Abstract

Challenges remain in the reconstruction of dynamic (4-D) fluorescence molecular tomography (FMT). In our previous work, we implemented a fully 4-D FMT reconstruction approach using Karhunen-Loève (KL) transformation. However, in the reconstruction processes, the input data were scan-by-scan fluorescence projections. As a result, the reconstruction interval is limited by the data acquisition time for scanning one circle projections, leading to a long time (typically >1 min). In this paper, we propose a new method to reduce the reconstruction interval of dynamic FMT imaging, which is achieved by re-assembling the acquired fluorescence projection sequence. Further, to eliminate the temporal correlations within measurement data, the re-assembled projection sequence is reconstructed by the KL-based method. The numerical simulation and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that after re-assembling measurement data, the reconstruction interval can be greatly reduced (~2.5 sec/frame). In addition, the proposed re-assembling method is helpful for improving reconstruction quality of the KL-based method.

© 2015 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Non-stationary reconstruction for dynamic fluorescence molecular tomography with extended kalman filter

Xin Liu, Hongkai Wang, and Zhuangzhi Yan
Biomed. Opt. Express 7(11) 4527-4542 (2016)

Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Guanglei Zhang, Wei He, Huangsheng Pu, Fei Liu, Maomao Chen, Jing Bai, and Jianwen Luo
Biomed. Opt. Express 6(6) 2036-2055 (2015)

Accelerated image reconstruction in fluorescence molecular tomography using dimension reduction

Xu Cao, Xin Wang, Bin Zhang, Fei Liu, Jianwen Luo, and Jing Bai
Biomed. Opt. Express 4(1) 1-14 (2013)

References

  • View by:
  • |
  • |
  • |

  1. S. Patwardhan, S. Bloch, S. Achilefu, and J. Culver, “Time-dependent whole-body fluorescence tomography of probe bio-distributions in mice,” Opt. Express 13(7), 2564–2577 (2005).
    [Crossref] [PubMed]
  2. K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
    [Crossref] [PubMed]
  3. X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
    [Crossref]
  4. V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
    [Crossref] [PubMed]
  5. S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
    [Crossref] [PubMed]
  6. X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
    [Crossref] [PubMed]
  7. M. A. O’Leary, D. A. Boas, X. D. Li, B. Chance, and A. G. Yodh, “Fluorescence lifetime imaging in turbid media,” Opt. Lett. 21(2), 158–160 (1996).
    [Crossref] [PubMed]
  8. A. Kak and M. Slaney, Computerized Tomographic Imaging (New York: IEEE Press, 1987), ch. 7.
  9. X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
    [Crossref] [PubMed]
  10. B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
    [Crossref] [PubMed]
  11. G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study,” Phys. Med. Biol. 50(17), 4225–4241 (2005).
    [Crossref] [PubMed]
  12. D. Wang, X. Liu, and J. Bai, “Analysis of fast full angle fluorescence diffuse optical tomography with beam-forming illumination,” Opt. Express 17(24), 21376–21395 (2009).
    [Crossref] [PubMed]
  13. X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
    [Crossref] [PubMed]
  14. Q. Chen, S. Mao, and J. Bai, “In vivo measurement of indocyanine green biodistribution in mammalian organs using fiber based system,” Proc. SPIE-OSA-IEEE Asia Communications and Photonics 7634, 76340C (2009).
    [Crossref]
  15. R. Roy and E. Sevick-Muraca, “Truncated Newton’s optimization scheme for absorption and fluorescence optical tomography: Part II reconstruction from synthetic measurements,” Opt. Express 4(10), 372–382 (1999).
    [Crossref] [PubMed]
  16. R. B. Cattell, “The scree test for the number of factors,” Multivariate Behav. Res. 1(2), 245–276 (1966).
    [Crossref]
  17. M. J. Niedre, G. M. Turner, and V. Ntziachristos, “Time-resolved imaging of optical coefficients through murine chest cavities,” J. Biomed. Opt. 11(6), 064017 (2006).
    [Crossref] [PubMed]
  18. D. Hyde, R. Schulz, D. Brooks, E. Miller, and V. Ntziachristos, “Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem,” J. Opt. Soc. Am. A 26(4), 919–923 (2009).
    [Crossref] [PubMed]
  19. H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
    [Crossref] [PubMed]
  20. X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).
    [Crossref] [PubMed]
  21. NVIDIA Corporation, NVIDIA CUDA C Programming Guide 4.0 (2011).
  22. A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
    [Crossref] [PubMed]
  23. S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
    [Crossref] [PubMed]

