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Abstract #0166

Adversarial Diffusion Probabilistic Models for Unpaired MRI Contrast Translation

Muzaffer Ozbey1,2, Onat Dalmaz1,2, Salman UH Dar1,2, Hasan Atakan Bedel1,2, Şaban Öztürk1,2, Alper Güngör1,2, and Tolga Çukur1,2,3
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceSynthesis of missing contrasts in an MRI protocol via translation from acquired contrasts can reduce costs associated with prolonged exams. Current learning-based translation methods are predominantly based on generative adversarial networks (GAN) that implicitly characterize the distribution of the target contrast, with limits fidelity of synthesized images. Here we present SynDiff, a novel conditional adversarial diffusion model for computationally efficient, high-fidelity contrast translation. SynDiff enables training on unpaired datasets, thanks to its cycle-consistent architecture with coupled diffusion processes. Demonstrations on multi-contrast MRI datasets indicate the superiority of SynDiff against competing GAN and diffusion models.

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