article

High-performance cone beam reconstruction using CUDA compatible GPUs

Authors:

Kenichi HagiharaAuthors Info & Claims

Parallel Computing, Volume 36, Issue 2-3

Pages 129 - 141

https://doi.org/10.1016/j.parco.2010.01.004

Published: 01 February 2010 Publication History

Abstract

Compute unified device architecture (CUDA) is a software development platform that allows us to run C-like programs on the nVIDIA graphics processing unit (GPU). This paper presents an acceleration method for cone beam reconstruction using CUDA compatible GPUs. The proposed method accelerates the Feldkamp, Davis, and Kress (FDK) algorithm using three techniques: (1) off-chip memory access reduction for saving the memory bandwidth; (2) loop unrolling for hiding the memory latency; and (3) multithreading for exploiting multiple GPUs. We describe how these techniques can be incorporated into the reconstruction code. We also show an analytical model to understand the reconstruction performance on multi-GPU environments. Experimental results show that the proposed method runs at 83% of the theoretical memory bandwidth, achieving a throughput of 64.3 projections per second (pps) for reconstruction of 512^3-voxel volume from 360 512^2-pixel projections. This performance is 41% higher than the previous CUDA-based method and is 24 times faster than a CPU-based method optimized by vector intrinsics. Some detailed analyses are also presented to understand how effectively the acceleration techniques increase the reconstruction performance of a naive method. We also demonstrate out-of-core reconstruction for large-scale datasets, up to 1024^3-voxel volume.

References

[1]

Kachelrieí, M., Knaup, M. and Bockenbach, O., Hyperfast parallel-beam and cone-beam backprojection using the cell general purpose hardware. Medical Physics. v34 i4. 1474-1486.

[2]

Xu, F. and Mueller, K., Real-time 3D computed tomographic reconstruction using commodity graphics hardware. Physics in Medicine and Biology. v52 i12. 3405-3419.

[3]

H. Scherl, B. Keck, M. Kowarschik, J. Hornegger, Fast GPU-based CT reconstruction using the common unified device architecture (CUDA), in: Proc. Nuclear Science Symp. and Medical Imaging Conf. (NSS/MIC'07), 2007, pp. 4464-4466.

[4]

D. Riabkov, X. Xue, D. Tubbs, A. Cheryauka, Accelerated cone-beam backprojection using GPU-CPU hardware, in: Proc. Ninth Int'l Meeting Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D '07), 2007, pp. 68-71.

[5]

X. Zhao, J. Bian, E.Y. Sidky, S. Cho, P. Zhang, X. Pan, GPU-based 3D cone-beam CT image reconstruction: application to micro CT, in: Proc. Nuclear Science Symp. and Medical Imaging Conf. (NSS/MIC'07), 2007, pp. 3922-3925.

[6]

T. Schiwietz, S. Bose, J. Maltz, R. Westermann, A fast and high-quality cone beam reconstruction pipeline using the GPU, in: Proc. SPIE Medical Imaging (MI 2007), 2007, pp. 1279-1290.

[7]

P.B. Noël, A.M. Walczak, K.R. Hoffmann, J. Xu, J.J. Corso, S. Schafer, Clinical evaluation of GPU-based cone beam computed tomography, in: Proc. High-Performance Medical Image Computing and Computer Aided Intervention (HP-MICCAI'08), 2008.

[8]

N. Gac, S. Mancini, M. Desvignes, Hardware/software 2D-3D backprojection on a SoPC platform, in: Proc. 21st ACM Symp. Applied Computing (SAC'06), 2006, pp. 222-228.

[9]

Luebke, D. and Humphreys, G., How GPUs work. Computer. v40 i2. 96-100.

[10]

Gschwind, M., Hofstee, H.P., Flachs, B., Hopkins, M., Watanabe, Y. and Yamazaki, T., Synergistic processing in cell's multicore architecture. IEEE Micro. v26 i2. 10-24.

[11]

Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E. and Purcell, T.J., A survey of general-purpose computation on graphics hardware. Computer Graphics Forum. v26 i1. 80-113.

[12]

Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E. and Phillips, J.C., GPU computing. Proceedings of the IEEE. v96 i5. 879-899.

[13]

nVIDIA Corporation, CUDA Programming Guide Version 1.1 (November 2007). URL <http://developer.nvidia.com/cuda/>.

[14]

F. Ino, S. Yoshida, K. Hagihara, RGBA packing for fast cone beam reconstruction on the GPU, in: Proc. SPIE Medical Imaging (MI 2009), 2009, 8pp. (CD-ROM).

[15]

Shreiner, D., Woo, M., Neider, J. and Davis, T., OpenGL Programming Guide. 2005. 5th ed. Addison-Wesley, Reading, MA.

[16]

Y. Okitsu, F. Ino, K. Hagihara, Fast cone beam reconstruction using the CUDA-enabled GPU, in: Proc. 15th Int'l Conf. High Performance Computing (HiPC'08), 2008, pp. 108-119.

[17]

Feldkamp, L.A., Davis, L.C. and Kress, J.W., Practical cone-beam algorithm. Journal of Optical Society of America. v1 i6. 612-619.

[18]

H. Yang, M. Li, K. Koizumi, H. Kudo, Accelerating backprojections via CUDA architecture, in: Proc. Ninth Int'l Meeting Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully 3D '07), 2007, pp. 52-55.

[19]

Shattuck, D.W., Rapela, J., Asma, E., Chatzioannou, A., Qi, J. and Leahy, R.M., Internet2-based 3D PET image reconstruction using a PC cluster. Physics in Medicine and Biology. v47 i15. 2785-2795.

