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Abstract #3492

Demographic conditioned DDPM: pseudo-normal PET generation for improved epileptic lesion detection

Zengji Zhang1, Jiwei Li1, Ya Cui1, Bingyang Cai1, Haiqing Zhang1, Xiaolai Ye2, Miao Zhang3, and Jie Luo1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Neurology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Synopsis

Keywords: AI Diffusion Models, PET/MR

Motivation: High quality and demographically-matched control groups are critical for lesion detection in neurological diseases such as epilepsy. However, for FDG PET, good controls are not always available.

Goal(s): To generate individualized control images for FDG PET from structural MRI images,thus enhancing lesion detection for patients with focal epilepsy.

Approach: We propose to leverage data from both healthy control and patients using improved DDPM-based model that incorporates demographic and clinical information to achieve precise MRI to PET mapping for healthy controls. Then we combine personalized pseudo healthy reference and patients’ FDG PET to obtain lesion probability maps.

Impact: Paired MRI and FDG PET brain images of normal subjects are scarce. We integrate controls’ and patients’ demographic information into MRI2PET image translation using improved DDPM-based model, which will provide personalized pseudo-normal PET reference to aid lesion detection.

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