Abstract

In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.

© 2017 Optical Society of America

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References

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  1. A. Berrington de González and S. Darby, “Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries,” Lancet 363(9406), 345–351 (2004).
    [Crossref] [PubMed]
  2. D. J. Brenner and E. J. Hall, “Computed tomography- An increasing source of radiation exposure,” N. Engl. J. Med. 357(22), 2277–2284 (2007).
    [Crossref] [PubMed]
  3. M. Balda, J. Hornegger, and B. Heismann, “Ray contribution masks for structure adaptive sinogram filtering,” IEEE Trans. Med. Imaging 30(5), 1116–1128 (2011).
    [PubMed]
  4. A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
    [Crossref] [PubMed]
  5. 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(5), 2505–2513 (2004).
    [Crossref]
  6. J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
    [Crossref] [PubMed]
  7. S. Tang and X. Tang, “Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain,” Med. Phys. 39(9), 5498–5512 (2012).
    [Crossref] [PubMed]
  8. E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).
    [Crossref] [PubMed]
  9. Y. Zhang, W. Zhang, Y. Lei, and J. Zhou, “Few-view image reconstruction with fractional-order total variation,” J. Opt. Soc. Am. A 31(5), 981–995 (2014).
    [Crossref] [PubMed]
  10. Y. Zhang, Y. Wang, W. Zhang, F. Lin, Y. Pu, and J. Zhou, “Statistical iterative reconstruction using adaptive fractional order regularization,” Biomed. Opt. Express 7(3), 1015–1029 (2016).
    [Crossref] [PubMed]
  11. Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
    [Crossref]
  12. Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
    [Crossref] [PubMed]
  13. 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(22), 7519–7542 (2012).
    [Crossref] [PubMed]
  14. Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
    [Crossref]
  15. Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
    [Crossref] [PubMed]
  16. J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
    [Crossref] [PubMed]
  17. Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
    [Crossref] [PubMed]
  18. 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(10), 5713–5731 (2011).
    [Crossref] [PubMed]
  19. 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(1), 011908 (2014).
    [Crossref] [PubMed]
  20. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
    [Crossref]
  21. Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
    [Crossref] [PubMed]
  22. P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
    [Crossref] [PubMed]
  23. K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
    [Crossref] [PubMed]
  24. D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
    [Crossref]
  25. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
    [Crossref] [PubMed]
  26. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
    [Crossref] [PubMed]
  27. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
    [Crossref] [PubMed]
  28. V. Jain and H. Seung, “Natural image denoising with convolutional networks,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2008), pp.769–776.
  29. C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
    [Crossref] [PubMed]
  30. L. Xu, J. S. J. Ren, C. Liu, and J. Jia, “Deep convolutional neural network for image deconvolution,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2014), pp. 1790–1798.
  31. J. Xie, L. Xun, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2012), pp. 350–352.
  32. H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), pp. 2392–2399.
    [Crossref]
  33. R. Wang and D. Tao, “Non-local auto-encoder with collaborative stabilization for image restoration,” IEEE Trans. Image Process. 25(5), 2117–2129 (2016).
    [Crossref] [PubMed]
  34. F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2013), pp. 1493–1501.
  35. S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
    [PubMed]
  36. K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
    [Crossref] [PubMed]
  37. M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
    [Crossref] [PubMed]
  38. J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
    [Crossref] [PubMed]
  39. K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
    [Crossref] [PubMed]
  40. H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
    [Crossref] [PubMed]
  41. S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
    [Crossref]
  42. H. Zhang, L. Li, K. Qiao, L. Wang, B. Yan, L. Li, and G. Hu, “Image predication for limited-angle tomography via deep learning with convolutional neural network,” arXiv:1607.08707 (2016).
  43. G. Wang, “A perspective on deep imaging,” IEEE Access. in press., doi:.
    [Crossref]
  44. E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,” arXiv:1610.09736 (2016).
  45. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
    [Crossref]
  46. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(12), 3736–3745 (2006).
    [Crossref] [PubMed]
  47. V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. Int. Conf. Mach. Learn. (IEEE, 2010), pp. 807–814.
  48. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
    [Crossref]
  49. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
    [Crossref] [PubMed]
  50. R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12(2), 252–255 (1985).
    [Crossref] [PubMed]
  51. N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted Boltzmann machines,” In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing. (IEEE, 2011), 5884–5887.
    [Crossref]
  52. W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
    [Crossref] [PubMed]
  53. J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

