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Abstract #0569

Online Gadgetron Reconstruction of 3D Magnetic Resonance Fingerprinting via a GPU-Accelerated Azure Kubernetes Cluster

Andrew Dupuis1, Yong Chen1, Rasim Boyacioglu1, John Stairs2, Michael Hansen2, Kelvin Chow3, and Mark A Griswold1
1Case Western Reserve University, Cleveland, OH, United States, 2Microsoft, Redmond, WA, United States, 3Siemens Medical Solutions USA, Inc., Chicago, IL, United States


Reconstruction of 3D Magnetic Resonance Fingerprinting acquisitions is computationally demanding, resulting in long processing times. GPU parallelization of the reconstruction’s NUFFT, pattern matching, and coil combination steps improves performance, but traditionally requires high-performance computers at the scanner. We propose an online reconstruction on a remote GPU-accelerated Kubernetes cluster. This allows many scanners or sites to share easily upgradeable and manageable computing resources. Additional calibration measurements, such as B1 maps, can also be transferred to allow inline B1 inhomogeneity correction. We also demonstrate that 3D-MRF reconstruction is robust with raw data compression that can be used to reduce site-to-cloud bandwidth requirements.

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