Abstract

Propagation-based X-ray phase-contrast imaging (PBI) is a powerful nondestructive imaging technique that can reveal the internal detailed structures in weakly absorbing samples. Extending PBI to CT (PBCT) enables high-resolution and high-contrast 3D visualization of microvasculature, which can be used for the understanding, diagnosis and therapy of diseases involving vasculopathy, such as cardiovascular disease, stroke and tumor. However, the long scan time for PBCT impedes its wider use in biomedical and preclinical microvascular studies. To address this issue, a novel CT reconstruction algorithm for PBCT is presented that aims at shortening the scan time for microvascular samples by reducing the number of projections while maintaining the high quality of reconstructed images. The proposed algorithm combines the filtered backprojection method into the iterative reconstruction framework, and a weighted guided image filtering approach (WGIF) is utilized to optimize the intermediate reconstructed images. Notably, the homogeneity assumption on the microvasculature sample is adopted as prior knowledge, and therefore, a prior image of microvasculature structures can be acquired by a k-means clustering approach. Then, the prior image is used as the guided image in the WGIF procedure to effectively suppress streaking artifacts and preserve microvasculature structures. To evaluate the effectiveness and capability of the proposed algorithm, simulation experiments on 3D microvasculature numerical phantom and real experiments with CT reconstruction on the microvasculature sample are performed. The results demonstrate that the proposed algorithm can, under noise-free and noisy conditions, significantly reduce the artifacts and effectively preserve the microvasculature structures on the reconstructed images and thus enables it to be used for clear and accurate 3D visualization of microvasculature from few-projection data. Therefore, for 3D visualization of microvasculature, the proposed algorithm can be considered an effective approach for reducing the scan time required by PBCT.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
TV-based conjugate gradient method and discrete L-curve for few-view CT reconstruction of X-ray in vivo data

Xiaoli Yang, Ralf Hofmann, Robin Dapp, Thomas van de Kamp, Tomy dos Santos Rolo, Xianghui Xiao, Julian Moosmann, Jubin Kashef, and Rainer Stotzka
Opt. Express 23(5) 5368-5387 (2015)

Propagation-based phase-contrast tomography of a guinea pig inner ear with cochlear implant using a model-based iterative reconstruction algorithm

Lorenz Hehn, Regine Gradl, Andrej Voss, Benedikt Günther, Martin Dierolf, Christoph Jud, Konstantin Willer, Sebastian Allner, Jörg U. Hammel, Roland Hessler, Kaye S. Morgan, Julia Herzen, Werner Hemmert, and Franz Pfeiffer
Biomed. Opt. Express 9(11) 5330-5339 (2018)

High resolution 3D visualization of the spinal cord in a post-mortem murine model

Inna Bukreeva, Victor Asadchikov, Alexey Buzmakov, Marina Chukalina, Anastasya Ingacheva, Nikolay A. Korolev, Alberto Bravin, Alberto Mittone, Gabriele E. M. Biella, Alejandra Sierra, Francesco Brun, Lorenzo Massimi, Michela Fratini, and Alessia Cedola
Biomed. Opt. Express 11(4) 2235-2253 (2020)

