Haris Saybasili1, 2, Daniel A. Herzka3, Kestutis Barkauskas4, Nicole Seiberlich4, Mark A. Griswold1, 4
1Radiology, Case Western Reserve University, Cleveland, OH, United States; 2Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States; 3Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; 4Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
A hybrid (CPU- and GPU-based), faster-than-acquisition through-time radial GRAPPA reconstruction was previously demonstrated for 15 coil, rate 8 (16 projections, 128x128 matrix) radial datasets. However, because of the increased number of acquisition coils on modern scanners, single-GPU radial GRAPPA reconstructions were challenging for low-latency, real-time MRI with high number of acquisition coils. We present a completely automated, multi-node (group of workstations connected via network), multi-GPU radial GRAPPA implementation that can reconstruct 32-coil 16 projection radial datasets much faster than acquisition. Images from 32 coil, 16x256 data (acquisition time 42ms/frame) were reconstructed in 11.2 ms/frame using four nodes (two GPUs on each).