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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 propose a CNN based end-to-end optimized T1 mapping by using a joint optimization of sequence parameters and neural network parameters for optimal signal acquistion, image reconstruction and T1 mapping. 
Figure 1: MR signal is simulated for given sequence and spin system, reconstruction and T1 mapping is performed with a NN. The output is compared to the target and gradient descent is performed to update TI/Trec and NN. Architecture: NN takes as input the complex k-space of all measurements and outputs a T1 map. The first two convolutional layers act as decomposed transform (DT) layers for image reconstruction, as described in 2. Magnitude of the output of the reconstruction part is calculated and feed into a three-hidden-layer multilayer perceptron for T1 quantification.
Figure 3: Optimized TI and Trec times (a,b) starting from a standard inversion recovery and resulting T1 maps (e,f) are compared to optimization from minimal TI and Trec times (c,d,g,h). The CNN provides T1 values in good agreement with literature values at 3T for both approaches (I,j & k,l).