Impact of Deep Learning-Based Noise Reduction Algorithm on Visual Analysis and Centiloid Quantification in Reduced-Dose and, or Time Acquisition Amyloid PET Imaging
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 40
- 试验地点
- 3
- 主要终点
- Evaluate the impact of a deep-learning noise reduction algorithm on visual analysis and centiloid quantification when simulating reduced injected doses of 18F-flutemetamol.
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 99 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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,
排除标准
- •Patient have objected to the use of their data.
结局指标
主要结局
Evaluate the impact of a deep-learning noise reduction algorithm on visual analysis and centiloid quantification when simulating reduced injected doses of 18F-flutemetamol.
时间窗: Day one
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.
次要结局
未报告次要终点
研究者
Antoine VERGER
Principal Investigator
Central Hospital, Nancy, France