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Abstract #3980

Fat/Water Classification Using a Supervised Neural Network

Anne Menini1, Kang Wang2, Zachary W. Slavens3, and Christopher J. Hardy4

1GE Global Research, Munich, Germany, 2Global MR Applications & Workflow, GE Healthcare, Madison, WI, United States, 3GE Healthcare, Waukesha, WI, United States, 4GE Global Research, Niskayuna, NY, United States

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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