Deep Learning on Amyloid Positons Emission Tomography

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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07309107

Acronym

DEEPAMY

Enrollment

Registered

2025-12-30

Start date

2026-05-06

Completion date

2027-01-30

Last updated

2026-06-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Alzheimer Disease

Brief summary

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.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 99 Years

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.

Design outcomes

Primary

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

Countries

France

Contacts

CONTACTAntoine VERGER, MD,PhD

a.verger@chru-nancy.fr0383155567

CONTACTVeronique Roch, MSc

v.roch@chru-nancy.fr0383154276

PRINCIPAL_INVESTIGATORPrincipal Investigator

CHRU of NANCY

Outcome results