Fat/Water classification methods relying on image intensity histograms or hydrogen chemical-shift spectra can be subject to failure when assumptions in the algorithm are not met. In this study, we propose a new classification method based entirely on machine learning. Different neural network types were trained and tested on databases covering various anatomies, RF-coil types and image contrasts. A 2D paired classification using a fully connected neural network was capable of reliably classifying fat versus water with an accuracy of 100% on test data sets different from the training data, with a clinically relevant processing time of 0.05 s per case.
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