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Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings.
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2012
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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. 677–88, Mar. 2012
2012
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L. Pfister and Y. Bresler, “Model-based iterative tomographic reconstruction with adaptive sparsifying transforms,” in Proc. SPIE 9020 Computational Imaging XII , 2014, p. 90200H
2014
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2014
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I. Y. Chun, H. Lim, Z. Huang, and J. A. Fessler, “Fast and convergent iterative image recovery using trained convolutional neural networks,” in Allerton Conf. on Comm., Control, and Computing , 2018, pp. 155–9, invited
2018
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Y. Kim, H. Kudo, K. Chigita, and S. Lian, “Image reconstruction in sparse-view CT using improved nonlocal total variation regularization,” in Developments in X-Ray Tomography XII , vol. 11113, International Society for Optics and Photonics. SPIE, 2019, pp. 329–337
2019
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2019
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H. D. Sarkissian, F. Lucka, M. . Eijnatten, G. Colacicco, S. B. Coban, and K. J. Batenburg, “A cone-beam X-ray computed tomography data collection designed for machine learning,” Sci. Data , vol. 6, p. 215, 2019
2019
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J. H. Cho and J. A. Fessler, “Regularization designs for uniform spatial resolution and noise properties in statistical image reconstruction for 3D X-ray CT,” IEEE Trans. Med. Imag. , vol. 34, no. 2, pp. 678–89, Feb. 2015
2015
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S. Ahn, S. G. Ross, E. Asma, J. Miao, X. Jin, L. Cheng, S. D. Wollenweber, and R. M. Manjeshwar, “Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET,” Phys. Med. Biol. , vol. 60, no. 15, pp. 5733–52, Aug. 2015
2015
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H. Nien and J. A. Fessler, “Relaxed linearized algorithm for faster X-ray CT image reconstruction,” in Proc. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , 2015, pp. 260–3. [Online]. Available: http://web.eecs.umich.edu/~fessler/papers/files/proc/15/web/nien-15-rla.pdf
2015
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G. Wang, “A perspective on deep imaging,” IEEE Access , vol. 4, pp. 8914–24, Nov. 2016
2016
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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,” BioMedical Engineering OnLine , vol. 15, no. 66, 2016
2016
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J. A. Fessler, “Michigan image reconstruction toolbox (MIRT) for Matlab,” 2016, available from http://web.eecs.umich.edu/ fessler/irt/irt
2016
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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. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med , 2017, pp. 115–9
2017
Cited alongside, same era.
W. Yu, C. Wang, X. Nie, M. Huang, and L. Wu, “Image reconstruction for few-view computed tomography based on ℓ 0 \ell_{0} sparse regularization,” Procedia Computer Science , vol. 107, pp. 808–813, 2017, advances in Information and Communication Technology: Proceedings of 7th International Congress of Information and Communication Technology (ICICT2017)
2017
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Later among the works it cites.
S. Ravishankar, J. C. Ye, and J. A. Fessler, “Image reconstruction: from sparsity to data-adaptive methods and machine learning,” Proc. IEEE , vol. 108, no. 1, pp. 86–109, Jan. 2020
2020
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C. Xu, B. Yang, F. Guo, W. Zheng, and P. Poignet, “Sparse-view CBCT reconstruction via weighted Schatten p-norm minimization,” Opt. Express , vol. 28, no. 24, pp. 35 469–35 482, Nov 2020
2020
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X. Zhang, Y. Zhou, W. Zhang, J. Sun, and J. Zhao, “Adaptive Prior Patch Size Based Sparse-View CT Reconstruction Algorithm,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) , 2020, pp. 1–4
2020
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G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 1, pp. 39–56, may 2020
2020
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S. Guan, A. A. Khan, S. Sikdar, and P. V. Chitnis, “Limited-view and sparse photoacoustic tomography for neuroimaging with deep learning,” Scientific reports , vol. 10, no. 1, pp. 1–12, 2020
2020
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M. Xiang, “Deep learning-based reconstruction of volumetric CT images of vertebrae from a single view X-ray image,” 2020, theses and Dissertations. 2629. University of Wisconsin Milwaukee. [Online]. Available: https://dc.uwm.edu/etd/2629
2020
Later among the works it cites.
H. Lim, I. Y. Chun, Y. K. Dewaraja, and J. A. Fessler, “Improved low-count quantitative PET reconstruction with a variational neural network,” IEEE Trans. Med. Imag. , vol. 39, no. 11, pp. 3512–22, Nov. 2020
2020
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S. Ye, Z. Li, M. T. McCann, Y. Long, and S. Ravishankar, “Unified Supervised-Unsupervised (SUPER) Learning for X-Ray CT Image Reconstruction,” IEEE Transactions on Medical Imaging , vol. 40, no. 11, pp. 2986–3001, 2021
2021
Later among the works it cites.
A. Lahiri, G. Wang, S. Ravishankar, and J. A. Fessler, “Blind primed supervised (BLIPS) learning for MR image reconstruction,” IEEE Trans. Med. Imag. , vol. 40, no. 11, pp. 3113–24, Nov. 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
G. Corda-D’Incan, J. A. Schnabel, and A. J. Reader, “Memory-efficient training for fully unrolled deep learned PET image reconstruction with iteration-dependent targets,” IEEE Trans. Radiation and Plasma Med. Sci. , 2021
2021
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V. Monga, Y. Li, and Y. C. Eldar, “Algorithm unrolling: interpretable, efficient deep learning for signal and image processing,” IEEE Sig. Proc. Mag. , vol. 38, no. 2, pp. 18–44, Mar. 2021
2021
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A. Ziabari, D. H. Ye, S. Srivastava, K. D. Sauer, J. Thibault, and C. A. Bouman, “2.5D deep learning for CT image reconstruction using A multi-GPU implementation,” in asccs , 2018, pp. 2044–9
2044
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