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Abstract #4656

Dynamic Multi-Coil Reconstruction using Variational Networks

Kerstin Hammernik1, Matthias Schloegl2, Erich Kobler1, Rudolf Stollberger2,3, and Thomas Pock1

1Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 2Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 3BioTechMed-Graz, Graz, Austria

In this work, we present a variational network for reconstructing dynamic multi-coil data. Incorporation of parallel imaging increases the acceleration potential due to additional spatial information, but was not considered so far in other learning-based reconstruction approaches for dynamic MRI. We show that variational network reconstructions with learned spatio-temporal regularization lead to further improvements in image quality compared to state-of-the-art Compressed Sensing approaches for different CINE cardiac datasets and acceleration factors with 10-times faster reconstruction time.

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