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This study aims to accelerate coronary MRI using a novel reconstructionalgorithm, called self-consistent robust artificial-neural-networks for k-spaceinterpolation (sRAKI). sRAKI performs iterative parallel imaging reconstructionby enforcing coil self-consistency using subject-specific neural networks. Thisapproach extends the linear convolutions in SPIRiT to nonlinear interpolationusing convolutional neural networks (CNNs). These CNNs are trained individuallyfor each scan using the scan-specific autocalibrating signal (ACS) data.Reconstruction is performed by imposing the learned self-consistency anddata-consistency enabling sRAKI to support random undersampling patterns.Fully-sampled targeted right coronary artery MRI was acquired in six healthysubjects for evaluation. The data were retrospectively undersampled, andreconstructed using SPIRiT, $\ell_1$-SPIRiT and sRAKI for acceleration rates of2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI wasacquired to further evaluate performance. The results indicate that sRAKIreduces noise amplification and blurring artifacts compared with SPIRiT and$\ell_1$-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 $\ell_1$-SPIRiT at rate 5) and vessel sharpness(~10% and ~20% over SPIRiT and $\ell_1$-SPIRiT at rate 5). In addition,whole-heart data shows the sharpest coronary arteries when resolved usingsRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and$\ell_1$-SPIRiT, respectively. Thus, sRAKI is a database-free neuralnetwork-based reconstruction technique that may further accelerate coronary MRIwith arbitrary undersampling patterns, while improving noise resilience overlinear parallel imaging and image sharpness over $\ell_1$ regularizationtechniques.

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