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Abstract #4123

Reduction of B1-field induced inhomogeneity for body imaging at 7T using deep learning and synthetic training data.

Seb Harrevelt1, Lieke Wildenberg2, Dennis Klomp2, C.A.T. van den Berg2, Josien Pluim3, and Alexander Raaijmakers1
1TU Eindhoven, Utrecht, Netherlands, 2UMC Utrecht, Utrecht, Netherlands, 3TU Eindhoven, Rossum, Netherlands

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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