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Proceedings Paper

Acceleration of fluoro-CT reconstruction for a mobile C-arm on GPU and FPGA hardware: a simulation study
Author(s): Xinwei Xue; Arvi Cheryauka; David Tubbs
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Paper Abstract

CT imaging in interventional and minimally-invasive surgery requires high-performance computing solutions that meet operational room demands, healthcare business requirements, and the constraints of a mobile C-arm system. The computational requirements of clinical procedures using CT-like data are increasing rapidly, mainly due to the need for rapid access to medical imagery during critical surgical procedures. The highly parallel nature of Radon transform and CT algorithms enables embedded computing solutions utilizing a parallel processing architecture to realize a significant gain of computational intensity with comparable hardware and program coding/testing expenses. In this paper, using a sample 2D and 3D CT problem, we explore the programming challenges and the potential benefits of embedded computing using commodity hardware components. The accuracy and performance results obtained on three computational platforms: a single CPU, a single GPU, and a solution based on FPGA technology have been analyzed. We have shown that hardware-accelerated CT image reconstruction can be achieved with similar levels of noise and clarity of feature when compared to program execution on a CPU, but gaining a performance increase at one or more orders of magnitude faster. 3D cone-beam or helical CT reconstruction and a variety of volumetric image processing applications will benefit from similar accelerations.

Paper Details

Date Published: 2 March 2006
PDF: 8 pages
Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 61424L (2 March 2006); doi: 10.1117/12.653839
Show Author Affiliations
Xinwei Xue, Univ. of Utah (United States)
Arvi Cheryauka, GE Healthcare (United States)
David Tubbs, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 6142:
Medical Imaging 2006: Physics of Medical Imaging
Michael J. Flynn; Jiang Hsieh, Editor(s)

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