References

  • View by:
  • |
  • |
  • |

  1. P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
    [Crossref]
  2. J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
    [Crossref]
  3. E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
    [Crossref]
  4. B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
    [Crossref]
  5. S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
    [Crossref]
  6. J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
    [Crossref]
  7. B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
    [Crossref]
  8. T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
    [Crossref]
  9. L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
    [Crossref]
  10. Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
    [Crossref]
  11. F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
    [Crossref]
  12. J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
    [Crossref]
  13. J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
    [Crossref]
  14. R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
    [Crossref]
  15. J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
    [Crossref]
  16. S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
    [Crossref]
  17. F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
    [Crossref]
  18. B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
    [Crossref]
  19. H. Analysis, Y. S. Han, J. Yoo, and J. C. Ye, “Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis,” arXiv Prepr. arXiv1611.06391 (2016).
  20. K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
    [Crossref]
  21. Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
    [Crossref]
  22. G. Wang, “A perspective on deep imaging,” IEEE Access. 4, 8914–8924 (2016).
    [Crossref]
  23. E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14(2), 119–139 (2006).
  24. O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
    [Crossref]
  25. K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
    [Crossref]
  26. Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
    [Crossref]
  27. G. 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. 35(2), 660–663 (2008).
    [Crossref]
  28. K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
    [Crossref]
  29. D. Ji, G. Qu, and B. Liu, “Simultaneous algebraic reconstruction technique based on guided image filtering,” Opt. Express 24(14), 15897–15911 (2016).
    [Crossref]
  30. Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
    [Crossref]
  31. M. Etehadtavakol, S. Sadri, and E. Ng, “Application of K-and Fuzzy c-means for color segmentation of thermal infrared breast images,” J. Med. Syst. 34(1), 35–42 (2010).
    [Crossref]
  32. K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
    [Crossref]
  33. T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
    [Crossref]
  34. R. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12(2), 252–255 (1985).
    [Crossref]
  35. G. Zeng, “A filtered backprojection algorithm with characteristics of the iterative landweber algorithm,” Med. Phys. 39(2), 603–607 (2012).
    [Crossref]
  36. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2003)
  37. S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
    [Crossref]
  38. Z. Berit, J. Mead, T. Deck, R. Tiina, G. Clough, R. Boardman, and S. Philipp, “Soft tissue 3D imaging in the lab through optimised propagation-based phase contrast computed tomography,” Opt. Express 25(26), 33451–33468 (2017).
    [Crossref]
  39. Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
    [Crossref]
  40. R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
    [Crossref]
  41. W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
    [Crossref]

2019 (4)

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

2018 (6)

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
[Crossref]

2017 (2)

Z. Berit, J. Mead, T. Deck, R. Tiina, G. Clough, R. Boardman, and S. Philipp, “Soft tissue 3D imaging in the lab through optimised propagation-based phase contrast computed tomography,” Opt. Express 25(26), 33451–33468 (2017).
[Crossref]

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
[Crossref]

2016 (5)

G. Wang, “A perspective on deep imaging,” IEEE Access. 4, 8914–8924 (2016).
[Crossref]

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

D. Ji, G. Qu, and B. Liu, “Simultaneous algebraic reconstruction technique based on guided image filtering,” Opt. Express 24(14), 15897–15911 (2016).
[Crossref]

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

2015 (3)

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

2014 (2)

O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

2013 (3)

J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
[Crossref]

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
[Crossref]

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

2012 (3)

G. Zeng, “A filtered backprojection algorithm with characteristics of the iterative landweber algorithm,” Med. Phys. 39(2), 603–607 (2012).
[Crossref]

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

2010 (3)

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

M. Etehadtavakol, S. Sadri, and E. Ng, “Application of K-and Fuzzy c-means for color segmentation of thermal infrared breast images,” J. Med. Syst. 34(1), 35–42 (2010).
[Crossref]

2009 (1)

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

2008 (1)

G. 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. 35(2), 660–663 (2008).
[Crossref]

2006 (2)

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14(2), 119–139 (2006).

B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
[Crossref]

2004 (1)

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

1999 (1)

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

1996 (1)

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

1985 (1)

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

Abel, R.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Albon, J.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Analysis, H.

H. Analysis, Y. S. Han, J. Yoo, and J. C. Ye, “Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis,” arXiv Prepr. arXiv1611.06391 (2016).

Andrew, J. B.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Asa, H. B.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Atwood, R.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Barnea, Z.

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

Bartscher, M.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Beckmann, F.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Berit, Z.

Biermann, T.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Boardman, R.

Bonse, U.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Brombal, L.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Campbell, I.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Cao, G.

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

Cao, Y.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

Chambers, R. C.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Chen, G.

G. 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. 35(2), 660–663 (2008).
[Crossref]

Chen, R.

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

Chen, X.

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Chen, Z.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Christian, D.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Clough, G.

Cong, C.

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Cookson, D.

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

Coudrillier, B.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Darren, T.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

De Coppi, P.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Deck, T.

Diego, D.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Ding, H.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Dong, X.

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

Dono, K.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Dreossi, D.

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

Dullin, C.

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

Endrizzi, M.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Enrico, D.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Etehadtavakol, M.

