Deep Learning on Amyloid Positons Emission Tomography
ConditionAlzheimer Disease
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-99
SponsorCentral Hospital, Nancy, France
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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,
Disqualifiers
Patient have objected to the use of their data.
Trial population
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, will be included
Trial design
Design model
Other
Time perspective
Retrospective
Treatments tested in this trial
Not listed
Trial groups
No trial groups listed
Trial outcomes
Primary outcomes
1
Evaluate the impact of a deep-learning noise reduction algorithm on visual analysis and centiloid quantification when simulating reduced injected doses of 18F-flutemetamol.
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.
Time frame
Day oneSponsors and contacts
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Source ClinicalTrials.gov