Generation of Synthetic [18F]FDG PET From Early-Phase Amyloid PET in Alzheimer's Disease

Alzheimer Disease (AD)

Alzheimer Disease, Amyloid PET, FDG PET, Artificial Intelligence, Deep Learning, Retrospective Study, Neuroimaging, Positron Emission Tomography, Florbetaben, Flutemetamol, Image Processing, Diagnostic Imaging

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.

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.

No related papers found yet.

None listed

Italy