Abstract #1053
Iterative RAKI with complex-valued convolution for improved image reconstruction with limited training samples
Peter Dawood1,2, Martin Blaimer3, Felix Breuer3, Paul R. Burd4, István Homolya5,6, Peter M. Jakob1, and Johannes Oberberger2
1Department of Physics, University of Würzburg, Würzburg, Germany, 2Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany, 3Magnetic Resonance and X-ray Imaging Department, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany, 4Institute for Theoretical Physics and Astrophysics, University of Würzburg, Würzburg, Germany, 5Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary, 6Institute of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary
Synopsis
Recently, the Parallel Imaging method GRAPPA has been generalized by the deep-learning method RAKI, in which Convolutional Neural Networks are used for non-linear k-space interpolation. RAKI uses scan-specific training data, however, due to its increased parameter-space, its reconstruction quality may deteriorate given a limited training-data amount. We evaluate an approach that includes augmented training-data via an initial GRAPPA k-space reconstruction, and weights refinement by iterative training. Thereby, severe residual artefacts are suppressed in RAKI, while preserving its resilience against g-factor noise enhancement in GRAPPA for standard 2D imaging at medium accelerations, for strongly varying contrast between training- and interpolation-data, too.
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