M. Etehadtavakol, S. Sadri, and E. Ng, “Application of K-and Fuzzy c-means for color segmentation of thermal infrared breast images,” J. Med. Syst. 34(1), 35–42 (2010).
[Crossref]

Ethier, C.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Fabrizio, Z.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Fakhri, G. E.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

Felipe, M. P.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Forty, E. J.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Friedman, J.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2003)

Froustey, E.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
[Crossref]

Fukami, T.

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Fulvia, A.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Geraldes, D.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Gho, S.

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

Gianluca, T.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Giuliana, T.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Gordon, W. B.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Gu, X.

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Gureyev, T.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

Han, Y. S.

H. Analysis, Y. S. Han, J. Yoo, and J. C. Ye, “Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis,” arXiv Prepr. arXiv1611.06391 (2016).

Hanjoong, J.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Hao, N.

O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
[Crossref]

Harry, Q.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Hastie, T.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2003)

He, K.

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
[Crossref]

Heise, K.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Hertz, H.

W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
[Crossref]

Hironobu, N.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Hori, M.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Hu, C.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Hu, D.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Hu, J.

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

Hu, Z.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Hwang, J.

J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
[Crossref]

Hwu, Y.

B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
[Crossref]

Hyodo, K.

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

Jane, F.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Je, J. H.

B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
[Crossref]

Jeremy, E.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Ji, D.

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

D. Ji, G. Qu, and B. Liu, “Simultaneous algebraic reconstruction technique based on guided image filtering,” Opt. Express 24(14), 15897–15911 (2016).
[Crossref]

Jian, J.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Jiang, J.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Jin, K. H.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
[Crossref]

Kallon, G.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Kao, C.-M.

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14(2), 119–139 (2006).

Katalin, P.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Keigo, O.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Keiji, U.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Kilmer, M. E.

O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
[Crossref]

Kim, D.

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

Kim, E.

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

Kim, K.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

Kim, M.

J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
[Crossref]

Kim, S.

J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
[Crossref]

Kobayashi, S.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Kölsch, B.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Lee, J.

J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
[Crossref]

Leng, S.

G. 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. 35(2), 660–663 (2008).
[Crossref]

Li, D.

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

Li, J.

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Li, Q.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

Li, Z.

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

Liang, D.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Liang, X.

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

Lin, X.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Liu, B.

Liu, P.

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Liu, Q.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Liu, X.

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Longo, R.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

Lu, H.

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

Luise, D. F.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Luo, Y.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Lv, W.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Lwin, T. T.

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Mancini, L.

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

Margaritondo, G.

B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
[Crossref]

Marian, C.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Martino, P.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Masato, S.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Matthew, D.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

McCann, M. T.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
[Crossref]

Mead, J.

Menk, R.

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

Michael, S.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Mikkaela, M.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Miller, E. L.

O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
[Crossref]

Mohammadi, S.

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

Momose, A.

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Morito, M.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Mu, Z.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Murakami, T.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Nagano, H.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Nakamori, S.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Nam, Y.

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

Nesterets, Y.

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

Ng, E.

M. Etehadtavakol, S. Sadri, and E. Ng, “Application of K-and Fuzzy c-means for color segmentation of thermal infrared breast images,” J. Med. Syst. 34(1), 35–42 (2010).
[Crossref]

Nugent, K.

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

Olivo, A.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Ouyang, J.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

Paganin, D.

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

Pan, X.

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14(2), 119–139 (2006).

Patrick, B.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Patrycja, B.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Peña, F. M.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Peng, X.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Persson, J.

W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
[Crossref]

Peter, R. B.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Philipp, S.

Qin, L.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Qu, G.

Rachna, P.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Rajewsky, M. F.

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

Raji, Y.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Rakvongthai, Y.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

Reinhard, C.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Rigon, L.

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

Rolf, B.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Sadri, S.

M. Etehadtavakol, S. Sadri, and E. Ng, “Application of K-and Fuzzy c-means for color segmentation of thermal infrared breast images,” J. Med. Syst. 34(1), 35–42 (2010).
[Crossref]

Sandeep, K.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Savvidis, S.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Schürmann, C.

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Semerci, O.

O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
[Crossref]

Serena, P.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Shen, F.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Sheridan, M.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Shi, S.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Siddon, R.

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

Sidky, E. Y.

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14(2), 119–139 (2006).

Silvia, C.

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

Sun, J.

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
[Crossref]

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Sun, M.

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Sunaguchi, N.

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Szekely, L.

