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Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection

Christian B. Mendl, Steven Eliuk, Michelle Noga, Pierre Boulanger
Mathematics Department, Technische Universitat Munchen, Boltzmannstrasse 3, 85748 Garching, Germany
Electronic Letters on Computer Vision and Image Analysis 12(1):1-16, 2013
@article{mendl2013comprehensive,

   title={Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection},

   author={Mendl, Christian B and Eliuk, Steven and Noga, Michelle and Boulanger, Pierre},

   journal={Electronic Letters on Computer Vision and Image Analysis},

   volume={12},

   number={1},

   pages={1–16},

   year={2013}

}

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This paper provides an extensive runtime, accuracy, and noise analysis of Computed Tomography (CT) reconstruction algorithms using various High-Performance Computing (HPC) frameworks such as: "conventional" multi-core, multi threaded CPUs, Compute Unified Device Architecture (CUDA), and DirectX or OpenGL graphics pipeline programming. The proposed algorithms exploit various built-in hardwired features of GPUs such as rasterization and texture filtering. We compare implementations of the Filtered Back-Projection (FBP) algorithm with fan-beam geometry for all frameworks. The accuracy of the reconstruction is validated using an ACR-accredited phantom, with the raw attenuation data acquired by a clinical CT scanner. Our analysis shows that a single GPU can run a FBP reconstruction 23 time faster than a 64-core multi-threaded CPU machine for an image of 1024 x 1024. Moreover, directly programming the graphics pipeline using DirectX or OpenGL can further increases the performance compared to a CUDA implementation.
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