RT Journal Article
T1 Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages.
A1 Segovia, Fermín
A1 Sánchez-Vañó, Raquel
A1 Górriz, Juan M
A1 Ramírez, Javier
A1 Sopena-Novales, Pablo
A1 Testart Dardel, Nathalie
A1 Rodríguez-Fernández, Antonio
A1 Gómez-Río, Manuel
K1 Alzheimer's disease
K1 florbetaben
K1 multivariate analysis
K1 positron emission tomography
K1 quantitative analysis
K1 support vector machine
AB 18F-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 18F-FBB PET brain images from 94 subjects diagnosed with AD and other disorders was evaluated by means of multiple analyses based on t-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.
SN 1663-4365
YR 2018
FD 2018-06-07
LK http://hdl.handle.net/10668/12632
UL http://hdl.handle.net/10668/12632
LA en
DS RISalud
RD Apr 12, 2025