Abstract

To realize low-dose imaging in X-ray computed tomography (CT) examination, lowering milliampere-seconds (low-mAs) or reducing the required number of projection views (sparse-view) per rotation around the body has been widely studied as an easy and effective approach. In this study, we are focusing on low-dose CT image reconstruction from the sinograms acquired with a combined low-mAs and sparse-view protocol and propose a two-step image reconstruction strategy. Specifically, to suppress significant statistical noise in the noisy and insufficient sinograms, an adaptive sinogram restoration (ASR) method is first proposed with consideration of the statistical property of sinogram data, and then to further acquire a high-quality image, a total variation based projection onto convex sets (TV-POCS) method is adopted with a slight modification. For simplicity, the present reconstruction strategy was termed as “ASR-TV-POCS.” To evaluate the present ASR-TV-POCS method, both qualitative and quantitative studies were performed on a physical phantom. Experimental results have demonstrated that the present ASR-TV-POCS method can achieve promising gains over other existing methods in terms of the noise reduction, contrast-to-noise ratio, and edge detail preservation.

© 2014 Optical Society of America

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  25. J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef] [PubMed]
  28. P. Milasevic and G. R. Ducharme, “Uniqueness of the spatial median,” The Annals of Statistics 15, 1332–1333 (1987).
    [CrossRef]
  29. K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections,” IEEE Trans. Signal Process. 41, C534–C548 (1993).
    [CrossRef]
  30. A. Andersen and A. Kak, “Simultaneous algebraic reconstruction technique (SART): a superior implementation of ART Ultrason,” Imaging 6, 81–94 (1984).
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2014 (1)

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

2013 (2)

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

2012 (3)

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57, 7923–7956 (2012).
[CrossRef] [PubMed]

2011 (1)

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

2009 (3)

A. M. Mendrik, E. J. Vonken, A. Rutten, M. A. Viergever, and B. van Ginneken, “Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch,” IEEE Trans. Med. Imaging 28, 1585–1594 (2009).
[CrossRef] [PubMed]

J. Wang, T. Li, and L. Xing, “Iterative image reconstruction for CBCT using edge-preserving prior,” Med. Phys. 36, 252–260 (2009).
[CrossRef] [PubMed]

L. Yu, “Radiation dose reduction in computed tomography: techniques and future perspective,” Imaging Med. 1, 65–84 (2009).
[CrossRef] [PubMed]

2008 (3)

A. Borsdorf, R. Raupach, T. Flohr, and J. Hornegger, “Wavelet based noise reduction in CT-images using correlation analysis,” IEEE Trans. Med. Imaging 27, 1685–1703 (2008).
[CrossRef] [PubMed]

E. Sidky and X. Pan, “Image reconstruction in circular cone-beam CT by constrained, total-variation minimization,” Phys. Med. Biol. 53, 4777–4807 (2008).
[CrossRef] [PubMed]

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

2007 (2)

A. J. Einstein, M. J. Henzlova, and S. Rajagopalan, “Estimating risk of cancer associated with radiation exposure from 64-slice CT coronary angiography,” J. Am. Med. Assoc. 298, 317–323 (2007).
[CrossRef]

D. J. Brenner and E. J. Hall, “CT – An increasing source of radiation exposure,” New Eng. J. Med. 357, 2277–2284 (2007).
[CrossRef]

2006 (4)

C. H. McCollough, M. R. Bruesewitz, and J. M. Kofler, “CT dose reduction and dose management tools: Overview of available options,” Radiographics 26, 503–512 (2006).
[CrossRef] [PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT,” IEEE Trans. Med. Imaging 25, 1272–1283 (2006).
[CrossRef] [PubMed]

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent beam CT,” J. X-Ray Sci. Technol. 14, 119–139 (2006).

