Meeting Banner
Abstract #4787

Accelerated Targeted Coronary MRI Using Sparsity-Regularized SPIRiT-RAKI

Seyed Amir Hossein Hosseini1,2, Steen Moeller2, Sebastian Weingärtner1,2,3, Kâmil Uğurbil2, and Mehmet Akçakaya1,2

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here