Computationally Efficient System Matrix Calculation Techniques in Computed Tomography Iterative Reconstruction
DOI: 10.4103/jmss.JMSS_29_19
Abstract
Background: Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we developa hybrid line-area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods: The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results: The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM. Conclusion: We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM-AIM that achieves significantly improved images though with its computational requirements.
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Rampinelli C, Origgi D, Vecchi V, Funicelli L, Raimondi S, Deak P, et al. Ultra-low-dose CT with model-based iterative reconstruction (MBIR): Detection of ground-glass nodules in an anthropomorphic phantom study. Radiol Med 2015;120:611-7.
Brenner DJ, Hall EJ. Computed tomography - An increasing source of radiation exposure. N Engl J Med 2007;357:2277-84.
Sodickson A, Baeyens PF, Andriole KP, Prevedello LM,
Nawfel RD, Hanson R, et al. Recurrent CT, cumulative radiation
exposure, and associated radiation-induced cancer risks from CT
of adults. Radiology 2009;251:175-84.
Pontana F, Henry S, Duhamel A, Faivre JB, Tacelli N, Pagniez J, et al. Impact of iterative reconstruction on the diagnosis of acute pulmonary embolism (PE) on reduced-dose chest CT angiograms. Eur Radiol 2015;25:1182-9.
McCollough CH, Primak AN, Braun N, Kofler J, Yu L, Christner J. Strategies for reducing radiation dose in CT. Radiol Clin North Am 2009;47:27-40.
Fessler JA, Sonka M, Fitzpatrick JM, editors. Statistical image reconstruction methods for transmission tomography. In: Handbook of Medical Imaging. Medical Image Processing and Analysis. Vol. 2. Bellingham, WA: SPIE; 2000.
Thibault JB, Sauer KD, Bouman CA, Hsieh J. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 2007;34:4526-44.
Wang G, Yu H, De Man B. An outlook on x-ray CT research and development. Med Phys 2008;35:1051-64.
Long Y, Fessler JA, Balter JM. 3D forward and back-projection for X-ray CT using separable footprints. IEEE Trans Med Imaging 2010;29:1839-50.
Van Hemelryck Tessa WS, Maggie G, Joost BK, Jan S. The Implementation of Iterative Reconstruction Algorithms in MATLAB: Masters thesis, Department of Industrial Sciences and Technology, University College of Antwerp. Belgium; 2007.
Ha S, Li H, Mueller K, editors. Efficient area-based ray integration using summed area tables and regression models. Proceedings of the 4th International Meeting on Image Formation in X-ray CT; 2016.
Arcadu F, Nilchian M, Studer A, Stampanoni M, Marone F. A forward regridding method with minimal oversampling for accurate and efficient iterative tomographic algorithms. IEEE Trans Image Process 2016;25:1207-18.
De Man B, Basu S, editors. Distance-Driven Projection and Backprojection. IEEE Nucl Sci Symp Conf Rec. IEEE; 2002.
Miao C, Liu B, Xu Q, Yu H. An improved distance-driven method for projection and backprojection. J Xray Sci Technol 2014;22:1-8.
Gao H. Fast parallel algorithms for the x-ray transform and its adjoint. Med Phys 2012;39:7110-20.
Zhang S, Zhang D, Gong H, Ghasemalizadeh O, Wang G, Cao G. Fast and accurate computation of system matrix for area integral model-based algebraic reconstruction technique. Opt Eng 2014;53:113101.
Rahmim A, Cheng JC, Blinder S, Camborde ML, Sossi V. Statistical dynamic image reconstruction in state-of-the-art high-resolution PET. Phys Med Biol 2005;50:4887-912.
Siddon RL. Fast calculation of the exact radiological path for a three-dimensional CT array. Med Phys 1985;12:252-5.
Yu H, Wang G. Finite detector based projection model for high spatial resolution. J Xray Sci Technol 2012;20:229-38.
Jiang M, Wang G. Convergence of the simultaneous algebraic reconstruction technique (SART). IEEE Trans Image Process 2003;12:957-61.
Lange K, Carson R. EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr 1984;8:306-16.
Shepp LA, Logan BF. The Fourier reconstruction of a head section. IEEE Trans Nucl Sci 1974;21:21-43.
Ghadiri H, Rahmim A, Shiran MB, Soltanian-Zadeh H, Ay MR, editors. A Fast and Hardware Mimicking Analytic CT Simulator. 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC); 2013.
Herman GT. Fundamentals of Computerized Tomography: Image Reconstruction from Projections. New York: Springer Science and Business Media; 2009.
Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment. Electron Lett 2008;44:800-1.
Hofmann C, Knaup M, KachelrieB M. Effects of ray profile modeling on resolution recovery in clinical CT. Med Phys 2014;41:021907.
De Man B, Basu S. Distance-driven projection and backprojection in three dimensions. Phys Med Biol 2004;49:2463-75.
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