P. J. La Rivière, J. Bian, and P. A. Vargas, “Penalized-likelihood sinogram restoration for computed tomography,” IEEE Trans. Med. Imaging 25, 1022–1036 (2006).
[CrossRef] [PubMed]

2004 (2)

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

2003 (1)

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

2002 (2)

D. F. Yu and J. A. Fessler, “Edge-preserving tomographic reconstruction with nonlocal regularization,” IEEE Trans. Med. Imaging 21, 159–173 (2002).
[CrossRef] [PubMed]

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

1993 (1)

K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections,” IEEE Trans. Signal Process. 41, C534–C548 (1993).
[CrossRef]

1987 (1)

P. Milasevic and G. R. Ducharme, “Uniqueness of the spatial median,” The Annals of Statistics 15, 1332–1333 (1987).
[CrossRef]

1984 (1)

A. Andersen and A. Kak, “Simultaneous algebraic reconstruction technique (SART): a superior implementation of ART Ultrason,” Imaging 6, 81–94 (1984).

Andersen, A.

A. Andersen and A. Kak, “Simultaneous algebraic reconstruction technique (SART): a superior implementation of ART Ultrason,” Imaging 6, 81–94 (1984).

Avinash, G. B.

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Bian, J.

P. J. La Rivière, J. Bian, and P. A. Vargas, “Penalized-likelihood sinogram restoration for computed tomography,” IEEE Trans. Med. Imaging 25, 1022–1036 (2006).
[CrossRef] [PubMed]

Bian, Z.

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Blake, M. A.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

Blezek, D. J.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

Borsdorf, A.

A. Borsdorf, R. Raupach, T. Flohr, and J. Hornegger, “Wavelet based noise reduction in CT-images using correlation analysis,” IEEE Trans. Med. Imaging 27, 1685–1703 (2008).
[CrossRef] [PubMed]

Bouman, C.

K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections,” IEEE Trans. Signal Process. 41, C534–C548 (1993).
[CrossRef]

Bovik, A. C.

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

Brenner, D. J.

D. J. Brenner and E. J. Hall, “CT – An increasing source of radiation exposure,” New Eng. J. Med. 357, 2277–2284 (2007).
[CrossRef]

Bruesewitz, M. R.

C. H. McCollough, M. R. Bruesewitz, and J. M. Kofler, “CT dose reduction and dose management tools: Overview of available options,” Radiographics 26, 503–512 (2006).
[CrossRef] [PubMed]

Chen, J.

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

Chen, W.

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Ducharme, G. R.

P. Milasevic and G. R. Ducharme, “Uniqueness of the spatial median,” The Annals of Statistics 15, 1332–1333 (1987).
[CrossRef]

Einstein, A. J.

A. J. Einstein, M. J. Henzlova, and S. Rajagopalan, “Estimating risk of cancer associated with radiation exposure from 64-slice CT coronary angiography,” J. Am. Med. Assoc. 298, 317–323 (2007).
[CrossRef]

Eremina, D.

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

Fan, Y.

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57, 7923–7956 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Feng, Q.

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

Fessler, J. A.

D. F. Yu and J. A. Fessler, “Edge-preserving tomographic reconstruction with nonlocal regularization,” IEEE Trans. Med. Imaging 21, 159–173 (2002).
[CrossRef] [PubMed]

Fletcher, J. G.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

Flohr, T.

A. Borsdorf, R. Raupach, T. Flohr, and J. Hornegger, “Wavelet based noise reduction in CT-images using correlation analysis,” IEEE Trans. Med. Imaging 27, 1685–1703 (2008).
[CrossRef] [PubMed]

Gao, Y.

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Hall, E. J.

D. J. Brenner and E. J. Hall, “CT – An increasing source of radiation exposure,” New Eng. J. Med. 357, 2277–2284 (2007).
[CrossRef]

Halpern, T. L.

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Hamberg, L. M.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

Han, G.

G. Han, Z. Liang, and J. You, “A fast ray-tracing technique for TCT and ECT studies,” in Proceeding of IEEE NSS-MIC, (Seattle, WA, 1999), pp. 1515–1518.

Henzlova, M. J.

A. J. Einstein, M. J. Henzlova, and S. Rajagopalan, “Estimating risk of cancer associated with radiation exposure from 64-slice CT coronary angiography,” J. Am. Med. Assoc. 298, 317–323 (2007).
[CrossRef]

Hornegger, J.

