Abstract #3838

A Generic, Multi-Node, Multi-GPU Reconstruction Framework for Online, Real-Time, Low-Latency MRI

Haris Saybasili¹, Daniel A. Herzka², Kestutis Barkauskas³, Nicole Seiberlich³, Mark A. Griswold¹

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

In the recent years, many research oriented, customizable, external MR image reconstruction frameworks have been presented. To the best of our knowledge, none of these frameworks provided fully automated, remotely and locally distributed (multi-node, and multi-GPU) image reconstruction capabilities. Additionally, these frameworks may depend on high-level software libraries that make it difficult to maintain, debug and update the existing code. In this work, we present a highly customizable, automatically distributed, multi-threaded image reconstruction environment, built using only low-level system libraries for improved performance and portability. Our framework utilizes multiple GPUs and multiple workstations (nodes) by transparently distributing reconstruction tasks.