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Positron Emission Tomography01:29

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  3. Multisequence Mri Enables High-fidelity Fdg-pet Synthesis For Epilepsy Using Gans.
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  3. Multisequence Mri Enables High-fidelity Fdg-pet Synthesis For Epilepsy Using Gans.

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Multisequence MRI Enables High-Fidelity FDG-PET Synthesis for Epilepsy Using GANs.

Chenyang Yao1,2,3, Bixiao Cui1,2,3, Jingjuan Wang1,2,3

  • 1Department of Radiology and Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing, China.

Journal of Magnetic Resonance Imaging : JMRI
|January 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Synthetic PET generated from MRI shows high agreement with traditional FDG-PET, offering a viable alternative for epilepsy evaluation when FDG-PET is inaccessible or impractical due to radiation exposure.

Keywords:
FDG‐PETepilepsygenerative adversarial networkmultisequence MRIradiomicssynthetic PET

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Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Epilepsy Diagnostics

Background:

  • ¹⁸F-fluorodeoxyglucose-positron emission tomography (FDG-PET) is crucial for presurgical epilepsy evaluation.
  • However, its use is limited by accessibility issues and radiation exposure concerns.

Purpose of the Study:

  • To assess the efficacy of synthetic FDG-PET generated from T1-weighted imaging and resting-state functional MRI (fMRI) metrics.
  • To evaluate synthetic PET as a potential alternative to conventional FDG-PET.

Main Methods:

  • A retrospective study involving 481 participants (epilepsy patients and healthy controls) who underwent simultaneous FDG PET/MR.
  • Synthetic PET images were generated from T1-weighted imaging and resting-state fMRI data.
  • Performance was evaluated using image quality metrics (SSIM, PSNR), quantitative SUVR correlation, Bland-Altman analysis, and visual grading by blinded readers.

Main Results:

  • Synthetic PET demonstrated high agreement with ground-truth PET, with high SSIM (0.98 internal, 0.97 external) and strong SUVR correlation (r=0.94 internal, r=0.89 external).
  • Visual quality ratings were excellent/good in 82.8% of cases.
  • Detection accuracy for temporal lobe hypometabolism was high (90.3% internal, 87.1% external).
  • Radiomics analysis using synthetic PET showed promising AUCs for classifying hippocampal sclerosis and predicting surgical outcomes.

Conclusions:

  • Multisequence MRI-based synthetic PET serves as a reliable PET-like metabolic surrogate.
  • This technique offers a valuable alternative when conventional FDG-PET is unavailable or impractical.
  • Synthetic PET holds potential for improving epilepsy presurgical evaluation.