bibtext: @InProceedings{ Hua_MetaAD_MICCAI2024,
                   author = { Huang, Haolin and Shen, Zhenrong and Wang, Jing and Wang, Xinyu and Lu, Jiaying and Lin, Huamei and Ge, Jingjie and Zuo, Chuantao and Wang, Qian },
                   title = { { MetaAD: Metabolism-Aware Anomaly Detection for Parkinson’s Disease in 3D 18F-FDG PET } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: The dopamine transporter (DAT) imaging such as 11C-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 18F-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 18F-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.
title: MetaAD: Metabolism-Aware Anomaly Detection for Parkinson’s Disease in 3D 18F-FDG PET
authors: Huang, Haolin Shen, Zhenrong Wang, Jing Wang, Xinyu Lu, Jiaying Lin, Huamei Ge, Jingjie Zuo, Chuantao Wang, Qian