Parallel CT Reconstruction for Multiple Slices Studies with SuiteSparseQR Factorization Package

[EN] Algebraic factorization methods applied to the discipline of Computerized Tomography (CT) Medical Imaging Reconstruction involve a high computational cost. Since these techniques are significantly slower than the traditional analytical ones and time is critical in this field, we need to employ parallel implementations in order to exploit the machine resources and obtain efficient reconstructions. In this paper, we analyze the performance of the sparse QR decomposition implemented on SuiteSparseQR factorization package applied to the CT reconstruction problem. We explore both the parallelism provided by BLAS threads and the use of the Householder reflections to reconstruct multiple slices at once efficiently. Combining both strategies, we can boost the performance of the reconstructions and implement a reliable and competitive method that gets high-quality CT images.This research has been supported by Universitat Politècnica de València, Generalitat Valenciana under PROMETEO/2018/035 co-financed by FEDER funds, as well as ACIF/2017/075 predoctoral grant, and the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R and TIAMHA co-financed by FEDER funds.Chillarón-Pérez, M.; Vidal-Gimeno, V.; Verdú Martín, GJ. (2019). Parallel CT Reconstruction for Multiple Slices Studies with SuiteSparseQR Factorization Package. Springer. 160-169. https://doi.org/10.1007/978-3-030-22744-9_12160169Brown, R.W., Haacke, E.M., Cheng, Y.C.N., Thompson, M.R., Venkatesan, R.: Magnetic Resonance Imaging: Physical Principles and Sequence Design. Wiley, Hoboken (2014)Brooks, R., Chiro, G.D.: Principles of computer assisted tomography (CAT) in radiographic and radioisotopic imaging. Phys. Med. Biol. 21(5), 689–732 (1976)Flores, L., Vidal, V., Verdú, G.: Iterative reconstruction from few-view projections. Procedia Comput. Sci. 51, 703–712 (2015)Parcero, E., Flores, L., Sánchez, M., Vidal, V., Verdú, G.: Impact of view reduction in CT on radiation dose for patients. Radiat. Phys. Chem. 137, 173–175 (2017)Flores, L.A., Vidal, V., Mayo, P., Rodenas, F., Verdú, G.: Parallel CT image reconstruction based on GPUs. Radiat. Phys. Chem. 95, 247–250 (2014)Chillarón, M., Vidal, V., Segrelles, D., Blanquer, I., Verdú, G.: Combining grid computing and Docker containers for the study and parametrization of CT image reconstruction methods. Procedia Comput. Sci. 108, 1195–1204 (2017)Chillarón, M., Vidal, V., Verdú, G., Arnal, J.: CT medical imaging reconstruction using direct algebraic methods with few projections. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10861, pp. 334–346. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_25Padole, A., Ali Khawaja, R.D., Kalra, M.K., Singh, S.: CT radiation dose and iterative reconstruction techniques. Am. J. Roentgenol. 204(4), W384–W392 (2015)Andersen, A.H., Kak, A.C.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrason. Imaging 6(1), 81–94 (1984)Yu, W., Zeng, L.: A novel weighted total difference based image reconstruction algorithm for few-view computed tomography. PLoS ONE 9(10), 1–10 (2014). https://doi.org/10.1371/journal.pone.0109345Rodríguez-Alvarez, M.J., Sanchez, F., Soriano, A., Moliner, L., Sanchez, S., Benlloch, J.M.: QR-factorization algorithm for computed tomography (CT): comparison with FDK and conjugate gradient (CG) algorithms. IEEE Trans. Radiat. Plasma Med. Sci. 2(5), 459–469 (2018)Golub, G.H., Van Loan, C.F.: Matrix Computations, vol. 3. Johns Hopkins University Press, Baltimore (2013)Davis, T.A.: Algorithm 915, suitesparseQR: multifrontal multithreaded rank-revealing sparse QR factorization. ACM Trans. Math. Softw. 38(1), 8:1–8:22 (2011)Joseph, P.: An improved algorithm for reprojecting rays through pixel images. IEEE Trans. Med. Imaging 1(3), 192–196 (1982)Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)Golub, G.H., Ortega, J.M.: Scientific Computing: An Introduction with Parallel Computing. Academic Press Professional Inc., Cambridge (1993)Arridge, S., Betcke, M., Harhanen, L.: Iterated preconditioned LSQR method for inverse problems on unstructured grids. Inverse Prob. 30(7), 075009 (2014)Hansen, P.C.: The L-curve and its use in the numerical treatment of inverse problems (1999)Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010