Multivariate analysis of dual-point amyloid PET intended to assist the diagnosis of Alzheimer’s disease - Segovia, F.; Ramírez, J.; Castillo-Barnes, D.; Salas-Gonzalez, D.; Gómez-Río, M.; Sopena-Novales, P.; Phillips, C.; Zhang, Y.; Górriz, J.M. (Neurocomputing (Amsterdam), 2020)
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 18F-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 18F-Florbetaben (FBB) PET and 18F-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 18F-FBB PET data or 18F-FDG PET neuroimages.