A. Borsdorf, R. Raupach, T. Flohr, and J. Hornegger, “Wavelet based noise reduction in CT-images using correlation analysis,” IEEE Trans. Med. Imaging 27, 1685–1703 (2008).
[CrossRef] [PubMed]

Hsieh, J.

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

Huang, J.

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Jiang, M.

M. Jiang and G. Wang, “Convergence of the simultaneous algebraic reconstruction technique (SART),” in Proceeding of 35th Asilomar Conf. on Signal, Systems and Computers, (2001), pp. 360–364.

Kak, A.

A. Andersen and A. Kak, “Simultaneous algebraic reconstruction technique (SART): a superior implementation of ART Ultrason,” Imaging 6, 81–94 (1984).

Kalra, M. K.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Kao, C.

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent beam CT,” J. X-Ray Sci. Technol. 14, 119–139 (2006).

Karau, K.

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Kofler, J. M.

C. H. McCollough, M. R. Bruesewitz, and J. M. Kofler, “CT dose reduction and dose management tools: Overview of available options,” Radiographics 26, 503–512 (2006).
[CrossRef] [PubMed]

La Rivière, P. J.

P. J. La Rivière, J. Bian, and P. A. Vargas, “Penalized-likelihood sinogram restoration for computed tomography,” IEEE Trans. Med. Imaging 25, 1022–1036 (2006).
[CrossRef] [PubMed]

Lake, D. S.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

Li, L.

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Li, T.

J. Wang, T. Li, and L. Xing, “Iterative image reconstruction for CBCT using edge-preserving prior,” Med. Phys. 36, 252–260 (2009).
[CrossRef] [PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT,” IEEE Trans. Med. Imaging 25, 1272–1283 (2006).
[CrossRef] [PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

Li, X.

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

Li, Z.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

Liang, Z.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57, 7923–7956 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT,” IEEE Trans. Med. Imaging 25, 1272–1283 (2006).
[CrossRef] [PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

G. Han, Z. Liang, and J. You, “A fast ray-tracing technique for TCT and ECT studies,” in Proceeding of IEEE NSS-MIC, (Seattle, WA, 1999), pp. 1515–1518.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Liu, N.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Liu, Y.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57, 7923–7956 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Lu, H.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT,” IEEE Trans. Med. Imaging 25, 1272–1283 (2006).
[CrossRef] [PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Ma, J.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

Y. Liu, J. Ma, Y. Fan, and Z. Liang, “Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction,” Phys. Med. Biol. 57, 7923–7956 (2012).
[CrossRef] [PubMed]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, W. Chen, and H. Lu, “Variance analysis of x-ray CT sinograms in the presence of electronic noise background,” Med. Phys. 39, 4051–4065 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

J. Ma, Z. Liang, Y. Fan, Y. Liu, J. Huang, L. Li, W. Chen, and H. Lu, “Variance estimation of X-ray CT sinogram in radon domain,” in Proc. SPIE8313, 83132G (2012).
[CrossRef]

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Maher, M. M.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Manduca, A.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

Manzione, J.

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

McCollough, C. H.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

C. H. McCollough, M. R. Bruesewitz, and J. M. Kofler, “CT dose reduction and dose management tools: Overview of available options,” Radiographics 26, 503–512 (2006).
[CrossRef] [PubMed]

Mendrik, A. M.

A. M. Mendrik, E. J. Vonken, A. Rutten, M. A. Viergever, and B. van Ginneken, “Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch,” IEEE Trans. Med. Imaging 28, 1585–1594 (2009).
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P. Milasevic and G. R. Ducharme, “Uniqueness of the spatial median,” The Annals of Statistics 15, 1332–1333 (1987).
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Moore, W.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

Niu, S.

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

Pan, X.

E. Sidky and X. Pan, “Image reconstruction in circular cone-beam CT by constrained, total-variation minimization,” Phys. Med. Biol. 53, 4777–4807 (2008).
[CrossRef] [PubMed]

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent beam CT,” J. X-Ray Sci. Technol. 14, 119–139 (2006).

