Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by F-FDG Brain PET Imaging.

Alzheimer's Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dementia of AD type among MCI patients.

Multiscale spatial gradient features for F-FDG PET image-guided diagnosis of Alzheimer's disease.

Background And Objective: F-FluoroDeoxyGlucose Positron Emission Tomography (F-FDG PET) is one of the imaging biomarkers to diagnose Alzheimer's Disease (AD). In F-FDG PET images, the changes of voxels' intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI). In this paper, F-FDG PET images are characterized in an alternative way-the spatial gradient, which is motivated by the observation that the changes of F-FDG rates also cause gradient changes.

Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease.

Using a single imaging modality to diagnose Alzheimer's disease (AD) or mild cognitive impairment (MCI) is a challenging task. FluoroDeoxyGlucose Positron Emission Tomography (FDG-PET) is an important and effective modality used for that purpose. In this paper, we develop a novel method by using single modality (FDG-PET) but multilevel feature, which considers both region properties and connectivities between regions to classify AD or MCI from normal control.