  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 and correct 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, such as RAKI and residual-RAKI.
Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to
improve reconstruction performance. SPARK also improves reconstruction quality
when applied to advanced acquisition and reconstruction techniques like 2D
virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS
region, and 2D/3D wave-encoded images.
  Results: SPARK yields 1.5x - 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 reconstruction
techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to
20% RMSE improvement. SPARK with 3D GRAPPA also improves performance by ~2x 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 acquisition and
reconstruction techniques to improve accelerated MRI by training scan-specific
models to estimate and correct reconstruction errors in k-space.

Signal Processing;Machine Learning;Medical Physics