Rajagopalan, S.

A. J. Einstein, M. J. Henzlova, and S. Rajagopalan, “Estimating risk of cancer associated with radiation exposure from 64-slice CT coronary angiography,” J. Am. Med. Assoc. 298, 317–323 (2007).
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A. Borsdorf, R. Raupach, T. Flohr, and J. Hornegger, “Wavelet based noise reduction in CT-images using correlation analysis,” IEEE Trans. Med. Imaging 27, 1685–1703 (2008).
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A. M. Mendrik, E. J. Vonken, A. Rutten, M. A. Viergever, and B. van Ginneken, “Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch,” IEEE Trans. Med. Imaging 28, 1585–1594 (2009).
[CrossRef] [PubMed]

Saini, S.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

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K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections,” IEEE Trans. Signal Process. 41, C534–C548 (1993).
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Sharma, A.

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Shepard, J. A.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Sidky, E.

E. Sidky and X. Pan, “Image reconstruction in circular cone-beam CT by constrained, total-variation minimization,” Phys. Med. Biol. 53, 4777–4807 (2008).
[CrossRef] [PubMed]

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent beam CT,” J. X-Ray Sci. Technol. 14, 119–139 (2006).

Tian, L.

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

Toth, T. L.

M. K. Kalra, M. M. Maher, T. L. Toth, L. M. Hamberg, M. A. Blake, J. A. Shepard, and S. Saini, “Strategies for CT radiation dose optimization,” Radiology 230, 619–628 (2004).
[CrossRef] [PubMed]

Trzasko, J. D.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

van Ginneken, B.

A. M. Mendrik, E. J. Vonken, A. Rutten, M. A. Viergever, and B. van Ginneken, “Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch,” IEEE Trans. Med. Imaging 28, 1585–1594 (2009).
[CrossRef] [PubMed]

Vargas, P. A.

P. J. La Rivière, J. Bian, and P. A. Vargas, “Penalized-likelihood sinogram restoration for computed tomography,” IEEE Trans. Med. Imaging 25, 1022–1036 (2006).
[CrossRef] [PubMed]

Viergever, M. A.

A. M. Mendrik, E. J. Vonken, A. Rutten, M. A. Viergever, and B. van Ginneken, “Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch,” IEEE Trans. Med. Imaging 28, 1585–1594 (2009).
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Vonken, E. J.

A. M. Mendrik, E. J. Vonken, A. Rutten, M. A. Viergever, and B. van Ginneken, “Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch,” IEEE Trans. Med. Imaging 28, 1585–1594 (2009).
[CrossRef] [PubMed]

Wang, G.

M. Jiang and G. Wang, “Convergence of the simultaneous algebraic reconstruction technique (SART),” in Proceeding of 35th Asilomar Conf. on Signal, Systems and Computers, (2001), pp. 360–364.

Wang, J.

J. Wang, T. Li, and L. Xing, “Iterative image reconstruction for CBCT using edge-preserving prior,” Med. Phys. 36, 252–260 (2009).
[CrossRef] [PubMed]

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT,” IEEE Trans. Med. Imaging 25, 1272–1283 (2006).
[CrossRef] [PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

Wang, K.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

Wang, S.

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
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Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
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T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

Wittram, C.

M. K. Kalra, C. Wittram, M. M. Maher, A. Sharma, G. B. Avinash, K. Karau, T. L. Halpern, S. Saini, and J. A. Shepard, “Can noise reduction filters improve low-radiation-dose chest CT images?” Pilot study Radiology 228, 257–264 (2003).

Xing, L.

J. Wang, T. Li, and L. Xing, “Iterative image reconstruction for CBCT using edge-preserving prior,” Med. Phys. 36, 252–260 (2009).
[CrossRef] [PubMed]

You, J.

G. Han, Z. Liang, and J. You, “A fast ray-tracing technique for TCT and ECT studies,” in Proceeding of IEEE NSS-MIC, (Seattle, WA, 1999), pp. 1515–1518.

