  Purpose: To develop a scan-specific model that estimates and corrects k-space
errors made when reconstructing accelerated Magnetic Resonance Imaging (MRI)
data.
  Methods: Scan-Specific Artifact Reduction in k-space (SPARK) trains a
convolutional neural network to estimate k-space errors made by an input
reconstruction technique by back-propagating from the mean-squared-error loss
between an auto-calibration signal (ACS) and the input technique's
reconstructed ACS. First, SPARK is applied to GRAPPA and demonstrates improved
robustness over other scan-specific models. Then, SPARK is shown to synergize
with advanced reconstruction techniques by improving image quality when applied
to 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS
region, and 2D/3D wave-encoded imaging.
  Results: SPARK yields 1.5 - 2x RMSE reduction when applied to GRAPPA and
improves robustness to ACS size for various acceleration rates in comparison to
other scan-specific techniques. When applied to advanced parallel imaging
techniques such as 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE
improvement. SPARK with 3D GRAPPA also improves RMSE performance and perceived
image quality without a fully sampled ACS region. Finally, SPARK synergizes
with non-cartesian, 2D and 3D wave-encoding imaging by reducing RMSE between 20
- 25% and providing qualitative improvements.
  Conclusion: SPARK synergizes with physics-based reconstruction techniques to
improve accelerated MRI by training scan-specific models to estimate and
correct reconstruction errors in k-space.
