://ahro.austin.org.au/austinjspui/handle/1/12042en
dc.description.abstractβ-amyloid (Aβ) plaques in brain's grey matter (GM) are one of the pathological hallmarks of Alzheimer's disease (AD), and can be imaged in vivo using Positron Emission Tomography (PET) with (11)C or (18)F radiotracers. Estimating Aβ burden in cortical GM has been shown to improve diagnosis and monitoring of AD. However, lacking structural information in PET images requires such assessments to be performed with anatomical MRI scans, which may not be available at different clinical settings or being contraindicated for particular reasons. This study aimed to develop an MR-less Aβ imaging quantification method that requires only PET images for reliable Aβ burden estimations.The proposed method has been developed using a multi-atlas based approach on (11)C-PiB scans from 143 subjects (75 PiB+ and 68 PiB- subjects) in AIBL study. A subset of 20 subjects (PET and MRI) were used as atlases: 1) MRI images were co-registered with tissue segmentation; 2) 3D surface at the GM-WM interfacing was extracted and registered to a canonical space; 3) Mean PiB retention within GM was estimated and mapped to the surface. For other participants, each atlas PET image (and surface) was registered to the subject's PET image for PiB estimation within GM. The results are combined by subject-specific atlas selection and Bayesian fusion to generate estimated surface values.All PiB+ subjects (N = 75) were highly correlated between the MR-dependent and the PET-only methods with Intraclass Correlation (ICC) of 0.94, and an average relative difference error of 13% (or 0.23 SUVR) per surface vertex. All PiB- subjects (N = 68) revealed visually akin patterns with a relative difference error of 16% (or 0.19 SUVR) per surface vertex.The demonstrated accuracy suggests that the proposed method could be an effective clinical inspection tool for Aβ imaging scans when MRI images are unavailable.en
dc.language.isoenen
dc.subject.otherAgeden
dc.subject.otherAged, 80 and overen
dc.subject.otherAlzheimer Disease.diagnosisen
dc.subject.otherAmyloid beta-Peptidesen
dc.subject.otherBenzothiazoles.diagnostic useen
dc.subject.otherBrain.pathologyen
dc.subject.otherFemaleen
dc.subject.otherHumansen
dc.subject.otherMagnetic Resonance Imagingen
dc.subject.otherMaleen
dc.subject.otherMiddle Ageden
dc.subject.otherPlaque, Amyloiden
dc.subject.otherPositron-Emission Tomographyen
dc.subject.otherReproducibility of Resultsen
dc.titleMR-less surface-based amyloid assessment based on 11C PiB PET.en
dc.typeJournal Articleen
dc.identifier.journaltitlePlos Oneen
dc.identifier.affiliationCSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australiaen
dc.identifier.affiliationCSIRO Preventative-Health National Research Flagship, Parkville, Australiaen
dc.identifier.affiliationDepartment of Nuclear Medicine and Centre for PET, Austin Hospital, Heidelberg, Australiaen
dc.identifier.affiliationMental Health Research Institute/Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australiaen
dc.identifier.affiliationNational Ageing Research Institute, Parkville, Australia ; Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Kew, Parkville, Australiaen
dc.identifier.affiliationCSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australia ; Department of Computer Science and Software Engineering, University of Wollongong, Wollongong, Australiaen
dc.identifier.affiliationMental Health Research Institute/Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia ; Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Kew, Parkville, Australiaen
dc.identifier.doi10.1371/journal.pone.0084777en
dc.description.pagese84777en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/24427295en
dc.contributor.corpauthorAIBL Research Groupen
Appears in Collections:Journal articles

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