title: MR-less surface-based amyloid assessment based on 11C PiB PET.
abstract: BACKGROUND
  β-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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The demonstrated accuracy suggests that the proposed method could be an effective clinical inspection tool for Aβ imaging scans when MRI images are unavailable.
authors.affiliation: CSIRO 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, Australia.
authors.forename: Luping
authors.initials: L
authors.lastname: Zhou
authors.affiliation: CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australia.
authors.forename: Olivier
authors.initials: O
authors.lastname: Salvado
authors.affiliation: CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australia.
authors.forename: Vincent
authors.initials: V
authors.lastname: Dore
authors.affiliation: CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australia.
authors.forename: Pierrick
authors.initials: P
authors.lastname: Bourgeat
authors.affiliation: CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australia.
authors.forename: Parnesh
authors.initials: P
authors.lastname: Raniga
authors.affiliation: CSIRO Preventative-Health National Research Flagship, Parkville, Australia.
authors.forename: S Lance
authors.initials: SL
authors.lastname: Macaulay
authors.affiliation: National Ageing Research Institute, Parkville, Australia ; Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Kew, Parkville, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Ames
authors.affiliation: Mental Health Research Institute/Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.
authors.forename: Colin L
authors.initials: CL
authors.lastname: Masters
authors.affiliation: Mental 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, Australia.
authors.forename: Kathryn A
authors.initials: KA
authors.lastname: Ellis
authors.affiliation: Department of Nuclear Medicine and Centre for PET, Austin Hospital, Heidelberg, Australia.
authors.forename: Victor L
authors.initials: VL
authors.lastname: Villemagne
authors.affiliation: Department of Nuclear Medicine and Centre for PET, Austin Hospital, Heidelberg, Australia.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Rowe
authors.affiliation: CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, Australia.
authors.forename: Jurgen
authors.initials: J
authors.lastname: Fripp
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis; D016229:Amyloid beta-Peptides; D000814:Aniline Compounds; D052160:Benzothiazoles; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D058225:Plaque, Amyloid; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D013844:Thiazoles
chemical_list: C475519:2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole; D016229:Amyloid beta-Peptides; D000814:Aniline Compounds; D052160:Benzothiazoles; D013844:Thiazoles