W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
[Crossref]

Takao, A.

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Takeda, T.

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Tang, B.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Tang, J.

G. 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. 35(2), 660–663 (2008).
[Crossref]

Tang, X.

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
[Crossref]

Tang, Y.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Tetsuya, Y.

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Thet, T.

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

Tibshirani, R.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2003)

Tiina, R.

Tromba, G.

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

Umeshita, K.

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

Unser, M.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
[Crossref]

Urbani, L.

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Vågberg, W.

W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
[Crossref]

Vo, N.

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

Wang, G.

G. Wang, “A perspective on deep imaging,” IEEE Access. 4, 8914–8924 (2016).
[Crossref]

Wang, J.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Wang, T.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Wang, Y.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Wang, Z.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Weon, B. M.

B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
[Crossref]

Worstell, W.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

Wu, J.

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Wu, P. Z.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Wu, S.

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

Wu, T.

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

Xiao, B.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Xiao, T.

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Xie, Q.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Xie, Y.

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

Xu, L.

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Xuan, R.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Yakov, N.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Yang, G.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Yao, W.

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

Ye, J. C.

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

H. Analysis, Y. S. Han, J. Yoo, and J. C. Ye, “Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis,” arXiv Prepr. arXiv1611.06391 (2016).

Yin, X.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

Yoneyama, A.

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

Yoo, J.

H. Analysis, Y. S. Han, J. Yoo, and J. C. Ye, “Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis,” arXiv Prepr. arXiv1611.06391 (2016).

Zdenka, P.

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

Zeng, G.

G. Zeng, “A filtered backprojection algorithm with characteristics of the iterative landweber algorithm,” Med. Phys. 39(2), 603–607 (2012).
[Crossref]

Zhang, H.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Zhang, J.

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

Zhang, L.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Zhang, M.

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

Zhang, N.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Zhang, Y.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Zhang, Z.

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

Zhao, J.

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

Zhao, Q.

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Zhao, X.

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Zhao, Y.

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

Zheng, H.

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Zheng, J.

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

Zho, S.

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

Zhu, Z.

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

Biophys. J. (1)

F. Beckmann, K. Heise, B. Kölsch, U. Bonse, M. F. Rajewsky, M. Bartscher, and T. Biermann, “Three-dimensional imaging of nerve tissue by X-ray phase-contrast microtomography,” Biophys. J. 76(1), 98–102 (1999).
[Crossref]

IEEE Access. (1)

G. Wang, “A perspective on deep imaging,” IEEE Access. 4, 8914–8924 (2016).
[Crossref]

IEEE Trans. Image. Proces. (2)

Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted Guided Image Filtering,” IEEE Trans. Image. Proces. 24(1), 120–129 (2015).
[Crossref]

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image. Proces. 26(9), 4509–4522 (2017).
[Crossref]

IEEE Trans. Med. Imaging (5)

Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of densenet and deconvolution,” IEEE Trans. Med. Imaging 37(6), 1407–1417 (2018).
[Crossref]

B. Coudrillier, D. Geraldes, N. Vo, R. Atwood, C. Reinhard, I. Campbell, Y. Raji, J. Albon, R. Abel, and C. Ethier, “Phase-contrast micro-computed tomography measurements of the intraocular pressure-induced deformation of the porcine lamina cribrosa,” IEEE Trans. Med. Imaging 35(4), 988–999 (2016).
[Crossref]

B. Patrycja, M. Sheridan, M. Mikkaela, P. Serena, T. Giuliana, D. Christian, Z. Fabrizio, A. Fulvia, D. Diego, F. Jane, P. Zdenka, C. Marian, Q. Harry, D. Matthew, N. Yakov, T. Darren, B. Patrick, and T. Gureyev, “High-Resolution X-ray Phase-Contrast 3D Imaging of Breast Tissue Specimens as a Possible Adjunct to Histopathology,” IEEE Trans. Med. Imaging 37(12), 2642–2650 (2018).
[Crossref]

O. Semerci, N. Hao, M. E. Kilmer, and E. L. Miller, “Tensor-based formulation and nuclear norm regularization for multienergy computed tomography,” IEEE Trans. Med. Imaging 23(4), 1678–1693 (2014).
[Crossref]