2016 (8)

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

R. Wang and D. Tao, “Non-local auto-encoder with collaborative stabilization for image restoration,” IEEE Trans. Image Process. 25(5), 2117–2129 (2016).
[Crossref] [PubMed]

Y. Zhang, Y. Wang, W. Zhang, F. Lin, Y. Pu, and J. Zhou, “Statistical iterative reconstruction using adaptive fractional order regularization,” Biomed. Opt. Express 7(3), 1015–1029 (2016).
[Crossref] [PubMed]

2015 (1)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

2014 (5)

K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
[Crossref] [PubMed]

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[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(1), 011908 (2014).
[Crossref] [PubMed]

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
[Crossref] [PubMed]

Y. Zhang, W. Zhang, Y. Lei, and J. Zhou, “Few-view image reconstruction with fractional-order total variation,” J. Opt. Soc. Am. A 31(5), 981–995 (2014).
[Crossref] [PubMed]

2013 (6)

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
[PubMed]

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

2012 (4)

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(22), 7519–7542 (2012).
[Crossref] [PubMed]

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

S. Tang and X. Tang, “Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain,” Med. Phys. 39(9), 5498–5512 (2012).
[Crossref] [PubMed]

2011 (2)

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(10), 5713–5731 (2011).
[Crossref] [PubMed]

M. Balda, J. Hornegger, and B. Heismann, “Ray contribution masks for structure adaptive sinogram filtering,” IEEE Trans. Med. Imaging 30(5), 1116–1128 (2011).
[PubMed]

2010 (1)

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

2009 (2)

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

2008 (2)

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).
[Crossref] [PubMed]

J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
[Crossref] [PubMed]

2007 (1)

D. J. Brenner and E. J. Hall, “Computed tomography- An increasing source of radiation exposure,” N. Engl. J. Med. 357(22), 2277–2284 (2007).
[Crossref] [PubMed]

2006 (5)

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(12), 3736–3745 (2006).
[Crossref] [PubMed]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

2004 (3)

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(5), 2505–2513 (2004).
[Crossref]

A. Berrington de González and S. Darby, “Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries,” Lancet 363(9406), 345–351 (2004).
[Crossref] [PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

1998 (1)

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

1985 (1)

R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12(2), 252–255 (1985).
[Crossref] [PubMed]

Aharon, M.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(12), 3736–3745 (2006).
[Crossref] [PubMed]

Ahmed Raza, S. E.

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Balda, M.

M. Balda, J. Hornegger, and B. Heismann, “Ray contribution masks for structure adaptive sinogram filtering,” IEEE Trans. Med. Imaging 30(5), 1116–1128 (2011).
[PubMed]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Berman, D. S.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Berrington de González, A.

A. Berrington de González and S. Darby, “Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries,” Lancet 363(9406), 345–351 (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(1), 011908 (2014).
[Crossref] [PubMed]

Bottou, L.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Brenner, D. J.

D. J. Brenner and E. J. Hall, “Computed tomography- An increasing source of radiation exposure,” N. Engl. J. Med. 357(22), 2277–2284 (2007).
[Crossref] [PubMed]

Bruckstein, A.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

Burger, H. C.

H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), pp. 2392–2399.
[Crossref]

Cai, J.-F.

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

Candès, E. J.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Caoili, E. M.

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

Cha, K. H.

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

Chan, H.-P.

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

Chen, H.

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

Chen, N.

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

Chen, W.

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(22), 7519–7542 (2012).
[Crossref] [PubMed]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (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(10), 5713–5731 (2011).
[Crossref] [PubMed]

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Chen, Y.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Coatrieux, J.-L.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Cohan, R. H.

