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An Open Source GPU Accelerated Framework for Flexible Algebraic Reconstruction at Synchrotron Light Sources

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The recent developments in detector technology made possible 4D (3D + time) X-ray microtomographywith high spatial and time resolutions. The resolution and duration of such experiments is currently limited by destructive X-ray radiation. Algebraic reconstruction technique (ART) can incorporate a priori knowledge into a reconstruction model that will allow us to apply some approaches to reduce an imaging dose and keep a good enough reconstruction quality. However, these techniques are very computationally demanding. In this paper we present a framework for ART reconstruction based on OpenCL technology. Our approach treats an algebraic method as a composition of interacting blocks which performdifferent tasks, such as projection selection, minimization, projecting and regularization. These tasks are realised using multiple algorithms differing in performance, the quality of reconstruction, and the area of applicability. Our framework allows to freely combine algorithms to build the reconstruction chain. All algorithms are implemented with OpenCL and are able to run on a wide range of parallel hardware. As well the framework is easily scalable to clustered environment with MPI. We will describe the architecture of ART framework and evaluate the quality and performance on latest generation of GPU hardware from NVIDIA and AMD.
Wydawca
Rocznik
Strony
259--274
Opis fizyczny
Bibliogr. 34 poz., rys., wykr.
Twórcy
autor
  • Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT)
autor
  • Department of Automation and Computer Systems Tomsk Polytechnic University (TPU)
  • Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT)
  • Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT)
autor
  • Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT)
  • Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT)
autor
  • Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT)
autor
  • Department of Automation and Computer Systems Tomsk Polytechnic University (TPU)
Bibliografia
  • [1] ASTRA Tomography Toolbox.
  • [2] OpenRecon ”Biomedical Imaging Division, School of Biomedical Engineering and Sciences, Virginia Tech / Wake Forest University”.
  • [3] UFO: Ultra-fast X-ray Imaging.
  • [4] Andersen, A., Kak, A.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm, Ultrasonic imaging, 6(1), 1984, 81–94.
  • [5] Batenburg, K. J., Sijbers, J.: DART: a practical reconstruction algorithm for discrete tomography, Image Processing, IEEE Transactions on, 20(9), 2011, 2542–2553.
  • [6] Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, Image Processing, IEEE Transactions on, 18(11), 2009, 2419–2434.
  • [7] Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences, 2(1), 2009, 183–202.
  • [8] Bushberg, J. T., Boone, J. M.: The essential physics of medical imaging, Lippincott Williams & Wilkins, 2011.
  • [9] Cauchy, A.: M´ethode g´en´erale pour la r´esolution des systemes d´equations simultan´ees, Comp. Rend. Sci. Paris, 25(1847), 1847, 536–538.
  • [10] Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on pure and applied mathematics, 57(11), 2004, 1413–1457.
  • [11] De Man, B., Basu, S.: Distance-driven projection and backprojection, Nuclear Science Symposium Conference Record, 2002 IEEE, 3, IEEE, 2002.
  • [12] Donoho, D. L.: Compressed sensing, Information Theory, IEEE Transactions on, 52(4), 2006, 1289–1306.
  • [13] Gilbert, P.: Iterative methods for the three-dimensional reconstruction of an object from projections, Journal of Theoretical Biology, 36(1), 1972, 105–117.
  • [14] Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems, SIAM Journal on Imaging Sciences, 2(2), 2009, 323–343.
  • [15] Gordon, R., Bender, R., Herman, G. T.: Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography, Journal of theoretical Biology, 29(3), 1970, 471–481.
  • [16] Joseph, P. M.: An improved algorithm for reprojecting rays through pixel images, Medical Imaging, IEEE Transactions on, 1(3), 1982, 192–196.
  • [17] Kaczmarz, S.: Angen¨aherte aufl¨osung von systemen linearer gleichungen, Bulletin International de lAcademie Polonaise des Sciences et des Lettres, 35, 1937, 355–357.
  • [18] Kharfi, F., Kharfi, F.: Mathematics and Physics of Computed Tomography (CT): Demonstrations and Practical Examples, 2013.
  • [19] Lauritsch, G., Bruder, H.: Head phantom.
  • [20] Long, Y., Fessler, J. A., Balter, J. M.: 3D forward and back-projection for X-ray CT using separable footprints, Medical Imaging, IEEE Transactions on, 29(11), 2010, 1839–1850.
  • [21] Mirone, A., Brun, E., Coan, P.: A Convex Functional for Image Denoising based on Patches with Constrained Overlaps and its vectorial application to Low Dose Differential Phase Tomography, arXiv preprint arXiv:1305.1256, 2013.
  • [22] Mirone, A., Brun, E., Gouillart, E., Tafforeau, P., Kieffer, J.: The PyHST2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 2014.
  • [23] Moosmann, J., Ershov, A., Altapova, V., Baumbach, T., Prasad, M. S., LaBonne, C., Xiao, X., Kashef, J., Hofmann, R.: X-ray phase-contrast in vivo microtomography probes new aspects of Xenopus gastrulation, Nature, 497(7449), 2013, 374–377.
  • [24] Mueller, K.: Fast and accurate three-dimensional reconstruction from cone-beam projection data using algebraic methods, Ph.D. Thesis, The Ohio State University, 1998.
  • [25] Palenstijn, W., Batenburg, K., Sijbers, J.: Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs), Journal of structural biology, 176(2), 2011, 250–253.
  • [26] Rack, A., Garc´ıa-Moreno, F., Baumbach, T., Banhart, J.: Synchrotron-based radioscopy employing spatiotemporal micro-resolution for studying fast phenomena in liquid metal foams, Journal of synchrotron radiation, 16(3), 2009, 432–434.
  • [27] Rack, A., Garcia-Moreno, F., Schmitt, C., Betz, O., Cecilia, A., Ershov, A., Rack, T., Banhart, J., Zabler, S.: On the possibilities of hard X-ray imaging with high spatio-temporal resolution using polychromatic synchrotron radiation, Journal of X-ray Science and Technology, 18(4), 2010, 429–441.
  • [28] Rack, A., Helwig, H.-M., B¨utow, A., Rueda, A., Matijaˇsevi´c-Lux, B., Helfen, L., Goebbels, J., Banhart, J.: Early pore formation in aluminium foams studied by synchrotron-based microtomography and 3-D image analysis, Acta Materialia, 57(16), 2009, 4809–4821.
  • [29] Rit, S., Oliva, M. V., Brousmiche, S., Labarbe, R., Sarrut, D., Sharp, G. C.: The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK), Journal of Physics: Conference Series, 489, IOP Publishing, 2014.
  • [30] Rudin, L. I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60(1), 1992, 259–268.
  • [31] Sidky, E. Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization, Physics in medicine and biology, 53(17), 2008, 4777.
  • [32] Vogelgesang, M., Chilingaryan, S., Kopmann, A., et al.: UFO: A Scalable GPU-based Image Processing Framework for On-line Monitoring, High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on, IEEE, 2012.
  • [33] Weisstein, E. W.: Method of steepest descent, MathWorld–A Wolfram Web Resource, http://mathworld. wolfram. com/MethodofSteepestDescent. html, 2005.
  • [34] Wilmet, S.: The GLib/GTK+ Development Platform, 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ef55c6bc-ff1d-40e8-8e95-a2146844259e
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