Ultra high-field MR images suffer from severe image inhomogeneity and artefacts due to the B1 field. Deep learning is a potential solution to this problem but training is difficult because no perfectly homogeneous 7T images exist that could serve as a ground truth. In this work, artificial training data has been created using numerically simulated 7T B1 fields, perfectly homogeneous 1.5T images and a signal model to add typical 7T B1 inhomogeneity on top of 1.5T images. A Pix2Pix model has been trained and tested on out-of-domain data where it out-performs classic bias field reducing algorithms.
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