2014 (1)

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).
[Crossref] [PubMed]

2012 (1)

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

2011 (4)

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[Crossref] [PubMed]

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

2010 (1)

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

2009 (2)

2007 (1)

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

2006 (1)

M. J. Niedre, G. M. Turner, and V. Ntziachristos, “Time-resolved imaging of optical coefficients through murine chest cavities,” J. Biomed. Opt. 11(6), 064017 (2006).
[Crossref] [PubMed]

2005 (2)

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study,” Phys. Med. Biol. 50(17), 4225–4241 (2005).
[Crossref] [PubMed]

S. Patwardhan, S. Bloch, S. Achilefu, and J. Culver, “Time-dependent whole-body fluorescence tomography of probe bio-distributions in mice,” Opt. Express 13(7), 2564–2577 (2005).
[Crossref] [PubMed]

2003 (3)

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

1999 (1)

1996 (2)

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

M. A. O’Leary, D. A. Boas, X. D. Li, B. Chance, and A. G. Yodh, “Fluorescence lifetime imaging in turbid media,” Opt. Lett. 21(2), 158–160 (1996).
[Crossref] [PubMed]

1966 (1)

R. B. Cattell, “The scree test for the number of factors,” Multivariate Behav. Res. 1(2), 245–276 (1966).
[Crossref]

Achilefu, S.

Alexandrakis, G.

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study,” Phys. Med. Biol. 50(17), 4225–4241 (2005).
[Crossref] [PubMed]

Arridge, S. R.

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Bai, J.

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

D. Wang, X. Liu, and J. Bai, “Analysis of fast full angle fluorescence diffuse optical tomography with beam-forming illumination,” Opt. Express 17(24), 21376–21395 (2009).
[Crossref] [PubMed]

Bloch, S.

Boas, D.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Boas, D. A.

Brooks, D.

Casavant, C.

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Cattell, R. B.

R. B. Cattell, “The scree test for the number of factors,” Multivariate Behav. Res. 1(2), 245–276 (1966).
[Crossref]

Chance, B.

Chatziioannou, A. F.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study,” Phys. Med. Biol. 50(17), 4225–4241 (2005).
[Crossref] [PubMed]

Culver, J.

Dogdas, B.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Franceschini, M. A.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Guo, X.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Hatano, E.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Hirao, K.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Hu, G.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Hyde, D.

Inomoto, T.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Kaipio, J. P.

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Kitai, T.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Kolehmainen, V.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Leahy, R. M.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Li, X. D.

Liao, Q.

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).
[Crossref] [PubMed]

Liu, F.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Liu, X.

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).
[Crossref] [PubMed]

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

D. Wang, X. Liu, and J. Bai, “Analysis of fast full angle fluorescence diffuse optical tomography with beam-forming illumination,” Opt. Express 17(24), 21376–21395 (2009).
[Crossref] [PubMed]

Luo, J.

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

Miller, E.

Niedre, M. J.

M. J. Niedre, G. M. Turner, and V. Ntziachristos, “Time-resolved imaging of optical coefficients through murine chest cavities,” J. Biomed. Opt. 11(6), 064017 (2006).
[Crossref] [PubMed]

Ntziachristos, V.

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

D. Hyde, R. Schulz, D. Brooks, E. Miller, and V. Ntziachristos, “Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem,” J. Opt. Soc. Am. A 26(4), 919–923 (2009).
[Crossref] [PubMed]

M. J. Niedre, G. M. Turner, and V. Ntziachristos, “Time-resolved imaging of optical coefficients through murine chest cavities,” J. Biomed. Opt. 11(6), 064017 (2006).
[Crossref] [PubMed]

O’Leary, M. A.

Patwardhan, S.

Peterson, J. D.

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Prince, S.

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Rannou, F. R.

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study,” Phys. Med. Biol. 50(17), 4225–4241 (2005).
[Crossref] [PubMed]

Roy, R.

Sarantopoulos, A.

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

Satoh, S.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Schulz, R.

Sevick-Muraca, E.

Shinohara, H.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Stout, D.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Tanaka, A.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Themelis, G.

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

Tian, F.

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Turner, G. M.

