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

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.