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

Collins, D. J.

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

Cong, W.

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

Cree,

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Darby, S.

A. Berrington de González and S. Darby, “Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries,” Lancet 363(9406), 345–351 (2004).
[Crossref] [PubMed]

Dey, D.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Dong, C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

Doran, S. J.

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

Duan, L.

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
[Crossref] [PubMed]

Elad, M.

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(12), 3736–3745 (2006).
[Crossref] [PubMed]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

Feng, D.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Feng, Q.

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(22), 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(10), 5713–5731 (2011).
[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(1), 011908 (2014).
[Crossref] [PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

Fumene Feruglio, P.

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

Gao, D.

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Gao, H.

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

Gao, Y.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
[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(22), 7519–7542 (2012).
[Crossref] [PubMed]

Gilmore, H.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

Gou, S.

K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
[Crossref] [PubMed]

Gros, J.

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

Hadjiiski, L.

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

Haffner, P.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Hall, E. J.

D. J. Brenner and E. J. Hall, “Computed tomography- An increasing source of radiation exposure,” N. Engl. J. Med. 357(22), 2277–2284 (2007).
[Crossref] [PubMed]

Harmeling, S.

H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), pp. 2392–2399.
[Crossref]

He, K.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

Heismann, B.

M. Balda, J. Hornegger, and B. Heismann, “Ray contribution masks for structure adaptive sinogram filtering,” IEEE Trans. Med. Imaging 30(5), 1116–1128 (2011).
[PubMed]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Hinton, G. E.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. Int. Conf. Mach. Learn. (IEEE, 2010), pp. 807–814.

N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted Boltzmann machines,” In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing. (IEEE, 2011), 5884–5887.
[Crossref]

Holland, R. R.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Hornegger, J.

M. Balda, J. Hornegger, and B. Heismann, “Ray contribution masks for structure adaptive sinogram filtering,” IEEE Trans. Med. Imaging 30(5), 1116–1128 (2011).
[PubMed]

Hsieh, J.

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[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(5), 2505–2513 (2004).
[Crossref]

Hu, Y.

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Huang, J.

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(22), 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(10), 5713–5731 (2011).
[Crossref] [PubMed]

Igel, C. M.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Jaitly, N.

N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted Boltzmann machines,” In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing. (IEEE, 2011), 5884–5887.
[Crossref]

Jia, X.

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

Jiang, S. B.

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

Kallenberg, M.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Kang, D.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Karssemeijer,

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Khaylova, N.

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

Kofler, J. M.

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

Kuo, C.-C. J.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

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(1), 011908 (2014).
[Crossref] [PubMed]

Leach, M. O.

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Lei, Y.

Li, T.

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(5), 2505–2513 (2004).
[Crossref]

Li, T.-Y.

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

Li, W.

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
[Crossref] [PubMed]

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(5), 2505–2513 (2004).
[Crossref]

Li, Y.

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

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(1), 011908 (2014).
[Crossref] [PubMed]

Liang, D.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Liang, F.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Liang, Z.

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(22), 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(10), 5713–5731 (2011).
[Crossref] [PubMed]

J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
[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(5), 2505–2513 (2004).
[Crossref]

Liao, S.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
[PubMed]

Lillholm, M.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Lin, F.

Lin, Y.

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Liu, Q.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

Loy, C. C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

Lu, H.

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(10), 5713–5731 (2011).
[Crossref] [PubMed]

J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
[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(5), 2505–2513 (2004).
[Crossref]

Luo, L.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Ma, J.

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(22), 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(10), 5713–5731 (2011).
[Crossref] [PubMed]

Madabhushi, A.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

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(1), 011908 (2014).
[Crossref] [PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[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(1), 011908 (2014).
[Crossref] [PubMed]

McCollough, C. M.

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

Mou, X.

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

Nair, V.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. Int. Conf. Mach. Learn. (IEEE, 2010), pp. 807–814.

Nakazato, R.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Ng, A. Y.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Nie, C.

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Nielsen, M.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Orton, M. R.

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

Osindero, S.

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

Oto, A.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
[PubMed]

Pan, X.

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).
[Crossref] [PubMed]

Peng, X.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Pengfei Diao, C.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Perkins, H.

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

Petersen, K.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Pu, Y.

Qi, S. X.

K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
[Crossref] [PubMed]

Rajpoot, N. M.

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Romberg, J.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Salakhutdinov, R. R.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

Samala, R. K.

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

Sbarbati, A.

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

Schuler, C. J.

H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), pp. 2392–2399.
[Crossref]

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Shen, D.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
[PubMed]

Shen, Z.

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

Sheng, K.

K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
[Crossref] [PubMed]

Shi, L.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Shin, H. C.

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

Shu, H.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Siddon, R. L.

R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12(2), 252–255 (1985).
[Crossref] [PubMed]

Sidky, E. Y.

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).
[Crossref] [PubMed]

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Sirinukunwattana, K.

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Slomka, P.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Snead, I. A.

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Su, Z.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Tang, J.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

Tang, S.

S. Tang and X. Tang, “Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain,” Med. Phys. 39(9), 5498–5512 (2012).
[Crossref] [PubMed]

Tang, X.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

S. Tang and X. Tang, “Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain,” Med. Phys. 39(9), 5498–5512 (2012).
[Crossref] [PubMed]

Tao, D.

R. Wang and D. Tao, “Non-local auto-encoder with collaborative stabilization for image restoration,” IEEE Trans. Image Process. 25(5), 2117–2129 (2016).
[Crossref] [PubMed]

Tao, T.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Teh, Y.-W.

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

Toumoulin, C.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[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(1), 011908 (2014).
[Crossref] [PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

Tsang, I. W.

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
[Crossref] [PubMed]

Vachon, K.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Vinegoni, C.

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

Wang, G.

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

G. Wang, “A perspective on deep imaging,” IEEE Access. in press., doi:.
[Crossref]

Wang, J.

J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
[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(5), 2505–2513 (2004).
[Crossref]

Wang, R.

R. Wang and D. Tao, “Non-local auto-encoder with collaborative stabilization for image restoration,” IEEE Trans. Image Process. 25(5), 2117–2129 (2016).
[Crossref] [PubMed]

Wang, S.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Wang, Y.

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Weissleder, R.

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

Wen, J.

J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
[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(5), 2505–2513 (2004).
[Crossref]

Winkel, N.

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

Woo, J.

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Wu, J.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
[Crossref] [PubMed]

Xi, Y.

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

Xiang, L.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

Xu, D.

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
[Crossref] [PubMed]

Xu, J.

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

Xu, Q.

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

Yang, G.

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Yang, M.-L.

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

Yang, Q.

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

Yang, Z.

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Yee-Wah Tsang, D. R. J.

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Yin, X.

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

Ying, L.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Yu, H.

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

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(1), 011908 (2014).
[Crossref] [PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

Zhang, B.

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

Zhang, H.

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(22), 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(10), 5713–5731 (2011).
[Crossref] [PubMed]

Zhang, L.

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

Zhang, W.

Zhang, W.-H.

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

Zhang, Y.

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

Y. Zhang, Y. Wang, W. Zhang, F. Lin, Y. Pu, and J. Zhou, “Statistical iterative reconstruction using adaptive fractional order regularization,” Biomed. Opt. Express 7(3), 1015–1029 (2016).
[Crossref] [PubMed]

Y. Zhang, W. Zhang, Y. Lei, and J. Zhou, “Few-view image reconstruction with fractional-order total variation,” J. Opt. Soc. Am. A 31(5), 981–995 (2014).
[Crossref] [PubMed]

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

Zhao, H.

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

Zhou, J.

Zhou, J.-L.

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

Zhu, J.

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

Zhu, S.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Zhu, Y.

