Trials / Not Yet Recruiting
Not Yet RecruitingNCT07309107
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
- Not Yet Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 40 (estimated)
- Sponsor
- Central Hospital, Nancy, France · Academic / Other
- Sex
- All
- Age
- 18 Years – 99 Years
- Healthy volunteers
- —
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.
Conditions
Timeline
- Start date
- 2026-01-06
- Primary completion
- 2026-01-20
- Completion
- 2026-01-30
- First posted
- 2025-12-30
- Last updated
- 2025-12-30
Source: ClinicalTrials.gov record NCT07309107. Inclusion in this directory is not an endorsement.