Deep Learning on Amyloid Positons Emission Tomography

Sponsor
Central Hospital, Nancy, France
Study ID
NCT07309107
Status
Recruiting
Apply to this trial

Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - 99 Years
Healthy Volunteers
Not accepted

Study Details

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.

Key Dates

First listed
Dec 30, 2025
Start date
May 6, 2026
Status verified
Jun 2026
Primary completion
Nov 20, 2026
Completion
Jan 30, 2027

Study Design

Enrollment
40 participants (estimated)

Primary Outcome Measure

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 ]

Central Contacts

Find similar trials

Nationwide
Alzheimer's disease trialsNeurology trialsDementia care trialsBrain disorders trialsGeriatrics trials

Related Studies