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Proceedings Paper

Computational analysis of PET by AIBL (CapAIBL): a cloud-based processing pipeline for the quantification of PET images
Author(s): Pierrick Bourgeat; Vincent Dore; Jurgen Fripp; Victor L. Villemagne; Chris C. Rowe; Olivier Salvado
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Paper Abstract

With the advances of PET tracers for β-Amyloid (Aβ) detection in neurodegenerative diseases, automated quantification methods are desirable. For clinical use, there is a great need for PET-only quantification method, as MR images are not always available. In this paper, we validate a previously developed PET-only quantification method against MR-based quantification using 6 tracers: 18F-Florbetaben (N=148), 18F-Florbetapir (N=171), 18F-NAV4694 (N=47), 18F-Flutemetamol (N=180), 11C-PiB (N=381) and 18F-FDG (N=34). The results show an overall mean absolute percentage error of less than 5% for each tracer. The method has been implemented as a remote service called CapAIBL ( PET images are uploaded to a cloud platform where they are spatially normalised to a standard template and quantified. A report containing global as well as local quantification, along with surface projection of the β-Amyloid deposition is automatically generated at the end of the pipeline and emailed to the user.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132V (20 March 2015); doi: 10.1117/12.2082492
Show Author Affiliations
Pierrick Bourgeat, Australian e-Health Research Ctr. (Australia)
Vincent Dore, Australian e-Health Research Ctr. (Australia)
Jurgen Fripp, Australian e-Health Research Ctr. (Australia)
Victor L. Villemagne, Austin Health (Australia)
Chris C. Rowe, Austin Health (Australia)
Olivier Salvado, Australian e-Health Research Ctr. (Australia)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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