Abstract
In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression–guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis.
Similar content being viewed by others
References
Bauer W, Bessler FT, Zabler E, Bergmann RB (2004) Computer tomography for nondestructive testing in the automotive industry, vol 5535. Optical Science and Technology, the SPIE 49th Annual Meeting. SPIE
Flisch A, Wirth J, Zanini R, Breitenstein M, Rudin A, Wendt F, Mnich F (1999) Industrial computed tomography in reverse engineering applications. 87
Willson PD (2000) Apparatus and method for automatic recognition of concealed objects using multiple energy computed tomography
Brenner DJ, Hall E (2007) Computed tomography—an increasing source of radiation exposure. N Engl J Med 357:2277–2284
Ho ST, Hutmacher DW (2006) A comparison of micro CT with other techniques used in the characterization of scaffolds. Biomaterials 27:1362–1376
Scarfe WC, Farman AG (2008) What is cone-beam CT and how does it work? Dent Clin N Am 52:707–730
Aissa J, Rubbert C, Boos J, Schleich C, Thomas C, Kr P, Antoch G, Miese F (2015) Low-tube voltage 100ákVp MDCT in screening of cocaine body packing: image quality and radiation dose compared to 120ákVp MDCT. Abdom Imaging 40:2152–2158
Wang X, He W, Chen J, Hu Z, Zhao L (2015) Feasibility study of radiation dose reduction in adult female pelvic CT scan with low tube-voltage and adaptive statistical iterative reconstruction. Korean J Radiol 16:1047–1055
Sato K, Abe M, Takatsuji T (2018) Development of high-energy and high-resolution X-ray CT. Precis Eng 54:276–283
Li T, Li X, Wang J, Wen J, Lu H, Hsieh J, Liang Z (2004) Nonlinear sinogram smoothing for low-dose X-ray CT. IEEE Trans Nucl Sci 51:2505–2513
Shtok J, Elad M, Zibulevsky M (2011) Sparsity-based sinogram denoising for low-dose computed tomography. In: IEEE ICASSP. IEEE, pp 569–572
Manduca A, Yu L, Trzasko JD, Khaylova N, Kofler JM, McCollough CM, Fletcher JG (2009) Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys 36:4911–4919
Ehman EC, Guimarães LS, Fidler JL, Takahashi N, Ramirez-Giraldo JC, Yu L, Manduca A, Huprich JE, McCollough CH, Holmes D III (2012) Noise reduction to decrease radiation dose and improve conspicuity of hepatic lesions at contrast-enhanced 80-kV hepatic CT using projection space denoising. Am J Roentgenol 198:405–411
Wang J, Li T, Xing L (2009) Iterative image reconstruction for CBCT using edge-preserving prior. Med Phys 36:252–260
Jorgensen JS, Sidky EY, Pan X (2012) Quantifying admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT. IEEE Trans Med Imaging 32:460–473
Li Z, Yu L, Trzasko JD, Lake DS, Blezek DJ, Fletcher JG, McCollough CH, Manduca A (2014) Adaptive nonlocal means filtering based on local noise level for CT denoising. Med Phys 41:83131H
Dabov K, Foi A, Katkovnik V, Egiazarian K (2006) Image denoising with block-matching and 3D filtering. Proc SPIE-IS&T Electronic Imaging:354–365
Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Med Imaging 16:2080–2095
Chen Y, Shi L, Feng Q, Yang J, Shu H, Luo L, Coatrieux J-L, Chen W (2014) Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans Med Imaging 33:2271–2292
Ghadrdan S, Alirezaie J, Dillenseger J-L, Babyn P (2014) Low-dose computed tomography image denoising based on joint wavelet and sparse representation. In: Conf Proc IEEE Eng Med Biol Soc. IEEE, pp 3325–3328
He K, Sun J, Tang X, intelligence m (2012) Guided image filtering. IEEE Trans Pattern Anal 35:1397–1409
Ji D, Qu G, Liu B (2016) Simultaneous algebraic reconstruction technique based on guided image filtering. Opt Express 24:15897–15911
Kak AC, Slaney M, Wang G (2002) Principles of computerized tomographic imaging. Med Phys 29:107
Yu W, Wang C, Nie X, Zeng D (2018) Sparsity-induced dynamic guided filtering approach for sparse-view data toward low-dose x-ray computed tomography. Physics in Medicine & Biology 63
Gordon R, Bender R, Herman GT (1970) Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J Theor Biol 29:471–481
Jiang M, Wang G (2003) Convergence studies on iterative algorithms for image reconstruction. IEEE Trans Med Imaging 22:569–579
Herman GT (2009) Fundamentals of computerized tomography: image reconstruction from projections. Springer Science & Business Media
Lebrun M (2012) An analysis and implementation of the BM3D image denoising method. Image Processing On Line 2:175–213
Hämäläinen K, Harhanen L, Kallonen A, Kujanpää A, Niemi E, Siltanen S (2015) Tomographic X-ray data of a walnut. Physics
Funding
This work was supported in part by Graduate Scientific Research and Innovation Foundation of Chongqing, China, under Grant CYS19026; the National Natural Science Foundation of China under Grants 61771003 and 61701174; and Natural Science Foundation of Hubei Province under Grant 2017CFB168.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
He, Y., Zeng, L., Yu, W. et al. Noise suppression–guided image filtering for low-SNR CT reconstruction. Med Biol Eng Comput 58, 2621–2629 (2020). https://doi.org/10.1007/s11517-020-02246-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-020-02246-1