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Abstract #3484

Image-to-image translation of 3T to 7T MRI using Generative Adversarial Networks: A step towards longitudinal harmonization

Eduardo Diniz1, Karim Helmet2, Tales Santini2, Howard Eizenstein2, and Tamer Ibrahim2
1Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States, 2Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States

We implemented a generative adversarial network (CycleGAN) to tackle the problem of MRI data harmonization across scanner strength. We leveraged a large dataset of unpaired 3T and 7T MR images for training and evaluated our model in a dataset of paired 3T and 7T data by generating synthetic 7T images and comparing them with their real counterparts. Dice scores and volumetric measures showed strong agreement between the synthetic and real 7T images. This approach allows for research studies to transition from 3T to 7T MR systems, thereby harnessing 7T systems advantages without losing the prior wave’s 3T MR data.

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