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Abstract #1969

Retrospective correction of B1 field inhomogeneities in T2w 7T prostate patient data

Seb Harrevelt1, Daan Reesink2, Astrid Lier, van3, Richard Meijer3, Josien Pluim4, and Alexander Raaijmakers1
1TU Eindhoven, Utrecht, Netherlands, 2Meander Medisch Centrum, Utrecht, Netherlands, 3UMC Utrecht, Utrecht, Netherlands, 4TU Eindhoven, Eindhoven, Netherlands


Prostate imaging at ultra-high fields is heavily affected by B1 field induced inhomogeneities. This not only results in unattractive images but it also might affect clinical diagnosis . To remedy this we developed a deep learning model that retrospectively corrects for the bias field. We applied this model to a clinical data set and demonstrated its performance in a qualitative manner. The results indicate that the model is able to drastically reduce the inhomogeneities in a variety of cases while the tissue contrast is generally maintained and the underlying anatomy has been successfully recovered.

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