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Abstract #0333

MRzero with dAUTOMAP reconstruction– automated invention of MR acquisition and neural network reconstruction

Hoai Nam Dang1, Simon Weinmüller1, Alexander Loktyushin2,3, Felix Glang2, Arnd Dörfler1, Andreas Maier4, Bernhard Schölkopf3, Klaus Scheffler2,5, and Moritz Zaiss1,2
1Neuroradiology, University Clinic Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 2Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, 3Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen, Germany, 4Pattern Recognition Lab, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 5Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany

We present an end-to-end optimized T1 mapping utilizing MRzero - a fully differentiable Bloch-equation-based MRI sequence invention framework. A convolutional neural network is employed for combined image reconstruction and parameter mapping. The pipeline performs a joint optimization of sequence parameters and neural network parameters to create a full autoencoder for T1 mapping. We demonstrate for in vivo measurements at 3T, that the CNN based reconstruction and T1 mapping outperformes a conventional reconstruction with pixelwise neural network based T1 quantification.

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