[20]

Kawasaki, Y., Ino, F., Mizutani, Y., Fujimoto, N., Sasama, T., Sato, Y., Sugano, N., Tamura, S. and Hagihara, K., High-performance computing service over the Internet for intraoperative image processing. IEEE Transactions on Information and Technology in Biomedicine. v8 i1. 36-46.

[21]

Ino, F., Ooyama, K. and Hagihara, K., A data distributed parallel algorithm for nonrigid image registration. Parallel Computing. v31 i1. 19-43.

[22]

B. Cabral, N. Cam, J. Foran, Accelerated volume rendering and tomographic reconstruction using texture mapping hardware, in: Proc. First Symp. Volume Visualization (VVS'94), 1994, pp. 91-98.

[23]

Mueller, K. and Yagel, R., Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware. IEEE Transactions on Medical Imaging. v19 i12. 1227-1237.

[24]

Xu, F. and Mueller, K., Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware. IEEE Transactions on Nuclear Science. v52 i3. 654-663.

[25]

Hudson, H.M. and Larkin, R.S., Accelerated image reconstruction using ordered subsets of projection data. IEEE Transactions on Medical Imaging. v13 i4. 601-609.

[26]

Anderson, A.H. and Kak, A.C., Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrasonic Imaging. v6. 81-94.

[27]

Mark, W.R., Glanville, R.S., Akeley, K. and Kilgard, M.J., Cg: a system for programming graphics hardware in a C-like language. ACM Transactions on Graphics. v22 i3. 896-897.

[28]

T. Ikeda, F. Ino, K. Hagihara, A code motion technique for accelerating general-purpose computation on the GPU, in: Proc. 20th IEEE Int'l Parallel and Distributed Processing Symp. (IPDPS'06), 2006, 10pp. (CD-ROM).

[29]

Grass, M., Köhler, T. and Proksa, R., 3D cone-beam CT reconstruction for circular trajectories. Physics in Medicine and Biology. v45 i2. 329-347.

[30]

H. Turbell, Cone-beam reconstruction using filtered backprojection, Ph.D. Thesis, Linköpings Universitet, Linköping, Sweden, 2001.

[31]

Grama, A., Gupta, A., Karypis, G. and Kumar, V., Introduction to Parallel Computing. 2003. 2nd ed. Addison-Wesley, Reading, MA.

[32]

Shepp, L.A. and Logan, B.F., The fourier reconstruction of a head section. IEEE Transactions on Nuclear Science. v21 i3. 21-43.

[33]

G.M. Amdahl, Validity of the single processor approach to achieving large scale computing capabilities, in: Proc. the AFIPS Conf., 1967, pp. 483-485.

Digital Library

Cited By

Chen PWahib MTakizawa STakano RMatsuoka STaufer MBalaji PPeña A(2019)iFDKProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3356163(1-24)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3295500.3356163
Alves Pereira LJanssens ECavalcanti GTsang IVan Dael MVerboven PNicolai BSijbers J(2017)Inline discrete tomography systemComputers and Electronics in Agriculture10.1016/j.compag.2017.04.010138:C(117-126)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.compag.2017.04.010
Ikuzawa TIno FHagihara K(2016)Reducing memory usage by the lifting-based discrete wavelet transform with a unified buffer on a GPUJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.03.01093:C(44-55)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jpdc.2016.03.010
Show More Cited By

Recommendations

A performance study of general-purpose applications on graphics processors using CUDA

Graphics processors (GPUs) provide a vast number of simple, data-parallel, deeply multithreaded cores and high memory bandwidths. GPU architectures are becoming increasingly programmable, offering the potential for dramatic speedups for a variety of ...
Performance analysis and optimization strategies for a D3Q19 lattice Boltzmann kernel on nVIDIA GPUs using CUDA

This paper presents implementation strategies and optimization approaches for a D3Q19 lattice Boltzmann flow solver on nVIDIA graphics processing units (GPUs). Using the STREAM benchmarks we demonstrate the GPU parallelization approach and obtain an ...
Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm

Modern graphics cards provide computational capabilities that exceed current CPUs. As one of the computational intensive problems, numerical weather prediction has the opportunity to benefit from the massive number of threads and large memory throughput ...

Comments

Information & Contributors

Information

Published In

cover image Parallel Computing

Parallel Computing Volume 36, Issue 2-3

February, 2010

82 pages

ISSN:0167-8191

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2010.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2010

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen PWahib MTakizawa STakano RMatsuoka STaufer MBalaji PPeña A(2019)iFDKProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3356163(1-24)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3295500.3356163
Alves Pereira LJanssens ECavalcanti GTsang IVan Dael MVerboven PNicolai BSijbers J(2017)Inline discrete tomography systemComputers and Electronics in Agriculture10.1016/j.compag.2017.04.010138:C(117-126)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.compag.2017.04.010
Ikuzawa TIno FHagihara K(2016)Reducing memory usage by the lifting-based discrete wavelet transform with a unified buffer on a GPUJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.03.01093:C(44-55)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jpdc.2016.03.010
Jiang NYang WDuan LXu XHuang CLiu Q(2012)Acceleration of CT reconstruction for wheat tiller inspection based on adaptive minimum enclosing rectangleComputers and Electronics in Agriculture10.1016/j.compag.2012.04.00485(123-133)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.1016/j.compag.2012.04.004

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents