Fetching the paper…
Reading the bibliography…
A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images.
1902
Earlier work this paper cites.
1907
Earlier work this paper cites.
L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone beam algorithm,” J. Opt. Soc. Am. A , vol. 1, no. 6, pp. 612–9, Jun. 1984
1984
Earlier work this paper cites.
S. D. Booth and J. A. Fessler, “Combined diagonal/Fourier preconditioning methods for image reconstruction in emission tomography,” in Proc. 1995 1995 ICIP , vol. 2, Washington, DC, Oct. 1995, pp. 441–444
1995
Earlier work this paper cites.
J. A. Fessler and W. L. Rogers, “Spatial resolution properties of penalized-likelihood image reconstruction methods: Space-invariant tomographs,” IEEE Trans. Image Process. , vol. 5, no. 9, pp. 1346–1358, Sep. 1996
1996
Earlier work this paper cites.
J. A. Fessler and S. D. Booth, “Conjugate-gradient preconditioning methods for shift-variant PET image reconstruction,” IEEE Trans. Image Process. , vol. 8, no. 5, pp. 688–699, May 1999
1999
Earlier work this paper cites.
H. Erdoğan and J. A. Fessler, “Ordered subsets algorithms for transmission tomography,” Phys. Med. Biol. , vol. 44, no. 11, pp. 2835–2851, Nov. 1999
1999
Earlier work this paper cites.
E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. X-ray Sci. Technol. , vol. 14, no. 2, pp. 119–139, 2006
2006
Earlier work this paper cites.
M. Aharon, M. Elad, and A. Bruckstein, “ K
2006
Earlier work this paper cites.
J. B. Thibault, C. A. Bouman, K. D. Sauer, and J. Hsieh, “A recursive filter for noise reduction in statistical iterative tomographic imaging,” in Proc. SPIE 6065, Computational Imaging IV , vol. 6065, Feb. 2006, p. 60650X
2006
Earlier work this paper cites.
G. H. Chen, J. Tang, and S. Leng, “Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,” Med. Phys. , vol. 35, no. 2, pp. 660–663, Feb. 2008
2008
Earlier work this paper cites.
W. P. Segars, M. Mahesh, T. J. Beck, E. C. Frey, and B. M. W. Tsui, “Realistic CT simulation using the 4D XCAT phantom,” Med. Phys. , vol. 35, no. 8, pp. 3800–3808, Jul. 2008
2008
Earlier work this paper cites.
H. Yu and G. Wang, “Compressed sensing based interior tomography,” Phys. Med. Biol. , vol. 54, no. 9, pp. 2791–2805, May 2009
2009
Earlier work this paper cites.
J. Bian, J. H. Siewerdsen, X. Han, E. Y. Sidky, J. L. Prince, C. A. Pelizzari, and X. Pan, “Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT,” Phys. Med. Biol. , vol. 55, no. 22, p. 6575, Oct. 2010
2010
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. & Trends in Machine Learning , vol. 3, no. 1, pp. 1–122, Jan. 2011
2011
Earlier work this paper cites.
S. Ramani and J. A. Fessler, “A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction,” IEEE Trans. Med. Imag. , vol. 31, no. 3, pp. 677–688, Mar. 2012
2012
Earlier work this paper cites.
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. Imag. , vol. 31, no. 9, pp. 1682–1697, Sep. 2012
2012
Earlier work this paper cites.
I. Y. Chun and T. Talavage, “Efficient compressed sensing statistical X-ray/CT reconstruction from fewer measurements,” in Proc. 12 th 12^{\text{th}} Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , Lake Tahoe, CA, Jun. 2013, pp. 30–33
2013
Earlier work this paper cites.
S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing . New York, NY: Springer, 2013
2013
Cited alongside, same era.
C. Lu, J. Shi, and J. Jia, “Online robust dictionary learning,” in Proc. 2013 2013 IEEE CVPR , Portland, OR, Jun. 2013, pp. 415–422
2013
Cited alongside, same era.
L. Fu, Z. Yu, J.-B. Thibault, B. De Man, M. McGaffin G., and J. A. Fessler, “Space-variant channelized preconditioner design for 3D iterative CT reconstruction,” in Proc. 12 th 12^{\text{th}} Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , Lake Tahoe, CA, Jun. 2013, pp. 205–208
2013
Cited alongside, same era.
S. Niu, 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,” Phys. Med. Biol. , vol. 59, no. 12, p. 2997, May 2014
2014
Cited alongside, same era.
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. , vol. 26, no. 9, pp. 4509–4522, Sep. 2017
2017
Closest in time.
D. Wu, K. Kim, G. E. Fakhri, and Q. Li, “Iterative low-dose CT reconstruction with priors trained by artificial neural network,” IEEE Trans. Med. Imag. , vol. 36, no. 12, pp. 2479–2486, Dec. 2017
2017
Closest in time.
——, “Convergent convolutional dictionary learning using adaptive contrast enhancement (CDL-ACE): Application of CDL to image denoising,” in Proc. 12 th 12^{\textmd{th}} Sampling Theory and Appl. (SampTA) , Tallinn, Estonia, Jul. 2017, pp. 460–464
2017
Closest in time.
X. Zheng, S. Ravishankar, Y. Long, and J. A. Fessler, “Union of learned sparsifying transforms based low-dose 3D CT image reconstruction,” in Proc. 14 th 14^{\text{th}} Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , Xi’an, China, Jun. 2017, pp. 69–72
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J.-F. Cai, H. Ji, Z. Shen, and G.-B. Ye, “Data-driven tight frame construction and image denoising,” Appl. Comput. Harmon. A. , vol. 37, no. 1, pp. 89–105, Oct. 2014
2014
Cited alongside, same era.
L. Pfister and Y. Bresler, “Model-based iterative tomographic reconstruction with adaptive sparsifying transforms,” in Proc. SPIE , vol. 9020, 2014, pp. 90 200H–1–90 200H–11
2014
Cited alongside, same era.
B. Adcock, A. C. Hansen, and B. Roman, “The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing,” in Compressed Sensing and its Applications , ser. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham, 2015, pp. 143–167
2015
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “ ℓ 0 \ell_{0} sparsifying transform learning with efficient optimal updates and convergence guarantees,” IEEE Trans. Signal Process. , vol. 63, no. 9, pp. 2389–2404, May 2015
2015
Cited alongside, same era.
W. Jiang, F. Nie, and H. Huang, “Robust dictionary learning with capped ℓ 1 \ell_{1} -norm,” in Proc. 2015 2015 IJCAI , Buenos Aires, Argentina, Jul. 2015, pp. 3590–3596
2015
Cited alongside, same era.
J. H. Cho and J. A. Fessler, “Regularization designs for uniform spatial resolution and noise properties in statistical image reconstruction for 3-D X-ray CT,” IEEE Trans. Med. Imag. , vol. 2, no. 34, pp. 678–689, Feb. 2015
2015
Cited alongside, same era.
C. Zhang, T. Zhang, M. Li, C. Peng, Z. Liu, and J. Zheng, “Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares,” Biomed. Eng. OnLine , vol. 15, no. 1, p. 66, Jun. 2016
2016
Cited alongside, same era.
X. Zheng, Z. Lu, S. Ravishankar, Y. Long, and J. A. Fessler, “Low dose CT image reconstruction with learned sparsifying transform,” in Proc. 2016 2016 IEEE IVMSP , Bordeaux, France, Jul. 2016, pp. 1–5
2016
Cited alongside, same era.
Closest in time.
I. Y. Chun, X. Zheng, Y. Long, and J. A. Fessler, “Sparse-view X-ray CT reconstruction using ℓ 1 \ell_{1} regularization with learned sparsifying transform,” in Proc. 14 th 14^{\text{th}} Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , Xi’an, China, Jun. 2017, pp. 115–119
2017
Closest in time.
L. Fu, J. A. Fessler, P. E. Kinahan, and B. De Man, “Combining non-diagonal preconditioning and ordered-subsets for iterative CT reconstruction,” in Proc. 14 th 14^{\text{th}} Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , Xi’an, China, Jun. 2017, pp. 760–766
2017
Closest in time.
J. Ye, Y. Han, and E. Cha, “Deep convolutional framelets: A general deep learning framework for inverse problems,” SIAM J. Imaging Sci. , vol. 11, no. 2, pp. 991–1048, Apr. 2018
2018
Closest in time.
H. Chen, Y. Zhang, W. Zhang, H. Sun, P. Liao, K. He, J. Zhou, and G. Wang, “LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT,” IEEE Trans. Med. Imag. , vol. 37, no. 6, pp. 1333–1347, Jun. 2018
2018
Closest in time.
I. Y. Chun and J. A. Fessler, “Deep BCD-net using identical encoding-decoding CNN structures for iterative image recovery,” in Proc. IEEE IVMSP Workshop , Zagori, Greece, Jun. 2018, pp. 1–5
2018
Closest in time.
I. Y. Chun, H. Lim, Z. Huang, and J. A. Fessler, “Fast and convergent iterative signal recovery using trained convolutional neural networkss,” in Proc. Allerton Conf. on Commun., Control, and Comput. , Allerton, IL, Oct. 2018, pp. 155–159
2018
Closest in time.
J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila, “Noise2Noise: learning image restoration without clean data,” in Proc. Intl. Conf. Mach. Learn , 2018, pp. 2971–2980
2018
Closest in time.
D. Pelt, K. Batenburg, and J. Sethian, “Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks,” Journal of Imaging , vol. 4, no. 11, p. 128, 2018
2018
Closest in time.
I. Y. Chun and J. A. Fessler, “Convolutional dictionary learning: acceleration and convergence,” IEEE Trans. Im. Proc. , vol. 27, no. 4, pp. 1697–712, Apr. 2018
2018
Closest in time.
——, “PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction,” IEEE Trans. Med. Imag. , vol. 37, no. 6, pp. 1498–1510, Jun. 2018
2018
Closest in time.
I. Y. Chun and J. A. Fessler, “Convolutional analysis operator learning: Application to sparse-view CT,” in Proc. Asilomar Conf. on Signals, Syst., and Comput. , Pacific Grove, CA, Oct. 2018, pp. 1631–1635
2018
Closest in time.
N. Yuan, J. Zhou, and J. Qi, “Low-dose CT image denoising without high-dose reference images,” in Proc. 15 th 15^{\text{th}} Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , Philadelphia, United States, Jun. 2019, p. 110721C
2019
Closest in time.
2019
Closest in time.
X. Zheng, I. Y. Chun, Z. Li, Y. Long, and J. A. Fessler, “Sparse-view X-ray CT reconstruction using ℓ 1 \ell_{1} prior with learned transform,” IEEE Trans. Computational Imaging , 2019, submitted
2019
Closest in time.