  We propose a convolutional neural network (CNN) approach that works
synergistically with physics-based reconstruction methods to reduce artifacts
in accelerated MRI. Given reconstructed coil k-spaces, our network predicts a
k-space correction term for each coil. This is done by matching the difference
between the acquired autocalibration lines and their erroneous reconstructions,
and generalizing this error term over the entire k-space. Application of this
approach on existing reconstruction methods show that SPARK suppresses
reconstruction artifacts at high acceleration, while preserving and improving
on detail in moderate acceleration rates where existing reconstruction
algorithms already perform well; indicating robustness. Introduction Parallel
