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Richard Dorrance November 4, 2011

Literature Review.

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Richard Dorrance November 4, 2011

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  1. Literature Review High Speed 3D Tomographyon CPU, GPU, and FPGANicolas GAC, Stéphane Mancini, Michel Desvignes, Dominique HouzetReconfigurable MPSoC versus GPU:Performance, Power and Energy EvaluationDiana Göhringer, Matthias Birk, Yves Dasse-Tiyo,Nicole Ruiter, Michael Hübner, Jürgen Becker Richard Dorrance November 4, 2011

  2. Review Computed Tomography

  3. Tomography • Basis for CAT scan, MRI, PET, SPECT, etc. • Cross-sectional imagingtechnique using transmissionor reflection data frommultiple angles • Computed Tomography (CT):A form of tomographic reconstruction on computers

  4. Cross-Sections by X-Ray Projections • Project X-ray through biological tissue;measure total absorption of ray by tissue • Projection Pθ(t) is the Radontransform of object functionf(x,y): • Total set of projections calledsinogram

  5. Phantom and Sinogram Shepp-Logan Phantom

  6. CT Reconstruction • Restore image from projection data • Inverse Radon transform • Most common algorithm is filtered backprojection • “Smear” each projection over image plane • Accuracy of reconstruction depends on the number of detectors and projection angles Original 4 Angles 16 Angles 64 Angles 256 Angles

  7. Note on Filtering No Filtering With Filtering

  8. FBP Algorithm • Input: sinogram sino(θ, N) • Output: image img(x,y) for each θ filter sino(θ,*) for each x for each y n = x cos θ + y sin θ img(x,y) = sino(θ, n) + img(x,y) • O(N3) algorithm • But highly parallelizable, given sufficient memory bandwidth; not computationally intensive

  9. High Speed 3D Tomographyon CPU, GPU, and FPGANicolas GAC, Stéphane Mancini, Michel Desvignes, Dominique Houzet

  10. 3PA-PET (Pipelined, Prefetch, Parallelized)

  11. Algorithms

  12. Hardware • CPU • Desktop PC: Pentium 4 (3.2 GHz) • Workstation: bi-Xeon Dual Core (3.0 GHz) • GPU • Nvidia GeForce 8800 GTS (1.2 GHz, 96 Cores) • FPGA • Virtex 4 (200 MHz) • ASIC • Projected/Extrapolated (1.2 GHz)

  13. CPU vs. GPU vs. FPGA vs. ASIC

  14. w/ Proper Normalization

  15. Reconfigurable MPSoC versus GPU:Performance, Power and Energy EvaluationDiana Göhringer, Matthias Birk, Yves Dasse-Tiyo,Nicole Ruiter, Michael Hübner, Jürgen Becker

  16. RAMPSoC • Runtime adaptive multi-processor system-on-chip • ROACH/iBOB-like system from a group out of Germany

  17. 3D Ultrasound Computed Tomography • Mammography for earlybreast cancer detection • 3D USCT works on thesame principles asregular CT scans

  18. Hardware • CPU • AMD Athlon 64 3200+ (2.2 GHz, 1 GB RAM) • GPU • Nvidia Tesla C2050 (1.15 GHz, 448 Cores) • FPGA • Xilinx Virtex-4FX100 (125 MHz)

  19. CPU vs. GPU vs. FPGA

  20. References • N. GAC, et al., “High Speed 3D Tomography on CPU, GPU, and FPGA,” EURASIP Journal on Embedded Systems, vol. 2008, Article ID 930250, 12 pages, 2008. • D. Göhringer, et al., “Reconfigurable MPSoC versus GPU: Performance, power and energy evaluation,” INDIN‘11, pp.848-853, 26-29 July 2011. • A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, IEEE Press, 1988. • J. Hsieh, Computerized Tomography: Principles, Design, Artifacts, and Recent Advancements, SPIE & Wiley, 2009.

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