Synthetic FDG PET images generated from T1 MRI by a 3D patch-based Cycle-GAN with MSE loss can train an unsupervised anomaly detector that localizes epileptogenic lesions in MRI-negative epilepsy patients with 74% sensitivity, outperforming a detector trained on real PET (42%).
Deep-learning predicted pet can be subtracted from the true clinical fluorodeoxyglucose pet co-registered to mri to identify the epileptogenic focus in focal epilepsy
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GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models
Synthetic FDG PET images generated from T1 MRI by a 3D patch-based Cycle-GAN with MSE loss can train an unsupervised anomaly detector that localizes epileptogenic lesions in MRI-negative epilepsy patients with 74% sensitivity, outperforming a detector trained on real PET (42%).