Abstract
In medicine, the acquisition process in Computed Tomography Images (CT) is obtained by a reconstruction algorithm. The classical method for image reconstruction is the Filtered Back Projection (FBP). This method is fast and simple but does not use any statistical information about the measurements. The appearance of artifacts and its low spatial resolution in reconstructed images must be considered. Furthermore, the FBP requires of optimal conditions of the projections and complete sets of data. In this paper a methodology to accelerate acquisition process for CT based on the Maximum Likelihood Estimation Method (MLEM) algorithm is presented. This statistical iterative reconstruction algorithm uses a GPU Programming Paradigms and was compared with sequential algorithms in which the reconstruction time was reduced by up to 3 orders of magnitude while preserving image quality. Furthermore, they showed a good performance when compared with reconstruction methods provided by commercial software. The system, which would consist exclusively of a commercial laptop and GPU could be used as a fast, portable, simple and cheap image reconstruction platform in the future.
References
S. C. Bushong, Manual de radiología para técnicos: Física, biología y protección radiológica. Elsevier, 2010.
Health at a Glance 2017. OECD Publishing, 2017. https://doi.org/10.1787/19991312
T. M. Buzug, Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Springer, Berlin, Heidelberg, 2008.
K. Peace et al., J. Neurosci. Nurs. 42, 109, 2010. https://doi.org/10.1097/JNN.0b013e3181ce5c5b
L. A. Feldkamp, L. C. Davis and J. W. Kress, J. Opt. Soc. Am. 1, 612 (1984). https://doi.org/10.1364/JOSAA.1.000612
A. C. Kak, M. Slaney, G. Wang, Med. Phys. 29, 107 (2002). https://doi.org/10.1118/1.1455742
S. R. Deans, The Radon Transform and Some of Its Applications. John Wiley & Sons, Inc., 1983.
J. Radon, Akad. Wiss. 69, 262 (1917).
F. Natterer, The Mathematics of Computerized Tomography. Society for Industrial and Applied Mathematics, 2001. https://doi.org/10.1137/1.9780898719284
B. F. Hutton, J. Nuyts, Y. H. Zaidi, “Iterative Reconstruction Methods”, en Quantitative Analysis in Nuclear Medicine Imaging, H. Zaidi, Ed. Boston, MA: Springer US, 2006, pp. 107–14. https://doi.org/10.1007/0-387-25444-7_4
G. L. Zeng, Medical Image Reconstruction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
J. Hsieh, B. Nett, Z. Yu, K. Sauer, J.-B. Thibault, C. A. Bouman, Curr. Radiol. Rep. 1, 39 (2013). https://doi.org/10.1007/s40134-012-0003-7
L.A. Shepp Y. Vardi, IEEE Trans. Med. Imaging 1, 113 (1982). https://doi.org/10.1109/TMI.1982.4307558
H. Shi, S. Luo, Z. Yang, G. Wu, PLoS One 10, 1 (2015). https://doi.org/10.1371/journal.pone.0138498
S. Vandenberghe et al., Computerized Medical Imaging and Graphics 25, 105 (2001). https://doi.org/10.1016/S0895-6111(00)00060-4
L. L. Geyer et al., Radiology 276, 339 (2015). https://doi.org/10.1148/radiol.2015132766
S. Cook, CUDA Programming: A Developer’s Guide to Parallel Computing with GPUs, núm. 1. 2013.
J. Sanders, E. Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st ed. Addison-Wesley Professional, 2010.
M. Schellmann et al., J. Supercomput. 57, 151 (2011). https://doi.org/10.1007/s11227-010-0397-z
R. Whitrow, OpenGL Graphics Through Applications, 1a ed. Springer Publishing Company, Incorporated, 2008.
GNU Project, “GCC, the GNU Compiler Collection”. 1987.
NVIDIA, “GPU-Accelerated applications”. 2019.
S. Che et al., J. Parallel Distrib. Comput. 68, 1370 (2008). https://doi.org/10.1016/j.jpdc.2008.05.014
A. Eklund, P. Dufort, D. Forsberg and S. M. LaConte, Med. Image Anal. 17, 1073 (2013). https://doi.org/10.1016/j.media.2013.05.008
T. Kalaiselvi, P. Sriramakrishnan and K. Somasundaram, Informatics Med. Unlocked 9, 133 (2017). https://doi.org/10.1016/j.imu.2017.08.001
N. Tatarchuk, J. Shopf and C. DeCoro, J. Parallel Distrib. Comput. 68, 1319 (2008). https://doi.org/10.1016/j.jpdc.2008.06.011
L. A. Flores, V. Vidal, P. Mayo, F. Rodenas and G. Verdú, Procedia Comput. Sci. 18, 1412 (2013). https://doi.org/10.1016/j.procs.2013.05.308
M.A. Belzunce, C.A. Verrastro, E. Venialgo and I. M. Cohen, Open Med. Imaging J. 108 (2012). https://doi.org/10.2174/1874347101206010108
G. Pratx and L. Xing, Med. Phys. 38, 2685 (2011). https://doi.org/10.1118/1.3578605
K. Lange, M. Bahn and R. Little, IEEE Trans. Med. Imaging. 6, 106 (1987). https://doi.org/10.1109/TMI.1987.4307810
E. S. Gopi, Digital Signal Processing for Medical Imaging Using Matlab. New York, NY, USA: Springer, 2013. https://doi.org/10.1007/978-1-4614-3140-4
V. Hemelryck Tessa, W. Sarah, G. Maggie, B. Kees Joost, y J. Sijbers, “ITERATIVE RECONSTRUCTION ALGORITHMS The implementation of iterative reconstruction algorithms in MATLAB”, 2007.
L. Han, “Tools for 2-D Tomographic Reconstruction”, GitHub repository. GitHub, 2017.
A. Biguri, M. Dosanjh, S. Hancock and M. Soleimani, Biomed. Phys. Eng. Express 2, 55010 (2016). https://doi.org/10.1088/2057-1976/2/5/055010
E. Y. Sidky and X. Pan, Phys. Med. Biol. 53, 4777 (2008). https://doi.org/10.1088/0031-9155/53/17/021
I. Intel Corporation Willow Garage, “Open Source Computer Vision”. 2000.
K. Clark et al., J. Digit. Imaging. 26, 1045 (2013). https://doi.org/10.1007/s10278-013-9622-7
T. A. Valencia-Pérez; J. M. Hernández-López; E. Moreno-Barbosa; B. de Celis-Alonso. Study of CT Images Processing With the Implementation of MLEM Algorithm Using CUDA on NVIDIA’S GPU Framework. J. Nucl. Phy. Mat. Sci. Rad. A. 2020, 7, 165-171.
