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.
Study Type
OBSERVATIONAL
Enrollment
40
CHRU NANCY Brabois, nuclear medicine department
Vandœuvre-lès-Nancy, France
RECRUITINGNancy's hospital
Vandœuvre-lès-Nancy, France
RECRUITINGNuclear medicine department CHRU de NANCY Brabois
Vandœuvre-lès-Nancy, France
RECRUITINGEvaluate 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 one
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