Supplementary file 1_Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.docx

作者：Jiaqi Wu (3706924), Jiahong Ouyang, Farshad Moradi, Mohammad Mehdi Khalighi (12196332), Greg Zaharchuk · 发表于：Figshare · 年份：2026 · DOI：10.3389/frai.2026.1841677.s001 · 研究领域：Artificial intelligence、Computer science、Nuclear medicine、Medicine

Fluorodeoxyglucose (FDG) PET to evaluate patients with epilepsy is one of the most common applications for simultaneous PET/MRI, given the need to image both brain structure and metabolism but is suboptimal due to the radiation dose in this young population. Little work has been done synthesizing diagnostic quality PET images from MRI data or MRI data with ultralow-dose PET using advanced generative AI methods, such as diffusion models, with attention to clinical evaluations tailored for the epilepsy population. We compared the performance of diffusion- and non-diffusion-based deep learning models for the MRI-to-PET image translation task for epilepsy imaging using simultaneous PET/MRI in 52 subjects (40 train/2 validate/10 hold-out test). We tested three different models: 2 score-based generative diffusion models (SGM-Karras Diffusion [SGM-KD] and SGM-variance preserving [SGM-VP]) and a Transformer-U-net. We report results on standard image processing metrics as well as clinically relevant metrics, including congruency measures (Congruence Index and Congruency Mean Absolute Error) that assess hemispheric metabolic asymmetry, which is a key part of the clinical analysis of these images. We compared the model performance using different inputs such as T1-weighted (T1w), T2 FLAIR (T2F), and 1% ultralow-dose PET images to evaluate the effect and necessity of each imaging contrast. The SGM-KD produced the best qualitative and quantitative results when synthesizing PET purely from...