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Abstract #4199

MRzero –- Automated invention of MRI sequences using supervised learning

Alexander Loktyushin1,2, Kai Herz1,3, Nam Dang4, Felix Glang1, Anagha Deshmane1, Simon Weinmüller4, Arnd Doerfler4, Bernhard Schölkopf2, Klaus Scheffler1,3, and Moritz Zaiss1,3
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2Max Planck Institute for Intelligent Systems, Tübingen, Germany, 3Eberhard Karls University Tübingen, Tübingen, Germany, 4University Clinic Erlangen, Erlangen, Germany

We propose a framework — MRzero — that allows automatic invention of MR sequences. At the core of the framework is a differentiable forward process allowing to simulate image measurement and reconstruction. The sequence parameters are variables of optimization. As a cost function we use mean squared error distance to a certain given target contrast of interest. To avoid overfitting we propose a method that generates synthetic data that is used for training. In the experiments, we demonstrate the ability of the method to learn RF flip angles and spatial encoding from scratch given a target obtained with GRE sequence.

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