Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages
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AuthorSegovia Román, Fermín; Sánchez-Vañó, Raquel; Gorriz Sáez, Juan Manuel; Ramírez Pérez De Inestrosa, Javier; Sopena Novales, Pablo; Nathalie Testart Dardel; Rodríguez Fernández, Antonio; Gómez Río, Manuel
Quantitative analysisMultivariate analysisFlorbetabenAlzheimer’s diseaseSupport vector machinePositron emission tomography
Segovia F, Sánchez-Vañó R, Górriz JM, Ramírez J, Sopena-Novales P, Testart Dardel N, Rodríguez-Fernández A and Gómez-Río M (2018) Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages. Front. Aging Neurosci. 10:158. [doi: 10.3389/fnagi.2018.00158]
SponsorshipThis work was supported by the MINECO/FEDER under the TEC2012-34306 and TEC2015-64718-R projects and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC- 7103. The work was also supported by the Vicerectorate of Research and Knowledge Transfer of the University of Granada.
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.