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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