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Comput. Med. Imaging Graph. (1)

Y. Chen, D. Gao, C. Nie, L. Luo, W. Chen, X. Yin, and Y. Lin, “Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior,” Comput. Med. Imaging Graph. 33(7), 495–500 (2009).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (1)

J. Wang, H. Lu, J. Wen, and Z. Liang, “Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography,” IEEE Trans. Biomed. Eng. 55(3), 1022–1031 (2008).
[Crossref] [PubMed]

IEEE Trans. Comput. Imaging (1)

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).
[Crossref]

IEEE Trans. Image Process. (3)

R. Wang and D. Tao, “Non-local auto-encoder with collaborative stabilization for image restoration,” IEEE Trans. Image Process. 25(5), 2117–2129 (2016).
[Crossref] [PubMed]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(12), 3736–3745 (2006).
[Crossref] [PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

IEEE Trans. Inf. Theory (1)

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

IEEE Trans. Med. Imaging (6)

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, C. Pengfei Diao, C. M. Igel, K. Vachon, R. R. Holland, N. Winkel, Karssemeijer, and M. Lillholm, “Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring,” IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016).
[Crossref] [PubMed]

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).
[Crossref] [PubMed]

K. Sirinukunwattana, S. E. Ahmed Raza, D. R. J. Yee-Wah Tsang, I. A. Snead, Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detectionand classification of nuclei in routine colon cancer histology images,” IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016).
[Crossref] [PubMed]

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).
[Crossref] [PubMed]

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).
[Crossref] [PubMed]

M. Balda, J. Hornegger, and B. Heismann, “Ray contribution masks for structure adaptive sinogram filtering,” IEEE Trans. Med. Imaging 30(5), 1116–1128 (2011).
[PubMed]

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(5), 2505–2513 (2004).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data,” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013).
[Crossref] [PubMed]

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).
[Crossref] [PubMed]

IEEE Trans. Signal Process. (1)

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

Int. J. Imaging Syst. Technol. (1)

Y. Zhang, W.-H. Zhang, H. Chen, M.-L. Yang, T.-Y. Li, and J.-L. Zhou, “Few-view image reconstruction combining total variation and a high-order norm,” Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013).
[Crossref]

J. Mach. Learn. Res. (1)

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

J. Opt. Soc. Am. A (1)

Lancet (1)

A. Berrington de González and S. Darby, “Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries,” Lancet 363(9406), 345–351 (2004).
[Crossref] [PubMed]

Med Image Comput Comput Assist Interv (1)

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate MR segmentation,” Med Image Comput Comput Assist Interv 16(2), 254–261 (2013).
[PubMed]

Med. Phys. (7)

K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys. 43(4), 1882–1896 (2016).
[Crossref] [PubMed]

R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12(2), 252–255 (1985).
[Crossref] [PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).
[Crossref] [PubMed]

S. Tang and X. Tang, “Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain,” Med. Phys. 39(9), 5498–5512 (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(10), 5713–5731 (2011).
[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(1), 011908 (2014).
[Crossref] [PubMed]

K. Sheng, S. Gou, J. Wu, and S. X. Qi, “Denoised and texture enhanced MVCT to improve soft tissue conspicuity,” Med. Phys. 41(10), 101916 (2014).
[Crossref] [PubMed]

N. Engl. J. Med. (1)

D. J. Brenner and E. J. Hall, “Computed tomography- An increasing source of radiation exposure,” N. Engl. J. Med. 357(22), 2277–2284 (2007).
[Crossref] [PubMed]

Nature (1)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Neural Comput. (1)

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

Phys. Med. Biol. (5)

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).
[Crossref] [PubMed]

P. Fumene Feruglio, C. Vinegoni, J. Gros, A. Sbarbati, and R. Weissleder, “Block matching 3D random noise filtering for absorption optical projection tomography,” Phys. Med. Biol. 55(18), 5401–5415 (2010).
[Crossref] [PubMed]

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).
[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(22), 7519–7542 (2012).
[Crossref] [PubMed]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).
[Crossref] [PubMed]

Proc. IEEE (1)

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Proc. SPIE (1)

D. Kang, P. Slomka, R. Nakazato, J. Woo, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm,” Proc. SPIE 8669, 86692G (2013).
[Crossref]

Science (1)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

Other (11)

V. Jain and H. Seung, “Natural image denoising with convolutional networks,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2008), pp.769–776.