K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. E. Fakhri, and Q. Li, “Sparse-View Spectral CT Reconstruction Using SpectralPatch-Based Low-Rank Penalty,” IEEE Trans. Med. Imaging 34(3), 748–760 (2015).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
[Crossref]

Int. J. Nanotechnol. (1)

B. M. Weon, J. H. Je, Y. Hwu, and G. Margaritondo, “Phase contrast X-ray imaging,” Int. J. Nanotechnol. 3(2/3), 280–297 (2006).
[Crossref]

J. Appl. Phys. (1)

T. Gureyev, S. Mohammadi, Y. Nesterets, C. Dullin, and G. Tromba, “Accuracy and precision of reconstruction of complex refractive index in near-field single-distance propagation-based phase-contrast tomography,” J. Appl. Phys. 114(14), 144906 (2013).
[Crossref]

J. Hepatol. (1)

S. Kobayashi, M. Hori, K. Dono, H. Nagano, K. Umeshita, S. Nakamori, S. Masato, O. Keigo, U. Keiji, T. Murakami, N. Hironobu, and M. Morito, “In vivo real-time microangiography of the liver in mice using synchrotron radiation,” J. Hepatol. 40(3), 405–408 (2004).
[Crossref]

J. Mech. Behav. Biomed. Mater. (1)

F. M. Peña, C. Silvia, D. Enrico, J. B. Andrew, P. Rachna, P. Martino, W. B. Gordon, H. B. Asa, and T. Gianluca, “Effect of SR-micro CT radiation on the mechanical integrity of trabecular bone using in situ mechanical testing and digital volume correlation,” J. Mech. Behav. Biomed. Mater. 88, 109–119 (2018).
[Crossref]

J. Med. Syst. (1)

M. Etehadtavakol, S. Sadri, and E. Ng, “Application of K-and Fuzzy c-means for color segmentation of thermal infrared breast images,” J. Med. Syst. 34(1), 35–42 (2010).
[Crossref]

J. Synchrotron Radiat. (6)

Y. Zhao, M. Sun, D. Ji, C. Cong, W. Lv, Q. Zhao, L. Qin, J. Jian, X. Chen, and C. Hu, “An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phasecontrast computed tomography,” J. Synchrotron Radiat. 25(5), 1450–1459 (2018).
[Crossref]

R. Chen, D. Dreossi, L. Mancini, R. Menk, L. Rigon, T. Xiao, and R. Longo, “PITRE: software for phasesensitive X-ray image processing and tomography reconstruction,” J. Synchrotron Radiat. 19(5), 836–845 (2012).
[Crossref]

P. Liu, J. Sun, J. Zhao, X. Liu, X. Gu, J. Li, T. Xiao, and L. Xu, “Microvascular imaging using synchrotron radiation,” J. Synchrotron Radiat. 17(4), 517–521 (2010).
[Crossref]

T. Takeda, A. Yoneyama, J. Wu, T. Thet, A. Momose, and K. Hyodo, “In vivo physiological saline-infused hepatic vessel imaging using a two-crystal-interferometer-based phase-contrast X-ray technique,” J. Synchrotron Radiat. 19(2), 252–256 (2012).
[Crossref]

Y. Cao, X. Yin, J. Zhang, T. Wu, and J. Hu, “Visualization of mouse spinal cord intramedullary arteries using phase-and attenuation-contrast tomographic imaging,” J. Synchrotron Radiat. 23(4), 966–974 (2016).
[Crossref]

S. Shi, H. Zhang, X. Yin, Z. Wang, B. Tang, Y. Luo, H. Ding, Z. Chen, Y. Cao, T. Wang, B. Xiao, and M. Zhang, “3D digital anatomic angioarchitecture of the mouse brain using synchrotron-radiation-based propagation phase-contrast imaging,” J. Synchrotron Radiat. 26(5), 1742–1750 (2019).
[Crossref]

J. XRay Sci. Technol. (2)

E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. XRay Sci. Technol. 14(2), 119–139 (2006).

