Abstract #4787

Accelerated Targeted Coronary MRI Using Sparsity-Regularized SPIRiT-RAKI

Seyed Amir Hossein Hosseini^1,2, Steen Moeller², Sebastian Weingärtner^1,2,3, Kâmil Uğurbil², and Mehmet Akçakaya^1,2

¹Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, ²Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States, ³Computer Assisted Clinical Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany

Long scan duration remains a challenge in coronary MRI. A scan-specific machine learning technique, called Robust Artificial-neural-network for k-space Interpolation (RAKI) has recently shown promising results in accelerating MRI. However, RAKI was originally designed for uniform undersampling patterns. In this study, we propose a technique, called SPIRiT-RAKI that enables RAKI with arbitrary undersampling using scan-specific convolutional neural networks to enforce self-consistency among coils. Regularization terms are also incorporated in the new formulation. Our results indicate that SPIRiT-RAKI can successfully accelerate 3D targeted coronary MRI.

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