# Accelerated Coronary MRI with sRAKI: A Database-Free Self-Consistent Neural Network k-space Reconstruction for Arbitrary Undersampling

arXiv: https://arxiv.org/abs/1907.08137 · HTML: https://mlsanity.com/paper/1907.08137/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
- [eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction](https://mlsanity.com/paper/2108.04218/) (2021)
- [Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging](https://mlsanity.com/paper/2204.01979/) (2022)
- [Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior](https://mlsanity.com/paper/2609.01959/) (2026)

## Abstract (arXiv)

This study aims to accelerate coronary MRI using a novel reconstruction algorithm, called self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI). sRAKI performs iterative parallel imaging reconstruction by enforcing coil self-consistency using subject-specific neural networks. This approach extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs). These CNNs are trained individually for each scan using the scan-specific autocalibrating signal (ACS) data. Reconstruction is performed by imposing the learned self-consistency and data-consistency enabling sRAKI to support random undersampling patterns. Fully-sampled targeted right coronary artery MRI was acquired in six healthy subjects for evaluation. The data were retrospectively undersampled, and reconstructed using SPIRiT, ℓ1-SPIRiT and sRAKI for acceleration rates of 2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was acquired to further evaluate performance. The results indicate that sRAKI reduces noise amplification and blurring artifacts compared with SPIRiT and ℓ1-SPIRiT, especially at high acceleration rates in targeted data. Quantitative analysis shows that sRAKI improves normalized mean-squared-error (~44% and ~21% over SPIRiT and ℓ1-SPIRiT at rate 5) and vessel sharpness (~10% and ~20% over SPIRiT and ℓ1-SPIRiT at rate 5). In addition, whole-heart data shows the sharpest coronary arteries when resolved using sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and ℓ1-SPIRiT, respectively. Thus, sRAKI is a database-free neural network-based reconstruction technique that may further accelerate coronary MRI with arbitrary undersampling patterns, while improving noise resilience over linear parallel imaging and image sharpness over ℓ1 regularization techniques.