Z. Hu, Q. Liu, N. Zhang, Y. Zhang, X. Peng, P. Z. Wu, H. Zheng, and D. Liang, “Image reconstruction from few-view CT data by gradient-domain dictionary learning,” J. XRay Sci. Technol. 24(4), 627–638 (2016).
[Crossref]

Kidney Int. (1)

J. Wu, T. Takeda, T. T. Lwin, A. Momose, N. Sunaguchi, T. Fukami, Y. Tetsuya, and A. Takao, “Imaging renal structures by X-ray phase-contrast microtomography,” Kidney Int. 75(9), 945–951 (2009).
[Crossref]

Magn. Reson. Imaging. (1)

S. Gho, Y. Nam, S. Zho, E. Kim, and D. Kim, “Three dimension double inversion recovery gray matter imaging using compressed sensing,” Magn. Reson. Imaging. 28(10), 1395–1402 (2010).
[Crossref]

Med Phys. (1)

G. 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. 35(2), 660–663 (2008).
[Crossref]

Med. Phys. (3)

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

G. Zeng, “A filtered backprojection algorithm with characteristics of the iterative landweber algorithm,” Med. Phys. 39(2), 603–607 (2012).
[Crossref]

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “High-resolution three-dimensional visualization of the rat spinal cord microvasculature by synchrotron radiation micro-CT,” Med. Phys. 41(10), 101904 (2014).
[Crossref]

Microvasc. Res. (1)

R. Xuan, X. Zhao, J. Jian, D. Hu, L. Qin, W. Lv, and C. Hu, “Phase-contrast computed tomography: A correlation study between portal pressure and three dimensional microvasculature of ex vivo liver samples from carbon tetrachloride-induced liver fibrosis in rats,” Microvasc. Res. 125, 103884 (2019).
[Crossref]

Opt. Express (2)

Phys. Rev. Appl. (1)

L. Brombal, G. Kallon, J. Jiang, S. Savvidis, P. De Coppi, L. Urbani, E. J. Forty, R. C. Chambers, R. Longo, A. Olivo, and M. Endrizzi, “Monochromatic propagation-based phase-contrast microscale computed-tomography system with a rotating-anode source,” Phys. Rev. Appl. 11(3), 034004 (2019).
[Crossref]

Phys. Rev. Lett. (1)

K. Nugent, T. Gureyev, D. Cookson, D. Paganin, and Z. Barnea, “Quantitative phase imaging using hard X-rays,” Phys. Rev. Lett. 77(14), 2961–2964 (1996).
[Crossref]

PLoS One (1)

J. Hwang, M. Kim, S. Kim, and J. Lee, “Quantifying morphological parameters of the terminal branching units in a mouse lung by phase contrast synchrotron radiation computed tomography,” PLoS One 8(5), e63552 (2013).
[Crossref]

Sci. Rep. (1)

W. Vågberg, J. Persson, L. Szekely, and H. Hertz, “Cellular-resolution 3D virtual histology of human coronary arteries using X-ray phase tomography,” Sci. Rep. 8(1), 11014 (2018).
[Crossref]

Spinal Cord. (1)

J. Hu, Y. Cao, T. Wu, D. Li, and H. Lu, “3D angioarchitecture changes after spinal cord injury in rats using synchrotron radiation phase-contrast tomography,” Spinal Cord. 53(8), 585–590 (2015).
[Crossref]

Theranostics (2)

J. Wang, X. Lin, Z. Mu, F. Shen, L. Zhang, Q. Xie, Y. Tang, Y. Wang, Z. Zhang, and G. Yang, “Rapamycin Increases Collateral Circulation in Rodent Brain after Focal Ischemia as detected by Multiple Modality Dynamic Imaging,” Theranostics 9(17), 4923–4934 (2019).
[Crossref]

E. Jeremy, P. Katalin, D. F. Luise, M. P. Felipe, B. Rolf, S. Michael, K. Sandeep, J. Hanjoong, C. Schürmann, and R. B. Peter, “3D imaging and quantitative analysis of vascular networks: a comparison of ultramicroscopy and micro-computed tomography,” Theranostics 8(8), 2117–2133 (2018).
[Crossref]

Other (2)

H. Analysis, Y. S. Han, J. Yoo, and J. C. Ye, “Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis,” arXiv Prepr. arXiv1611.06391 (2016).

