MetaAD: Metabolism-Aware Anomaly Detection for Parkinson’s Disease in \(\text {3D}\) \(^\text {18}\) F-FDG PET

The dopamine transporter (DAT) imaging such as \(^{11}\) C-CFT PET has shown significant superiority in diagnosing Parkinson’s Disease (PD). However, most hospitals have no access to DAT imaging but instead turn to the commonly used \(^{18}\) F-FDG PET, which may not show major abnormalities of PD at visual analysis and thus hinder the performance of computer-aided diagnosis (CAD). To tackle this challenge, we propose a Metabolism-aware Anomaly Detection (MetaAD) framework to highlight abnormal metabolism cues of PD in \(^{18}\) F-FDG PET scans. MetaAD converts the input FDG image into a synthetic CFT image with healthy patterns, and then reconstructs the FDG image by a reversed modality mapping. The visual differences between the input and reconstructed images serve as indicators of PD metabolic anomalies. A dual-path training scheme is adopted to prompt the generators to learn an explicit normal data distribution via cyclic modality translation while enhancing their abilities to memorize healthy metabolic characteristics. The experiments reveal that MetaAD not only achieves superior performance in visual interpretability and anomaly detection for PD diagnosis, but also shows effectiveness in assisting supervised CAD methods. Our code is available at https://github.com/MedAIerHHL/MetaAD .