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BMC Medical Imaging
Published in: BMC Medical Imaging 1/2016

Open Access 01-12-2016 | TECHNICAL ADVANCE

A sinogram denoising algorithm for low-dose computed tomography

Authors: Davood Karimi, Pierre Deman, Rabab Ward, Nancy Ford

Published in: BMC Medical Imaging | Issue 1/2016

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Abstract

Background

From the viewpoint of the patients’ health, reducing the radiation dose in computed tomography (CT) is highly desirable. However, projection measurements acquired under low-dose conditions will contain much noise. Therefore, reconstruction of high-quality images from low-dose scans requires effective denoising of the projection measurements.

Methods

We propose a denoising algorithm that is based on maximizing the data likelihood and sparsity in the gradient domain. For Poisson noise, this formulation automatically leads to a locally adaptive denoising scheme. Because the resulting optimization problem is hard to solve and may also lead to artifacts, we suggest an explicitly local denoising method by adapting an existing algorithm for normally-distributed noise. We apply the proposed method on sets of simulated and real cone-beam projections and compare its performance with two other algorithms.

Results

The proposed algorithm effectively suppresses the noise in simulated and real CT projections. Denoising of the projections with the proposed algorithm leads to a substantial improvement of the reconstructed image in terms of noise level, spatial resolution, and visual quality.

