# Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK)

arXiv: https://arxiv.org/abs/1911.07219 · HTML: https://mlsanity.com/paper/1911.07219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction](https://mlsanity.com/paper/2202.02814/) (2022)

## Abstract (arXiv)

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