Using CT Data to Improve the Quantitative Analysis of <sup>18</sup>F-FBB PET Neuroimages.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_FD6F8FFBA356
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Using CT Data to Improve the Quantitative Analysis of <sup>18</sup>F-FBB PET Neuroimages.
Journal
Frontiers in aging neuroscience
Author(s)
Segovia F., Sánchez-Vañó R., Górriz J.M., Ramírez J., Sopena-Novales P., Testart Dardel N., Rodríguez-Fernández A., Gómez-Río M.
ISSN
1663-4365 (Print)
ISSN-L
1663-4365
Publication state
Published
Issued date
2018
Peer-reviewed
Oui
Volume
10
Pages
158
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
<sup>18</sup> F-FBB PET is a neuroimaging modality that is been increasingly used to assess brain amyloid deposits in potential patients with Alzheimer's disease (AD). In this work, we analyze the usefulness of these data to distinguish between AD and non-AD patients. A dataset with <sup>18</sup> F-FBB PET brain images from 94 subjects diagnosed with AD and other disorders was evaluated by means of multiple analyses based on <i>t</i> -test, ANOVA, Fisher Discriminant Analysis and Support Vector Machine (SVM) classification. In addition, we propose to calculate amyloid standardized uptake values (SUVs) using only gray-matter voxels, which can be estimated using Computed Tomography (CT) images. This approach allows assessing potential brain amyloid deposits along with the gray matter loss and takes advantage of the structural information provided by most of the scanners used for PET examination, which allow simultaneous PET and CT data acquisition. The results obtained in this work suggest that SUVs calculated according to the proposed method allow AD and non-AD subjects to be more accurately differentiated than using SUVs calculated with standard approaches.
Keywords
Alzheimer's disease, florbetaben, multivariate analysis, positron emission tomography, quantitative analysis, support vector machine
Pubmed
Open Access
Yes
Create date
25/06/2018 16:59
Last modification date
20/08/2019 16:28
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