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2003)

Supplementary Material (2)

NameDescription
» Visualization 1       The 3D reconstruction process for microvasculature in which all slices were reconstructed using the FBP approach from 200 projections
» Visualization 2       The 3D reconstruction process for microvasculature in which all slices were reconstructed using the proposed approach from 200 projections

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (10)

Fig. 1.
Fig. 1. Flowchart of the proposed CT reconstruction algorithm. The symbol + represents the addition operator, the symbol represents the subtraction operator, and the arrows with dotted lines represent iterative processes.
Fig. 2.
Fig. 2. The 3D-MV phantom used for evaluating CT reconstruction algorithms. (a) The surface-rendering image of the 3D-MV phantom for 3D visualization. (b) The compositions in the 3D-MV phantom. The subimage located in the upper left corner of the image (b) is the 24th slice of the 3D-MV phantom, and in this study, it was used as the ground truth for comparing the resultant images from different CT reconstruction algorithms. The red rectangle area (30×50 pixel2) in (b) was the local sample region, and the blue rectangle area (20×30 pixel2) in (b) was the local background region, which was used to compute the SNR, as depicted in Eq. (18).
Fig. 3.
Fig. 3. The surface-rendering images and the 24th slices of the 3D-MV phantom in which all slices were reconstructed using the FBP and the proposed algorithm from the noise-free 340 projections, 68 projections and 34 projections. (a, e, i) are the surface-rendering images of the 3D-MV phantom in which all slices were reconstructed using the FBP method from the noise-free 340 projections, 68 projections and 34 projections, respectively; (b, f, j) are the 24th slices of the 3D-MV phantom in which all slices were reconstructed using the FBP method from the noise-free 340 projections, 68 projections and 34 projections. (c, g, k) are the surface-rendering images of the 3D-MV phantom in which all slices were reconstructed using the proposed method from the noise-free 340 projections, 68 projections and 34 projections, respectively; (d, h, l) are the 24th slices of the 3D-MV phantom in which all slices were reconstructed using the proposed method from the noise-free 340 projections, 68 projections and 34 projections, respectively. The pixel values of the above grayscale images were normalized to the range [0, 255]. The display window was [0, 215]. The scale bar represented 100 µm.
Fig. 4.
Fig. 4. The surface-rendering images and the 24th slices of the 3D-MV phantom where all slices were reconstructed using the FBP and the proposed algorithm under the noisy 340 projections, 68 projections and 34 projections. (a, e, i) are the surface-rendering images of the 3D-MV phantom where all slices were reconstructed using the FBP method under the noisy 340 projections, 68 projections and 34 projections, respectively; (b, f, j) are the 24th slices of the 3D-MV phantom where all slices reconstructed using the FBP method under the noisy 340 projections, 68 projections and 34 projections. (c, g, k) are the surface-rendering images of the 3D-MV phantom where all slices were reconstructed using the proposed method under the noisy 340 projections, 68 projections and 34 projections, respectively; (d, h, l) are the 24th slices of the 3D-MV phantom where all slices were reconstructed using the proposed method under the noisy 340 projections, 68 projections and 34 projections, respectively. The pixel values of the above grayscale images were normalized to the range [0, 255]. The display window was [0, 215]. The scale bar represented 100 µm.
Fig. 5.
Fig. 5. The quantitative metrics for the 3D-MV phantom where all slices were reconstructed using the FBP and the proposed algorithm under different few-projection conditions. (a, b) are the MSSIMs calculated under the noise-free and noisy few-projection conditions, respectively. (c, d) show the SNRs calculated under the noise-free and noisy few-projection conditions, respectively. (e, f) display some relative loss rates computed under the noise-free and noisy few-projection conditions, respectively.
Fig. 6.
Fig. 6. Comparisons of the different reconstructed images based on α values ranging from 0 to 1.8. (a, c, e) are the curves of MSSIMs from the different reconstructed images using the proposed algorithm under the noise-free 340 projections, 68 projections and 34 projections, respectively. (b, d, f) are the curves of MSSIMs from the different reconstructed images using the proposed algorithm under the noisy 340 projections, 68 projections and 34 projections, respectively. Some reconstructed images from the different α values are presented in the dotted boxes, and the middle images located in the dotted boxes represent the reconstructed images from the optimal α values. When α = 0, the proposed algorithm will be equivalent to the traditional FBP combined with WGIF.
Fig. 7.
Fig. 7. Convergence curves of the proposed algorithm under the noise-free and noisy few-projection conditions. (a) and (c) are the MSSIM-based and SNR-based convergence curves of the proposed algorithm under the noise-free 340 projections, 68 projections and 34 projections. (b) and (d) are the MSSIM-based and SNR-based convergence curves of the proposed algorithm under the noisy 340 projections, 68 projections and 34 projections.
Fig. 8.
Fig. 8. Comparisons of surface-rendering images and the 318th reconstructed slices of the entire liver lobe using the FBP and the proposed algorithm under different few-projection conditions. (a, e, i) are surface-rendering images of the entire liver lobe in which all slices were reconstructed using the FBP method from 1200 projections, 400 projections and 200 projections, respectively. (b, f, j) are the 318th slices of the entire liver lobe in which all slices were reconstructed using the FBP method from 1200 projections, 400 projections and 200 projections, respectively. (c, g, k) are surface-rendering images of the entire liver lobe in which all slices were reconstructed using the proposed algorithm from 1200 projections, 400 projections and 200 projections, respectively. (d, h, l) are the 318th slices of the entire liver lobe in which all slices were reconstructed using the proposed algorithm from 1200 projections, 400 projections and 200 projections, respectively. The pixel values of the above grayscale images were normalized to the range [0, 255]. The display window was [0, 255]. The scale bar represented 250 µm.
Fig. 9.
Fig. 9. The enlarged ROIs and the profiles of the reconstructed images using the FBP and the proposed algorithm under different few-projection conditions. (a, b, c) are the enlarged ROIs of the reconstructed images using the FBP from 1200 projections, 400 projections and 200 projections, respectively. (d, e, l) are the enlarged ROIs of the reconstructed images using the proposed algorithm from 1200 projections, 400 projections and 200 projections, respectively. (g, h, i) are the profiles of the reconstructed images using the FBP and the proposed algorithm from 1200 projections, 400 projections and 200 projections, respectively. The scale bar represents 500 µm.
Fig. 10.
Fig. 10. RD-based convergence curves of the proposed algorithm under different few-projection conditions.

