학술논문
Using Early Acquisitions of Amyloid-PET as a Surrogate of FDG-PET: A Machine Learning Based Approach
Document Type
Conference
Author
Source
2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pattern Recognition in Neuroimaging (PRNI), 2018 International Workshop on. :1-4 Jun, 2018
Subject
Language
Abstract
Recent studies have suggested that early acquisitions of 18 F-FBB-PET data (eFBB) provides similar information to 18 F-FDG-PET images. As far as we know, presently this attractive idea has been only tested by experiments focused on the routine clinical practice. In this work, we compare the usefulness of FDG and eFBB images to separate Alzheimer’s disease (AD) and non-AD patients using Computer Aided Diagnosis (CAD) systems based on machine learning. Specifically, a Support Vector Machine classifier was used to estimate the potential of both data modalities to separate the groups. Two dimensionality reduction approaches, one based on previous knowledge (predefined regions of interest) and other based on Principal Component Analysis were also investigated. The results suggest that eFBB images could be used as a surrogate of FDG data in CAD systems for AD. However we found slight differences that might indicate that FDG images are more suitable than eFBB data to model the metabolic changes of non-AD patients. In addition, using multimodal systems we evaluated weather FDG and eFBB images contain complementary information and would be worth to use them together.