Generation of Synthetic [18F]FDG PET From Early-Phase Amyloid PET in Alzheimer's Disease
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Alzheimer Disease (AD). Basic parameters: from 50 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Italy
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Detailed description
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.
Objectives and Endpoints:
Primary Objective: To quantitatively and qualitatively assess the accuracy of synthetic FDG PET images compared with native FDG PET images.
Primary Endpoint: 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).
Secondary Objective: To assess visual interpretability and clinical intuitiveness of synthetic FDG PET images by expert nuclear medicine physicians.
Secondary Endpoint: Inter-rater agreement (Cohen's kappa) among 2 blinded nuclear medicine physicians rating synthetic FDG scans as "clinically acceptable" or not.
Ethical Considerations:
Due to the retrospective and non-interventional nature of this study, no additional informed consent is required. A waiver of informed consent will be requested from the Ethics Committee. The image data will be fully anonymized in accordance with institutional policies.
Primary outcome measures
- Quantitative Accuracy of Synthetic FDG PET Images (SUVR Correlation and MAE) [Time frame: Retrospective analysis of imaging data acquired between January 1, 2025 and December 31, 2025]
Secondary outcome measures (1)
- Regional SUVR Bias Between Synthetic and Native FDG Across Machine Types [Time frame: Retrospective analysis of imaging data acquired between January 1, 2025 and December 31, 2025]
Eligibility criteria
Inclusion criteria
- 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, 2025.
- 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.
Exclusion criteria
- 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Italy · 1 center
- IRCCS Ospedale San Raffaele — Milan
Identifiers
NCT: NCT07431255 · ALZ-AI