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Abstract #2521

Predicting PDFF and R2* from Magnitude-Only Two-Point Dixon MRI Using Generative Adversarial Networks

Nicolas Basty1, Marjola Thanaj1, Madeleine Cule2, Elena P. Sorokin2, E. Louise Thomas1, Jimmy D. Bell1, and Brandon Whitcher1
1Research Centre for Optimal Health, University of Westminster, London, United Kingdom, 2Calico Life Sciences LLC, South San Francisco, CA, United States


We implement generative adversarial network (GAN) models to predict fully quantitative parameters from a complex-valued multiecho MRI sequence using only data from the magnitude-only two-point Dixon acquisition in the UK Biobank abdominal protocol. The training data consists of in- and opposed-phase channels from the Dixon sequence as inputs and the proton density fat fraction (PDFF) and R2* parameter maps estimated from the IDEAL acquisition as outputs. We compare conditional and cycle GANs, where the conditional GAN models outperformed the cycleGAN models in SSIM, PSNR and MSE. PDFF predictions were better than R2* predictions for all models.

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