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Abstract #1120

Synthesizing Full-dose FDG Brain PET from MRI With and Without Ultralow-dose PET using Deep Learning Diffusion Models in Patients with Epilepsy

Jiaqi Wu1, Jiahong Ouyang1, Mehdi Khalighi2, and Greg Zaharchuk2
1Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Epilepsy, generative AI, score-based generative model

Motivation: Synthesizing full-dose PET images from MRI data holds significant clinical potential by reducing patients' radiation exposure during imaging procedures. The use of generative-AI in epilepsy imaging remains underexplored.

Goal(s): We compared the performance of generative and non-generative deep learning models in MRI-PET image translation tasks through clinically focused evaluations.

Approach: We adapted existing 2D cross-modal image translation models including CNN-based Transformer-UNet and Score-based Generative Models trained on axial slices collected from PET-MR.

Results: We demonstrate the ability to synthesize full-dose FDG-PET images from MRI inputs alone using deep learning diffusion models and demonstrate further improved performance with ultralow-dose (1%) FDG-PET as an input.

Impact: This study suggests the possibility to use generative AI approaches to massively reduce dose levels for FDG PET brain studies. Further work will leverage 3D patch-based approaches can improve the performance and slice consistencies.

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Keywords