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

Dang, HN; Weinmüller, S; Loktyushin, A; Glang, F; Dörfler, A; Maier, A; Schölkopf, B; Scheffler, K; Zaiss, M

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

MPS-Authors

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Loktyushin, A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons230667

Glang, F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf, B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84187

Scheffler, K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons214560

Zaiss, M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.ismrm.org/21/program-files/O-44.htm
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引用

Dang, H., Weinmüller, S., Loktyushin, A., Glang, F., Dörfler, A., Maier, A., Schölkopf, B., Scheffler, K., & Zaiss, M. (2021). MRzero with dAUTOMAP reconstruction: automated invention of MR acquisition and neural network reconstruction. In 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).

引用: https://hdl.handle.net/21.11116/0000-0008-9011-1

要旨

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.