Deep Learning on Amyloid Positons Emission Tomography

Reducing injected dose and/or acquisition time in amyloid PET imaging would improve comfort, radiation safety and cost-effectiveness in diagnosis and follow-up of patients. This study evaluates the impact of a deep learning-based noise reduction algorithm on visual analysis and Centiloid quantification when simulating reduced injected doses of [18F]flutemetamol.

Recruiting

Interested in participating?

Request Info

Key information

Conditions

Alzheimer Disease Brain Diseases Central Nervous System Diseases Dementia Mental Disorders Nervous System Diseases Neurocognitive Disorders Neurodegenerative Diseases Tauopathies

Age range

18 year–99 year

Sex eligibility

All sexes

Study type

Observational

Primary location

CHRU NANCY Brabois, nuclear medicine department, Vandœuvre-lès-Nancy, France

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients with objective cognitive impairment,
Referred to our department for a cerebral [¹⁸F]flutemetamol positron emission tomography scan between January 1, 2023 and July 1, 2025,

Exclusion criteria

Patient have objected to the use of their data.

Treatment and study plan

Primary outcomes

Evaluate the impact of a deep-learning noise reduction algorithm on visual analysis and centiloid quantification when simulating reduced injected doses of 18F-flutemetamol.

Time frame: Day one

Visual analysis of the cerebral [¹⁸F]flutemetamol PET images will be performed by two nuclear medicine specialists in a blinded manner, with a third reader acting as an arbitrator in case of disagreement, according to routine diagnostic criteria.

Study contacts

Contact information is provided by the study sponsor or research team.

Antoine VERGER, MD,PhD

CONTACT

[email protected]

0383155567 ext. +33

Veronique Roch, MSc

CONTACT

[email protected]

0383154276

Sponsors and collaborators

Lead sponsor

Central Hospital, Nancy, France

Other

Registry information

Official study title

Impact of Deep Learning-Based Noise Reduction Algorithm on Visual Analysis and Centiloid Quantification in Reduced-Dose and, or Time Acquisition Amyloid PET Imaging

Acronym: DEEPAMY

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2027
First posted: Dec 30, 2025
Registry last updated: Jun 25, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Recruiting