Adversarial Diffusion Probabilistic Models for Unpaired MRI Contrast Translation
Muzaffer Özbey, Onat Dalmaz, Salman Ul Hassan Dar, Hasan A. Bedel, Şaban Öztürk, Alper Güngör, Tolga Çukur · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Synthesis 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.