Publications
Category: PET
Erosion of The Supratentorial White Matter Reference for Increased Power in Longitudinal Amyloid PET
The use of the supratentorial white matter as a reference region, alone 1 7 or in composite 3 can facilitate the detection of subtle changes in amyloid plaque burden Investigators often erode or otherwise restrict the sWM labels to reduce the influence of signal “spill in” from grey matter 2 4 8 but the optimal extent…
A Deep Learning Framework For Clinical Trial Enrichment in Alzheimer’s Disease
The selection of participants at risk of cognitive decline in clinical trials, known as trial enrichment, increases the probability of trial success. It is estimated that by 2050, 153 million people worldwide will be living with a type of dementia. Hence, innovative trial recruitment strategies are necessary to accelerate treatment development.
Automated VMAT2 [18F] AV-133 PET analysis in Parkinson's disease
We discuss our latest work to evaluate a fully automated image analysis pipeline to process vesicular monoamine transporters type 2 (VMAT2) [18F]AV-133 tracer positron emission tomography (PET) images, by comparison with a methodology requiring manual intervention.
Fully automatic deep-learning based segmentation of hippocampal subfields from standard resolution T1W MRI in Alzheimer’s disease
Here we train an AI method to segment the hippocampus into subfields (CA1-3 CA4+DG, and Subiculum) from standard resolution T1W MRI alone to assess utility as biomarkers compared to whole hippocampal volume, in the absence of a high-resolution T2 MRI.
Deep-learning methods for enrichment of Alzheimer’s Disease clinical trials using MRI and PET
Drug development trials aimed to halt Alzheimer’s disease (AD) progression favour recruitment of participants at early stages, preferably before symptomatic onset. In this investigation, we developed a deep-learning framework to differentiate participants with accelerated cognitive decline from those that remain cognitively stable within 24 months.