RT Journal Article
T1 Multivariate analysis of dual-point amyloid PET intended to assist the diagnosis of Alzheimer's disease
A1 Segovia, F.
A1 Ramirez, J.
A1 Castillo-Barnes, D.
A1 Salas-Gonzalez, D.
A1 Gomez-Rio, M.
A1 Sopena-Novales, P.
A1 Phillips, C.
A1 Zhang, Y.
A1 Gorriz, J. M.
K1 Computer aided diagnosis
K1 Multimodal systems
K1 Amyloid PET imaging
K1 Support vector machine
K1 Multiple kernel learning
K1 Late fusion
K1 Partial least squares
K1 Alzheimer's disease
K1 Partial least-squares
K1 Images
K1 Mri
K1 Classification
K1 Biomarker
K1 Ad
AB Several studies have recently suggested that amyloid Positron Emission Tomography (PET) data acquired immediately after the radiotracer injection provide information related to the brain metabolism, similar to that contained in F-18-Fluorodeoxyglucose (FDG) PET neuroimages. If corroborated, it would allow us to acquire information about brain injury and potential brain amyloid deposits in a single examination, using a dual-point protocol.In this work we assess the equivalence between early F-18-Florbetaben (FBB) PET and F-18-FDG PET data using multivariate approaches based on machine learning. In addition, we propose several systems based on data fusion that take advantage of the additional information provided by dual-point amyloid PET examinations. The proposed systems perform an initial dimensionality reduction of the data using a partial-least-square-based algorithm and then combine early and standard PET acquisitions using two approaches: multiple kernel learning (intermediate fusion) or an ensemble of two Support Vector Machine classifiers (late fusion). The proposed approaches were evaluated and compared with other fusion techniques using data from 43 subjects with cognitive impairments. They achieved a good trade-off between sensitivity and specificity and higher accuracy rates than systems based on single-modality approaches such as standard F-18-FBB PET data or F-18-FDG PET neuroimages. (C) 2020 Elsevier B.V. All rights reserved.
PB Elsevier
SN 0925-2312
YR 2020
FD 2020-12-05
LK http://hdl.handle.net/10668/18844
UL http://hdl.handle.net/10668/18844
LA en
DS RISalud
RD Apr 7, 2025