Recently, there has been an interest in machine learning reconstruction techniques for accelerated MRI, where the focus has been on training regularizers on large databases. Another line of work, called Robust Artificial-neural-networks for k-space Interpolation (RAKI) explored the use of CNNs, trained on subject-specific ACS data for improving parallel imaging. In this work, we propose a ResNet architecture, called Residual RAKI (rRAKI) for training a subject-specific CNN that simultaneously approximates a linear convolutional operator and a nonlinear component that compensates for noise amplification artifacts that arise from coil geometry. Brain data shows improved noise resilience at high acceleration rates.
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