Abstract #3222

Unsupervised MR2PET Synthesis Provides Pseudo-Normal Reference to Improve Epileptic Lesion Detection

Jiwei Li¹, Wentao Chen², Hui Huang¹, Siyu Yuan¹, Xichen Xu³, Bingyang Cai¹, Ya Cui¹, Miao Zhang⁴, Weimin Zhou^2,3, and Jie Luo¹

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ²University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China, ³Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China, ⁴Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Synopsis

Keywords: Epilepsy, PET/MR, Imaging Translation

Motivation: It is challenging to obtain demographically matched controls for every patient who underwent FDG PET examinations.

Goal(s): We aim to generate pseudo-normal PET for each epilepsy patient leveraging recent progress in cross modality image translation.

Approach: We employed diffusion model to learn the translation between T1w-MRI and FDG PET of healthy subjects, then generated pseudo-normal PET for a cohort of 104 patients with focal epilepsy, who underwent PET/MR scanning for presurgical evaluation.

Results: Unsupervised SynDiff achieved comparable performance as supervised Pix2pixGAN in PET synthesis. Improved DICE coefficient and lesion detection were achieved using synthesized reference compared with traditional group reference.

Impact: Imaging translation provides a personalized pseudo-normal reference for each epilepsy patient. Pseudo-normal PET is poised for potential adoption as an auxiliary tool to enhance the capability of PET imaging in detecting epileptic lesions within clinical settings.

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