SC13 Home > SC13 Schedule > SC13 Presentation - High Performance Computing for High Patient Throughput Magnetic Resonance Imaging

SCHEDULE: NOV 16-22, 2013

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High Performance Computing for High Patient Throughput Magnetic Resonance Imaging

SESSION: HPC Impact Showcase Thursday

EVENT TYPE: HPC Impact Showcases

TIME: 12:20PM - 1:00PM

SESSION CHAIR: Ray Bair

ROOM:Booth 3947

ABSTRACT:
The increased importance of health care cost containment has led to the development of techniques for achieving high patient throughput in magnetic resonance imaging (MRI). These clinically relevant techniques, often a product of collaborative industrial and academic research efforts, reduce cost by minimizing patient imaging time. Sophisticated image reconstruction algorithms are typically required to ensure clinically relevant image quality. High-performance computing (HPC) resources are then utilized to facilitate real-time imaging for radiologists and neuroscience researchers. Through partnership with Siemens Medical Solutions we illustrate successful collaboration for the development, integration, and distribution of the Simultaneous Multi-Slice (SMS) imaging technique. SMS allows for information from multiple regions of the human body to be encoded concurrently. The expertise of our industry collaborator was leveraged for the formulation of appropriate image-quality metrics. Based on these metrics, penalty-driven optimization models were formulated. Utilizing continuous feedback from Siemens, we successfully embedded our SMS reconstruction algorithm into several MRI platforms. Software packages were jointly released for testing at major hospital and research centers around the world. Work is on-going to develop SMS into a real-time tool through utilization of the multi-CPU multi-GPU HPC environment available on the newest Siemens scanners. At this time we are involved in a new joint collaboration with Siemens for the development of real-time compressed sensing (CS) algorithms. CS is a general technique for producing images using rapidly acquired data. Here, we will focus on the balance between algorithm simplicity, algorithm efficiency, and the cost-benefit of HPC for MRI applications.

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