Conclusion

The proposed algorithm can suppress very strong quantum noise in CT projections. Therefore, it can be used as an effective tool in low-dose CT.
Literature
1.
Berrington de González A, Mahesh M, Kim K, et al.Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2009; 169(22):2071.CrossRefPubMed
2.
Beister M, Kolditz D, Kalender WA. Iterative reconstruction methods in X-ray CT. Physica Med. 2012; 28(2):94.CrossRef
3.
Fessler JA. Statistical image reconstruction methods for transmission tomography. Handb Med Imaging. 2000; 2:1.
4.
Hsieh J. Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise. Med Phys. 1998; 25(11):2139.CrossRefPubMed
5.
Kachelriess M, Watzke O, Kalender W. Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT. Med Phys. 2001; 28(4):475.CrossRefPubMed
6.
Forthmann P, Köhler T, Begemann PGC, Defrise M. Penalized maximum-likelihood sinogram restoration for dual focal spot computed tomography. Phys Med Biol. 2007; 52(15):4513.CrossRefPubMed
7.
LaRivière PJ. Penalized-likelihood sinogram smoothing for low-dose CT. Med Phys. 2005; 32(6):1676.CrossRef
8.
Kak AC, Slaney M. Principles of Computerized Tomographic Imaging. Philadelphia, PA: Society for Industrial and Applied Mathematics; 2001.CrossRef
9.
Natterer F. The Mathematics of Computerized Tomography. Philadelphia, PA: Society for Industrial and Applied Mathematics; 2001.CrossRef
10.
Jiao C, Wang D, Lu H, Zhang Z, Liang JZ. Multiscale noise reduction on low-dose CT sinogram by stationary wavelet transform. In: Nuclear Science Symposium Conference Record, 2008. NSS ’08. IEEE: 2008. p. 5339–44.
11.
Sahiner B, Yagle A. Reconstruction from projections under time-frequency constraints. Med Imaging IEEE Trans. 1995; 14(2):193.CrossRef
12.
Macovski A. Medical Imaging Systems. Upper Saddle River, NJ: Prentice Hall; 1983.
13.
Wang J, Lu H, Liang Z, Eremina D, Zhang G, Wang S, et al.An experimental study on the noise properties of x-ray CT sinogram data in Radon space. Phys Med Biol. 2008; 53(12):3327.PubMedCentralCrossRefPubMed
14.
Manduca A, Yu L, Trzasko JD, Khaylova N, Kofler JM, McCollough CM, et al.Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys. 2009; 36(11):4911.PubMedCentralCrossRefPubMed
15.
Wang J, Li T, Lu H, Liang Z. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography. Med Imaging IEEE Trans. 2006; 25(10):1272.CrossRef
16.
Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. Image Process IEEE Trans. 2006; 15(12):3736.CrossRef
17.
Mairal J, Elad M, Sapiro G. Sparse representation for color image restoration. IEEE Trans Image Process. 2008; 17(1):53.CrossRefPubMed
18.
Chen Y, Yin X, Shi L, Shu H, Luo L, Coatrieux JL, et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Phys Med Biol. 2013; 58(16):5803.CrossRefPubMed
19.
Li S, Fang L, Yin H. An efficient dictionary learning algorithm and its application to 3-D medical image denoising. Biomed Eng IEEE Trans. 2012; 59(2):417.CrossRef
20.
Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol 2. IEEE: 2005. p. 60–5.
21.
Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Model Simul. 2005; 4(2):490.CrossRef
22.
Maggioni M, Katkovnik V, Egiazarian K, Foi A. A nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process. 2013; 22(1):1057.CrossRef
23.
Rudin LI, Osher S, Fatemi E, Physica D. Nonlinear total variation based noise removal algorithms. Nonlinear Phenomena. 1992; 60(1-4):259.CrossRef
24.
Le T, Chartrand R, Asaki T. A variational approach to reconstructing images corrupted by Poisson noise. J Math Imaging Vis. 2007; 27(3):257.CrossRef
25.
Sawatzky A, Brune C, Müller J, Burger M. Total variation processing of images with Poisson statistics In: Jiang X, Petkov N, editors. Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 5702. Berlin, Heidelberg: Springer: 2009. p. 533–40.
26.
Chambolle A. An algorithm for total variation minimization and applications. J Math Imaging Vis. 2004; 20(1-2):89.
27.
Lefkimmiatis S, Bourquard A, Unser M. Hessian-based norm regularization for image restoration with biomedical applications. Image Process IEEE Trans. 2012; 21(3):983.CrossRef
28.
Lysaker M, Tai X. Iterative image restoration combining total variation minimization and a second-order functional. Int J Comput Vis. 2006; 66(1):5.CrossRef
29.
Louchet C, Moisan L. Total variation as a local filter. SIAM J Imaging Sci. 2011; 4(2):651.CrossRef
30.
31.
Kundel HL, Van Metter RL, Beutel J. Handbook of Medical Imaging, Volume 1. Physics and Psychophysics. Bellingham, WA: SPIE Publications; 2000.
32.
Pluim J, Maintz J, Viergever M. Mutual-information-based registration of medical images: a survey. Med Imaging IEEE Trans. 2003; 22(8):986.CrossRef
33.
Bian J, Siewerdsen JH, Han X, Sidky EY, Prince JL, Pelizzari CA, et al.Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT. Phys Med Biol. 2010; 55(22):6575.PubMedCentralCrossRefPubMed
34.
Feldkamp LA, Davis LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A. 1984; 1(6):612.CrossRef
35.
Wang Z, Bovik A, Sheikh H, Simoncelli E. Image quality assessment: from error visibility to structural similarity. Image Process IEEE Trans. 2004; 13(4):600.CrossRef
36.
Du LY, Umoh J, Nikolov HN, Pollmann SI, Lee TY, Holdsworth DW. A quality assurance phantom for the performance evaluation of volumetric micro-CT systems. Phys Med Biol. 2007; 52(23):7087.CrossRefPubMed
37.
Buhr E, Günther-Kohfahl S, Neitzel U. Simple method for modulation transfer function determination of digital imaging detectors from edge images. In: Medical Imaging 2003. Bellingham, WA, USA: International Society for Optics and Photonics: 2003. p. 877–84.
38.
Beck A, Teboulle M. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. Image Process IEEE Trans. 2009; 18(11):2419.CrossRef
39.
Long Y, Fessler J, Balter J. 3D forward and back-projection for x-ray CT using separable footprints. Med Imaging IEEE Trans. 2010; 29(11):1839.CrossRef
40.
De Man B, Basu S. Distance-driven projection and backprojection in three dimensions. Phys Med Biol. 2004; 49(11):2463.CrossRefPubMed
Metadata
Title
A sinogram denoising algorithm for low-dose computed tomography
Authors
Davood Karimi
Pierre Deman
Rabab Ward
Nancy Ford
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2016
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-016-0112-5

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