Generation of Synthetic [18F]FDG PET From Early-Phase Amyloid PET in Alzheimer's Disease
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 35
- 试验地点
- 1
- 主要终点
- Quantitative Accuracy of Synthetic FDG PET Images (SUVR Correlation and MAE)
研究概览
简要总结
This study aims to test a new artificial intelligence (AI) method to create brain scan images without needing an extra scan. Currently, patients with memory problems often undergo two types of PET scans (Amyloid PET and FDG PET) to assess Alzheimer's disease. This study will use existing scan data from patients who already had both scans as part of their routine care.
The AI model will try to generate the FDG PET image using only the Amyloid PET scan and an MRI. If successful, this method could reduce radiation exposure, costs, and time for future patients by eliminating the need for a separate FDG injection and scan.
No new scans, injections, or procedures will be performed for this study. All data will be fully anonymized (personal information removed) before analysis. The study involves approximately 35 adult patients (age 50+) whose data were collected between January 2025 and December 2025 at IRCCS Ospedale San Raffaele in Milan, Italy.
详细描述
This is a retrospective observational study conducted at IRCCS Ospedale San Raffaele, Milan, Italy. The study evaluates the accuracy of synthetic [18F]FDG PET images generated using a SwinUNETR deep learning model compared to native [18F]FDG PET images.
Study Population:
Adults (≥ 50 years) who underwent amyloid PET imaging (using Florbetaben or Flutemetamol), structural MRI, and [18F]FDG PET due to cognitive symptoms between January 2025 and December 2025. Approximately 35 patients meeting inclusion criteria will be included.
Methodology:
All imaging and clinical data were collected as part of routine diagnostic care; thus, no additional procedures, interventions, or interactions with patients are required for this study. All data are fully deidentified before analysis, consistent with GDPR and institutional data protection policy. The SwinUNETR model processes volumetric images to generate synthetic FDG PET images from early-phase amyloid PET and MRI inputs.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 50 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 50 years at the time of imaging.
- •Clinically indicated amyloid PET scan performed with Florbetaben or Flutemetamol between January 1, 2025 and December 31,
- •Availability of paired structural MRI (3D T1-weighted) and real [18F]FDG PET scan acquired within ±6 months of the amyloid PET.
- •All three imaging modalities (Amyloid PET, FDG PET, MRI) are of sufficient technical quality for co-registration and quantitative analysis.
排除标准
- •Presence of other major neurological disorders that may confound FDG metabolism (e.g., Parkinson's disease, frontotemporal dementia, brain tumor, or recent stroke).
- •Severe motion artifacts or technical failures in any of the three imaging modalities that prevent reliable co-registration or SUVR calculation.
- •Incomplete or irreversibly corrupted DICOM data preventing anonymization or conversion to analysis-ready format.
研究组 & 干预措施
Patients with cognitive impairment undergoing amyloid PET
Adults (≥50 years) with cognitive symptoms who underwent amyloid PET (Florbetaben or Flutemetamol), structural MRI, and [18F]FDG PET at IRCCS Ospedale San Raffaele between January 2025 and December 2025 as part of routine diagnostic care. All data are fully anonymized prior to analysis.
结局指标
主要结局
Quantitative Accuracy of Synthetic FDG PET Images (SUVR Correlation and MAE)
时间窗: Retrospective analysis of imaging data acquired between January 1, 2025 and December 31, 2025
Pearson correlation coefficient and mean absolute error (MAE) of SUVR values obtained from native FDG PET and synthetic FDG PET in Alzheimer relevant areas of interest (precuneus, posterior cingulate, lateral temporal cortex, and frontal cortex).
次要结局
- Regional SUVR Bias Between Synthetic and Native FDG Across Machine Types(Retrospective analysis of imaging data acquired between January 1, 2025 and December 31, 2025)
研究者
Chiti Arturo
Arturo Chiti
IRCCS San Raffaele