# Diagnosis of Alzheimer's Disease and Prediction of Clinical Progression Using an Automated PET Image Analysis Tool

> **NIH NIH R44** · CORTECHS LABS, INC. · 2020 · $500,000

## Abstract

Project Summary/Abstract
The overall goal of this project is to deliver an automated PET image analysis tool to be used in
a clinical setting that quickly evaluates the PET images and reports PET standard uptake
utilization rates from the corresponding MRI segmented anatomical locations to determine
patients’ risk of developing Alzheimer’s disease (AD). While multiple research studies have
highlighted the utility of using PET imaging in diagnosis of AD and stratification of patients for
the possible conversion from mild cognitive impairment (MCI) to AD, the research methods used
are far from being available as a versatile tool that can be utilized in a clinical setting. In
addition to the common ligand 18F-FDG, multiple Alzheimer’s relevant ligands have been
recently introduced (11C-PiB, 18F-Florbetapir, 18F-Florbetaben, 18F-flutemetamol, as well as
THK5317, THK5351, AV-1451, and PBB3). An automated PET image analysis tool will be
timely and will make the best use of the newly introduced PET ligands. The advance of
Alzheimer’s relevant ligands will likely improve diagnosis of AD and monitoring of disease
progression. In phase I of our project, we will develop methods that assist in diagnosis of AD
and calculate the odds of MCI to AD conversion using automated processing of PET data (FDG-
PET) and MRI atrophy measures. In phase 2 of our proposal, we will extend our methods to
include the new PET ligands. The amyloid β and tau specific ligands present with different brain
distributions, and will enhance our understanding of the dynamic details of the Alzheimer’s
footprint in the brain. The degree of cortical binding of amyloid agents in patients with AD is
variable, and the quantitative evaluation will require calculating normative data for the new
ligands. As an additional confounder, the amyloid binding can exist in non-AD patients such as
patients with Lewy body dementia or cerebral amyloid angiopathy. Similar to amyloid β
deposition in non-AD entities, various tauopathies in addition to Alzheimer’s disease will show
tau ligand depositions in the patients’ brain. We will determine normative values for each
segmented brain region for multiple PET ligands currently available and calculate MRI atrophy
measures. Using all regional PET measurements and MRI atrophy measures we will develop
an automatic classification algorithm that will separate AD patients from non-AD controls, and
also compute MCI to AD conversion risk for each MCI subject.

## Key facts

- **NIH application ID:** 9969300
- **Project number:** 5R44AG060798-03
- **Recipient organization:** CORTECHS LABS, INC.
- **Principal Investigator:** AZIZ M ULUG
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $500,000
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9969300

## Citation

> US National Institutes of Health, RePORTER application 9969300, Diagnosis of Alzheimer's Disease and Prediction of Clinical Progression Using an Automated PET Image Analysis Tool (5R44AG060798-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9969300. Licensed CC0.

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