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Abstract #1197

Synthetic FDG PET from functional MRI for epilepsy: development and external validation

Chenyang Yao1 and Jie Lu1
1Xuanwu Hospital Capital Medical University, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation:

  • FDG-PET, essential for identifying hypometabolism in epilepsy, is costly and involves radioactive tracers. To address this, we developed and validated a conditional GAN pipeline to produce FDG-PET from functional MRI using multi-device, multi-modal datasets.

Goal(s):

  • Develop a GAN-based MRI-to-PET translation framework and validate its clinical potential.

Approach:

  • Develop a deep learning model based on T1-weighted and blood oxygen level-dependent (BOLD) imaging to generate FDG-PET.
  • Evaluate and validate the model using features including visual inspection, computational vision, standardized uptake value ratio, asymmetry index, and radiomics.

Results:

  • The synthetic PET scans demonstrate potential in enhancing epilepsy detection and predictive clinical outcomes.

Impact:

  • Deep learning can generate high-fidelity synthetic PET based on functional MRI
  • Radiologists confirm high imaging fidelity between synthetic and actual PET
  • The imaging features of synthetic PET are similar to actual PET
  • Synthetic PET improves epilepsy diagnosis and prognosis

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Abstract

Keywords