Yu, D. F.

D. F. Yu and J. A. Fessler, “Edge-preserving tomographic reconstruction with nonlocal regularization,” IEEE Trans. Med. Imaging 21, 159–173 (2002).
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Yu, G.

N. Liu, Y. Gao, Z. Bian, J. Huang, W. Chen, G. Yu, Z. Liang, and J. Ma, “Sparse-view x-ray CT reconstruction via total generalized variation regularization,” submitted to Phys. Med. Biol. (PMB-100428), in press.

Yu, L.

Z. Li, L. Yu, J. D. Trzasko, D. S. Lake, D. J. Blezek, J. G. Fletcher, C. H. McCollough, and A. Manduca, “Adaptive nonlocal means filtering based on local noise level for CT denoising,” Med. Phys. 41, 011908 (2013).
[CrossRef]

L. Yu, “Radiation dose reduction in computed tomography: techniques and future perspective,” Imaging Med. 1, 65–84 (2009).
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Zhang, G.

J. Wang, H. Lu, Z. Liang, D. Eremina, G. Zhang, S. Wang, J. Chen, and J. Manzione, “An experimental study on the noise properties of X-ray CT sinogram data in radon space,” Phys. Med. Biol. 53, 3327–3341 (2008).
[CrossRef] [PubMed]

Zhang, H.

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
[CrossRef]

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
[CrossRef] [PubMed]

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57, 7519–7542 (2012).
[CrossRef] [PubMed]

J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, and W. Chen, “Low-dose computed tomography image restoration using previous normal-dose scan,” Med. Phys. 38, 5714–5731 (2011).
[CrossRef]

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

Zhang, Y.

Z. Bian, J. Ma, L. Tian, J. Huang, H. Zhang, Y. Zhang, W. Chen, and Z. Liang, “Penalized weighted alpha-divergence approach to sinogram restoration for low-dose X-ray computed tomography,” in Proceeding of IEEE NSS-MIC, (2012), pp. 3675–3678.

Comput. Med. Imaging Graph. (1)

Z. Bian, J. Ma, J. Huang, H. Zhang, S. Niu, Q. Feng, Z. Liang, and W. Chen, “SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography,” Comput. Med. Imaging Graph. 37, 293–303 (2013).
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J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray CT,” IEEE Trans. Med. Imaging 25, 1272–1283 (2006).
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IEEE Trans. Med. Imaging, (1)

Y. Liu, Z. Liang, J. Ma, H. Lu, K. Wang, H. Zhang, and W. Moore, “Total variation-stokes strategy for sparse-view X-ray CT image reconstruction,” IEEE Trans. Med. Imaging, 33, 749–763 (2014).
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IEEE Trans. Nucl. Sci. (1)

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 2505–2513 (2004).
[CrossRef]

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Figures (11)

Fig. 1
Fig. 1

The physical phantoms used in the studies. (a) illustration of an anthropomorphic torso phantom; (b) the truth image reconstructed by the FBP method with ramp filter from the averaged sinogram data of 150 repeatedly measured samples at 100 mAs, 120 kVp.

Fig. 2
Fig. 2

The images reconstructed by the FBP, ASR-FBP, TV-POCS, and ASR-TV-POCS methods from 116, 145, 290, 580, and 1160-projection views at 17 mAs.

Fig. 3
Fig. 3

The images reconstructed by the FBP, ASR-FBP, TV-POCS, and ASR-TV-POCS methods from 116, 145, 290, 580, and 1160-projection views at 40 mAs.

Fig. 4
Fig. 4

The images reconstructed by the FBP, ASR-FBP, TV-POCS, and ASR-TV-POCS methods from 116, 145, 290, 580, and 1160-projection views at 100 mAs.

Fig. 5
Fig. 5

Zoomed details of the ROI in Fig. 2.

Fig. 6
Fig. 6

Vertical profiles across the 245th to 285th rows at the 468th column from the results reconstructed by TV-POCS and ASR-TV-POCS methods at 17 mAs from the projection views of (a) 116, (b) 145, (c) 290, (d) 580, and (e) 1160.

