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Deep Learning on Amyloid Positons Emission Tomography

NCT07309107 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 40

Last updated 2025-12-30

No results posted yet for this study

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

  • Alzheimer Disease

Sponsors & Collaborators

  • Central Hospital, Nancy, France

    lead OTHER

Principal Investigators

  • Antoine VERGER, MD, PhD · CHRU of NANCY

Eligibility

Min Age
18 Years
Max Age
99 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-06
Primary Completion
2026-01-20
Completion
2026-01-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07309107 on ClinicalTrials.gov