  [$^{18}$F]fluorodeoxyglucose (FDG) positron emission tomography (PET) has
emerged as a crucial tool in identifying the epileptic focus, especially in
cases where magnetic resonance imaging (MRI) diagnosis yields indeterminate
results. FDG PET can provide the metabolic information of glucose and help
identify abnormal areas that are not easily found through MRI. However, the
effectiveness of FDG PET-based assessment and diagnosis depends on the
selection of a healthy control group. The healthy control group typically
consists of healthy individuals similar to epilepsy patients in terms of age,
gender, and other aspects for providing normal FDG PET data, which will be used
as a reference for enhancing the accuracy and reliability of the epilepsy
diagnosis. However, significant challenges arise when a healthy PET control
group is unattainable. Yaakub \emph{et al.} have previously introduced a
Pix2PixGAN-based method for MRI to PET translation. This method used paired MRI
and FDG PET scans from healthy individuals for training, and produced pseudo
normal FDG PET images from patient MRIs that are subsequently used for lesion
detection. However, this approach requires a large amount of high-quality,
paired MRI and PET images from healthy control subjects, which may not always
be available. In this study, we investigated unsupervised learning methods for
unpaired MRI to PET translation for generating pseudo normal FDG PET for
epileptic focus localization. Two deep learning methods, CycleGAN and SynDiff,
were employed, and we found that diffusion-based method achieved improved
performance in accurately localizing the epileptic focus.
