MetaAD: Metabolism-Aware Anomaly Detection for Parkinson's Disease in 3D <SUP>18</SUP>F-FDG PET

doi:10.1007/978-3-031-72069-7_28

	MetaAD: Metabolism-Aware Anomaly Detection for Parkinson's Disease in 3D ¹⁸F-FDG PET
	Huang, Haolin1,2 ; Shen, Zhenrong 3; Wang, Jing 4; Wang, Xinyu1,2 ; Lu, Jiaying 4; Lin, Huamei 4; Ge, Jingjie 4; Zuo, Chuantao 4; Wang, Qian1,2,5
	2024
会议录名称	MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT II (IF:0.402[JCR-2005],0.000[5-Year])
ISSN	0302-9743
卷号	15002
发表状态	已发表
DOI	10.1007/978-3-031-72069-7_28
摘要	The dopamine transporter (DAT) imaging such as C-11-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 F-18-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 F-18-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.
关键词	Parkinson's disease Brain PET Unsupervised Anomaly Detection Cross-modality Synthesis
会议名称	27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点	Palmeraie Conf Ctr,Marrakesh,MOROCCO
会议日期	OCT 06-10, 2024
URL	查看原文
收录类别	CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China["82394432","82394434","82272039","82021002","81971641"] ; STI 2030-Major Projects[2022ZD0211600]
WOS研究方向	Computer Science ; Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001342225800028
出版者	SPRINGER INTERNATIONAL PUBLISHING AG
EISSN	1611-3349
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/458347
专题	生物医学工程学院生物医学工程学院_PI研究组_王乾组生物医学工程学院_硕士生生物医学工程学院_硕士生
通讯作者	Zuo, Chuantao; Wang, Qian
作者单位	1.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China 2.ShanghaiTech Univ, State Key Lab Adv Med Mat & Devices, Shanghai, Peoples R China 3.Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China 4.Fudan Univ, Huashan Hosp, PET Ctr, Dept Nucl Med, Shanghai, Peoples R China 5.Shanghai Clin Res & Trial Ctr, Shanghai, Peoples R China
第一作者单位	生物医学工程学院; 上海科技大学
通讯作者单位	生物医学工程学院; 上海科技大学
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Huang, Haolin,Shen, Zhenrong,Wang, Jing,et al. MetaAD: Metabolism-Aware Anomaly Detection for Parkinson's Disease in 3D ¹⁸F-FDG PET[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024.