Purpose In vivo measurement of the spatial distribution of neurofibrillary tangle pathology is
critical for early diagnosis and disease monitoring of Alzheimer’s disease (AD). Methods …

Purpose While sampled or short-frame realizations have shown the potential power of deep
learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose …

Objective To determine whether memory tasks with demonstrated sensitivity to hippocampal
function can detect variance related to preclinical Alzheimer disease (AD) biomarkers, we …

Introduction Alzheimer's disease (AD) is the most common form of dementia, characterized
primarily by abnormal aggregation of two proteins, tau and amyloid beta. We assessed tau …

Objective Tau pathology is recognized as a primary contributor to neurodegeneration and
clinical symptoms in Alzheimer's disease (AD). This study aims to localize the early tau …

BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created
with deep learning using actual ultra-low-dose PET images and simultaneous structural MR …

Background Progressive loss of synapses occurs throughout the Alzheimer’s Disease (AD)
continuum and is closely associated with cognitive impairment. Here, we present pilot data …

We have previously trained a deep learning network with simultaneous PET/MRI inputs to
generate diagnostic quality images from ultra-low-dose amyloid PET acquisitions. With data …

521 Objectives: Combining deep learning-based methods such as convolutional neural
networks (CNNs) with the advantage of complementary information from simultaneous positron …

Background Pathological tau burden has been linked to neurodegeneration in AD patients.
Although previous studies have linked brain atrophy in people at the preclinical stage of AD …