M. J. Niedre, G. M. Turner, and V. Ntziachristos, “Time-resolved imaging of optical coefficients through murine chest cavities,” J. Biomed. Opt. 11(6), 064017 (2006).
[Crossref] [PubMed]

Vasquez, K. O.

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Wang, D.

Wang, H.

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).
[Crossref] [PubMed]

Wang, X.

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Yamaoka, Y.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Yanabu, N.

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

Yodh, A. G.

Zhang, B.

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Zhang, H.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

Zhang, Y.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

Hepatology (1)

H. Shinohara, A. Tanaka, T. Kitai, N. Yanabu, T. Inomoto, S. Satoh, E. Hatano, Y. Yamaoka, and K. Hirao, “Direct measurement of hepatic indocyanine green clearance with near-infrared spectroscopy: separate evaluation of uptake and removal,” Hepatology 23(1), 137–144 (1996).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (4)

X. Liu, Q. Liao, and H. Wang, “Fast X-ray luminescence computed tomography imaging,” IEEE Trans. Biomed. Eng. 61(6), 1621–1627 (2014).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (1)

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

J. Biomed. Opt. (1)

M. J. Niedre, G. M. Turner, and V. Ntziachristos, “Time-resolved imaging of optical coefficients through murine chest cavities,” J. Biomed. Opt. 11(6), 064017 (2006).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (2)

Mol. Imaging Biol. (1)

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

Multivariate Behav. Res. (1)

R. B. Cattell, “The scree test for the number of factors,” Multivariate Behav. Res. 1(2), 245–276 (1966).
[Crossref]

Opt. Express (3)

Opt. Lett. (1)

Phys. Med. Biol. (4)

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study,” Phys. Med. Biol. 50(17), 4225–4241 (2005).
[Crossref] [PubMed]

PLoS ONE (1)

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Other (3)

NVIDIA Corporation, NVIDIA CUDA C Programming Guide 4.0 (2011).

Q. Chen, S. Mao, and J. Bai, “In vivo measurement of indocyanine green biodistribution in mammalian organs using fiber based system,” Proc. SPIE-OSA-IEEE Asia Communications and Photonics 7634, 76340C (2009).
[Crossref]

A. Kak and M. Slaney, Computerized Tomographic Imaging (New York: IEEE Press, 1987), ch. 7.

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 (11)

