Abstract #0333

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

Hoai Nam Dang¹, Simon Weinmüller¹, Alexander Loktyushin^2,3, Felix Glang², Arnd Dörfler¹, Andreas Maier⁴, Bernhard Schölkopf³, Klaus Scheffler^2,5, and Moritz Zaiss^1,2

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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