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Abstract #2554

Low-Latency Radial GRAPPA Reconstruction Using Multi-Core CPUs and General Purpose GPU Programming

Haris Saybasili1, 2, Daniel A. Herzka2, Nicole Seiberlich3, Mark Griswold1, 3

1Department of Radiology, Case Western Reserve University, Cleveland, OH, United States; 2Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States; 3Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States

Real-time imaging using non-Cartesian trajectories permits very high acceleration rates with parallel imaging. However, image reconstruction from undersampled non-Cartesian datasets is computationally demanding, and leads to long reconstruction times .We present a hybrid (CPU- and GPU-based), fully auto-calibrated, fast reconstruction implementation for radial GRAPPA that uses RT-GROG to grid radial data prior to FFT. Radial GRAPPA and RT-GROG calibrations were performed on the CPU, while image reconstruction was performed asynchronously on the GPU. Our implementation was tested on healthy volunteer cardiac data for different parameters. Images from 12 coil, 144x256, R=9 data were reconstructed in 33ms/frame (acquisition time 42ms/frame).