Fig. 7
Fig. 7

Vertical profiles across the 245th to 285th rows at the 468th column from the results reconstructed by TV-POCS and ASR-TV-POCS methods at 40 mAs from the projection views of (a) 116, (b) 145, (c) 290, (d) 580, and (e) 1160.

Fig. 8
Fig. 8

The UQI values of results by the FBP, ASR-FBP, TV-POCS, and ASR-TV-POCS methods from 1160, 580, 290, 145, and 116-projection views at (a) 17mAs, (b) 40mAs, (c) 60mAs, (d) 80mAs, and (e) 100 mAs.

Fig. 9
Fig. 9

MTF curves from the results reconstructed by TV-POCS and ASR-TV-POCS methods at 17 mAs from the projection views of (a) 116, (b) 145, (c) 290, (d) 580, and (e) 1160.

Fig. 10
Fig. 10

MTF curves from the results reconstructed by TV-POCS and ASR-TV-POCS methods at 40 mAs from the projection views of (a) 116, (b) 145, (c) 290, (d) 580, and (e) 1160.

Fig. 11
Fig. 11

NMSE versus iteration step for TV-POCS and ASR-TV-POCS methods in the case of 17 mAs with the projection views of (a) 116, (b) 145, (c) 290, (d) 580, and (e) 1160.

Tables (5)

Tables Icon

Table 1 Lin’s concordance correlation (CC) coefficient between the profiles from the true image and those from the image reconstructed by the TV-POCS and ASR-TV-POCS methods at 17 mAs.

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Table 2 Lin’s concordance correlation (CC) coefficient between the profiles from the true image and those from the image reconstructed by the TV-POCS and ASR-TV-POCS methods at 40 mAs.

Tables Icon

Table 3 PSNR (dB) measures of the results reconstructed by the four different methods.

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Table 4 NMSE (10−3) measures of the results reconstructed by the four different methods.

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Table 5 CNR measures of the results reconstructed by the four different methods.

Equations (19)

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min μ 0 μ TV subject to | y A μ | ε
μ TV = s , t ( μ s , t μ s 1 , t ) 2 + ( μ s , t μ s , t 1 ) 2
Φ ( p ) = ( y p ) T Σ 1 ( y p ) + β R ( p )
σ i 2 = 1 I i 0 exp ( p ¯ i ) ( 1 + 1 I i 0 exp ( p ¯ i ) ( σ e 2 1.25 ) )
R ( p ) = 1 2 i ( p i Median ( p ( N i ) ) ) 2
p i m + 1 = y i + β σ i 2 Median ( p m ( N i ) ) 1 + β σ i 2
y ˜ i = w i y i + ( 1 w i ) p i
min μ 0 μ TV subject to | y ˜ A μ | ε .
μ j k + 1 = μ j k + ω A + , j i = 1 M A i , j A i , + ( y ˜ i y ¯ i ( μ k ) )
A i , + = j = 1 N A i , j for i = 1 , , M
A + , j = i = 1 M A i , j for j = 1 , , N
y ¯ ( μ ) = A μ
c α = d TV d data | d TV | | d data |
PSNR = 10 log 10 ( max 2 ( μ truth ) n ( μ ( n ) μ truth ( n ) ) 2 / ( N 1 ) )
NMSE = n ( μ ( n ) μ truth ( n ) ) 2 n ( μ truth ( n ) ) 2
μ ¯ = 1 Q q = 1 Q μ ( q ) , σ 2 = 1 Q 1 q = 1 Q ( μ ( q ) μ ¯ ) 2
Cov { μ , μ truth } = 1 Q 1 q = 1 Q ( μ truth ( q ) μ ¯ truth ) ( μ ( q ) μ ¯ )
UQI = 4 Cov { μ , μ truth } σ 2 + σ truth 2 μ ¯ μ ¯ truth μ ¯ 2 + μ ¯ truth 2 .
CNR = | μ ¯ ROI μ ¯ BG | / σ ROI 2 + σ BG 2

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