F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2013), pp. 1493–1501.

L. Xu, J. S. J. Ren, C. Liu, and J. Jia, “Deep convolutional neural network for image deconvolution,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2014), pp. 1790–1798.

J. Xie, L. Xun, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS, 2012), pp. 350–352.

H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), pp. 2392–2399.
[Crossref]

N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted Boltzmann machines,” In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing. (IEEE, 2011), 5884–5887.
[Crossref]

V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. Int. Conf. Mach. Learn. (IEEE, 2010), pp. 807–814.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

H. Zhang, L. Li, K. Qiao, L. Wang, B. Yan, L. Li, and G. Hu, “Image predication for limited-angle tomography via deep learning with convolutional neural network,” arXiv:1607.08707 (2016).

G. Wang, “A perspective on deep imaging,” IEEE Access. in press., doi:.
[Crossref]

E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,” arXiv:1610.09736 (2016).

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Figures (7)

Fig. 1
Fig. 1

Typical CT images used in the training set.

Fig. 2
Fig. 2

Results with a chest image. (a) Original normal-dose image; (b) the low-dose image; (c) the ASD-POCS image; (d) the KSVD image; (e) the BM3D image; (f) the CNN processed low-dose image; and (g)-(l) the zoomed regions within the red box in (a)-(f).

Fig. 3
Fig. 3

Results of an abdomen image. (a) Original normal-dose image; (b) the low-dose image; (c) the ASD-POCS image; (d) the KSVD image; (e) the BM3D image; (f) the CNN processed low-dose image.

Fig. 4
Fig. 4

Results of the abdomen image processed by: (a) CNN200-1; (b) CNN200-4; (c) CNN200-1-DA; (d) CNN200-4-DA; (e) CNN2000-1; (f) CNN2000-4.

Fig. 5
Fig. 5

Zoomed images from Fig. 4. (a) The normal-dose image, (b) low-dose image; the images processed with (c) CNN200-1; (d) CNN200-4; (e) CNN200-1-DA; (f) CNN200-4-DA; (g) CNN2000-1; and (h) CNN2000-4 respectively.

Fig. 6
Fig. 6

Results images from a low-dose sinogram collected in the sheep lung CT. (a) Original normal-dose image; (b) the low-dose images; (c) the ASD-POCS reconstructed image; (d) the KSVD processed low-dose image; (e) the BM3D processed low-dose image; (f) the CNN processed low-dose image.

Fig. 7
Fig. 7

Difference images between the low-dose FBP image and the results using (a) ASD-POCS, (b) KSVD, (c) BM3D, and (d) CNN methods respectively.

Tables (6)

Tables Icon

Table 1 Quantitative Measurements Associated with Different Algorithms for The Image in Fig. 2

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Table 2 Quantitative Measurements Associated with Different Algorithms for The Image in Fig. 3

Tables Icon

Table 3 Quantitative Measurements (Average Values) Associated with Different Algorithms for The Images in Testing Set

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Table 4 Statistical Analysis of Image Quality Scores of Different Algorithms (Mean ± SD).

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Table 5 Quantitative Measurements (Average Values) Associated with Different Algorithms for Various Combinations of Noise Levels.

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Table 6 Quantitative results for the CNN-based method with different sizes of the training set.

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

X=σ(Y)
f= argmin f ||f(X)Y| | 2 2
C 1 (y)=ReLU( W 1 y+ b 1 ),
C 2 (y)=ReLU( W 2 C 1 (y)+ b 2 ),
C(y)= W 3 C 2 (y)+ b 3 ,
L(D;Θ)= 1 N i=1 N x i C( y i ) 2 .