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Abstract #0552

Cycle-Consistent Adversarial Transformers for Unpaired MR Image Translation

Onat Dalmaz1,2, Mahmut Yurt3, Salman UH Dar1,2, and Tolga Çukur1,2,4
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Electrical Engineering, Stanford University, Stanford, CA, United States, 4Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey


Translating acquired sequences to missing ones in multi-contrast MRI protocols can dramatically reduce scan costs. Neural network models devised for this purpose are characteristically trained on paired datasets, which can be difficult to compile. Moreover, these models exclusively rely on convolutional operators with undesirable biases towards feature locality and spatial invariance. Here, we present a cycle-consistent translation model, ResViT, to enable training on unpaired datasets. ResViT combines localization power of convolution operators with contextual sensitivity of transformers. Demonstrations on multi-contrast MRI datasets indicate the superiority of ResViT against state-of-the-art translation models.

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