Tables (2)

Tables Icon

Table 1. Computational time (s) of the two algorithms for simulation experiment.

Tables Icon

Table 2. Reconstructed time (s) of the two algorithms for SR-PBCT experiment.

Equations (20)

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

n ( x , y , z ) = 1 δ ( x , y , z ) + i β ( x , y , z ) .
φ θ ( x , y ) = γ 2 ln ( F 1 { F [ I θ , z = D ( x , y ) ] 1 + λ π γ D ( ξ 2 + η 2 ) } ) ,
φ θ ( x , y ) = ( 2 π / λ ) L θ δ ( x , y , z ) d z ,
A δ ( x , z ) = P ,
δ ( x , z ) = 0 π P θ ( ω ) | ω | e j 2 π ω ( x cos θ + z sin θ ) d ω d θ ,
δ = P ,
δ t + 1 = δ t + α ( A δ t P ) .
δ l = a k G l + b k , l ω k ,
δ l = δ l e l ,
E ( a k , b k ) = l ω k [ ( a k G l + b k δ l ) 2 + ε a k 2 ] ,
W k G = 1 M l = 1 M ( σ k G , 1 ) 2 + ε 0 ( σ l G , 1 ) 2 + ε 0 ,
E ( a k , b k ) = l ω k [ ( a k G l + b k δ l ) 2 + ε W k G a k 2 ] ,
a k = 1 | ω k | l ω k ( G l δ l u k δ k ) σ k 2 + ε W k G ,
b k = δ k a k u k .
δ l = 1 | ω k | k | l ω k ( a k G l + b k ) = a l _ G l + b l _ .
S S I M ( x j , y j ) = ( 2 u x j u y j + c 1 ) ( 2 σ x j y j + c 2 ) ( u x j 2 + u y j 2 + c 1 ) ( σ x j 2 + σ y j 2 + c 2 ) ,
M S S I M ( x , y ) = 1 T j = 1 T S S I M ( x j , y j ) ,
S N R ( y ) = 20 log 10 ( S s a m p l e σ b a c k g r o u n d ) ,
R e l a t i v e l o s s r a t e = Vesse l G T Vesse l R Vesse l G T × 100 %
R D ( y t , y t + 1 ) = | | y t + 1 y t | | 2 | | y t | | 2 × 100 % .

Metrics