Fig. 1
Fig. 1 Re-assembling the fluorescence projection sequence. The red points depict ICG concentrations corresponding to different projection angles from scan 1 (acquired during ~0 sec to 60 sec). The green points depict ICG concentrations from scan 2 (acquired during ~60 sec to 120 sec). Here, we assemble the frame 1 using projections U P 1 S 1 , U P 2 S 1 ,..., U P i S 1 ,..., U P K S 1 from scan 1. We re-assemble the frame 2 using the (K-1) projections U P 2 S 1 , U P 3 S 1 ,..., U P i S 1 ,..., U P K S 1 from scan 1 and the first projection U P 1 S 2 from scan 2. Similarly, we re-assemble the frame 3 using the (K-2) projections U P 3 S 1 , U P 4 S 1 ,..., U P i S 1 ,..., U P K S 1 from scan 1 and the first two projections U P 1 S 2 , U P 2 S 2 from scan 2.
Fig. 2
Fig. 2 The hybrid FMT/XCT imaging system.
Fig. 3
Fig. 3 Schematic diagram of the simulation model. (a) The mouse 3-D geometry model used in simulation studies with a length of 2.6 cm from the neck to the base of liver. The red points in (a) represent the excitation point source locations. For each excitation location, the fluorescence is measured from the opposite side within 150 o field of view. (b) The intensity curve of ICG in mouse liver, which is normalized by its maximum value. Correspondingly, in forward model, the maximum intensity (concentration) inside the targets is set as 1 unit. Inset shows that ICG intensities for 24 fluorescence projections corresponding to different projection angles from scan 10 (acquired during ~10 min to 11 min).
Fig. 4
Fig. 4 Comparison of the reconstruction results of frame 1, obtained by using the conventional and the re-assembling measurement data. (a) and (c) The reconstruction results obtained by using the conventional assembled measurement data (120-frame). (b) and (d) The reconstruction result obtained by using the re-assembled measurement data (2880-frame). The white circles in (a) and (b) depict the actual boundary of the fluorescence target. For the reconstruction in KL domain, the volume of interest is of 2.0 cm×3.0 cm×2.6 cm and sampled to 21×31×27 voxels. The 6,365 voxels inside the imaged object and 16,841 source-detector pairs are used. Note that the non-negativity constraint is not included in the reconstruction, which is necessary because some of the KL component can be negative. In addition, the same imaging model W 1 is used in the two assembling modes. All images are displayed in the same range.
Fig. 5
Fig. 5 The reconstructed FMT images acquired about 1 min after imaging (frames 1~24). After re-assembling projection sequence, the reconstruction interval is reduced from 1 min (the conventional resemble method) to ~2.5 sec. These fluorescence images are reconstructed by the KL-based method, retaining the first 2 KL components in the reconstruction processes. All images are displayed in the same range.
Fig. 6
Fig. 6 The 3-D rendering of the reconstructed images at different time points. These reconstructed images (frames 1, 2, 3, 4, 25, 241, 1441, and 2641) are obtained by the proposed re-assembling method combined with the KL-based reconstruction method. In the case, the 2880-frame (re-assembled) measurement data are used as the input data of the reconstruction. For KL reconstruction, the first 2 KL components are retained and reconstructed. All images are displayed in the same range.
Fig. 7
Fig. 7 The time course of ICG in the fluorescence targets, which is obtained by calculating the mean concentrations of ICG from the reconstructed image sequence through all time points. The green asterisks depict the ICG time course obtained by using the re-assembled measurement data (2880-frame). The black rectangles depict the ICG time course obtained by using the conventional assembled measurement data (120-frame). The red points depict the actual ICG concentrations (2880).
Fig. 8
Fig. 8 The reconstruction results from the dynamic in vivo experiment, obtained by using the conventional and the re-assembled measurement data. (a) The x-ray computed tomography image of liver region. (b) and (c) The corresponding fluorescence tomographic images that are imaged about 8 min after ICG injection. (b) The reconstruction results obtained by using the conventional assembled measurement data (120-frame). (c) The reconstruction result obtained by using the re-assembled measurement data (2880-frame). The conventional assembled or re-assembled measurement sequences are reconstructed by the KL-based method. The volume considered for reconstruction is a 1.8 cm×2.4 cm×3.1 cm 3-D region and sampled to 19×25×32 voxels. The 5,466 voxels inside the imaged object and 19,980 source-detector pairs are used in the reconstruction process of KL domain. The same imaging model W 1 is used in the two assembling modes. To improve the reconstruction quality in in vivo experiment, the reconstruction is performed incorporating a heterogeneous forward model. All images are displayed at the same range.
Fig. 9
Fig. 9 The reconstructed FMT image sequence from the dynamic in vivo imaging study, acquired about 1 min after injecting ICG (frames 1~24). The results indicate that after re-assembling projection sequence, the frame interval of reconstruction is reduced from 1 min (the conventional resemble method) to ~2.5 sec. Here, the tomographic sequence is obtained by the KL-based reconstruction method, incorporating the first 5 KL components retained. All images are displayed in the same range.
Fig. 10
Fig. 10 The 3-D rendering of the reconstructed images at different time points from in vivo experiment. These reconstructed image (frames 1, 13, 25, 49, 241, 721, 1441, and 2641) are obtained by the proposed re-assembling method combined with the KL-based reconstruction method (the first 5 KL components retained). Here, the 2880-frame (re-assembled) measurement data are used as the input data of the reconstruction. All images are displayed in the same range.
Fig. 11
Fig. 11 The time course of ICG in the mouse liver in vivo. The X axis represents the time (min). The Y axis represents the mean concentrations of ICG, which are normalized by its maximum value. The green asterisks and black rectangles describe ICG time course obtained by using the re-assembled measurement data (2880-frame) and the conventional assembled measurement data (120-frame), respectively. The red line describes the fitted ICG time course using a two compartmental model [19].

Tables (3)

Tables Icon

Table 1 The reconstruction interval in conventional and re-assembling modes.

Tables Icon

Table 2 Comparison of computational times for different assembling modes in the numerical simulation.

Tables Icon

Table 3 Comparison of computational times for different assembling methods in the in vivo experiment.

Equations (6)

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

u k = W 1 ρ k
u=Wρ
u ^ = W ^ ρ ^ .
u KL = W ^ ρ KL
u i KL = W 1 ρ i KL
ICG(t)=Aexp(αt)+Bexp(βt)

Metrics