Abstract

Wide-field optical tomography based on structured light illumination and detection strategies enables efficient tomographic imaging of large tissues at very fast acquisition speeds. However, the optical inverse problem based on such instrumental approach is still ill-conditioned. Herein, we investigate the benefit of employing compressive sensing-based preconditioning to wide-field structured illumination and detection approaches. We assess the performances of Fluorescence Molecular Tomography (FMT) when using such preconditioning methods both in silico and with experimental data. Additionally, we demonstrate that such methodology could be used to select the subset of patterns that provides optimal reconstruction performances. Lastly, we compare preconditioning data collected using a normal base that offers good experimental SNR against that directly acquired with optimal designed base. An experimental phantom study is provided to validate the proposed technique.

© 2015 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection

Qi Pian, Ruoyang Yao, Lingling Zhao, and Xavier Intes
Opt. Lett. 40(3) 431-434 (2015)

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method

Jinzuo Ye, Chongwei Chi, Zhenwen Xue, Ping Wu, Yu An, Han Xu, Shuang Zhang, and Jie Tian
Biomed. Opt. Express 5(2) 387-406 (2014)

Nonuniform update for sparse target recovery in fluorescence molecular tomography accelerated by ordered subsets

Dianwen Zhu and Changqing Li
Biomed. Opt. Express 5(12) 4249-4259 (2014)

References

  • View by:
  • |
  • |
  • |

  1. C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
    [Crossref] [PubMed]
  2. S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
    [Crossref] [PubMed]
  3. J. Chen, V. Venugopal, F. Lesage, and X. Intes, “Time-resolved diffuse optical tomography with patterned-light illumination and detection,” Opt. Lett. 35(13), 2121–2123 (2010).
    [Crossref] [PubMed]
  4. V. Venugopal, J. Chen, F. Lesage, and X. Intes, “Full-field time-resolved fluorescence tomography of small animals,” Opt. Lett. 35(19), 3189–3191 (2010).
    [Crossref] [PubMed]
  5. N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
    [Crossref] [PubMed]
  6. V. Venugopal, J. Chen, M. Barroso, and X. Intes, “Quantitative tomographic imaging of intermolecular FRET in small animals,” Biomed. Opt. Express 3(12), 3161–3175 (2012).
    [Crossref] [PubMed]
  7. K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
    [Crossref] [PubMed]
  8. Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
    [Crossref] [PubMed]
  9. L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
    [Crossref] [PubMed]
  10. A. Jin, B. Yazici, and V. Ntziachristos, “Light illumination and detection patterns for fluorescence diffuse optical tomography based on compressive sensing,” IEEE Trans. Image Process. 23(6), 2609–2624 (2014).
    [Crossref] [PubMed]
  11. S. Arridge and J. Schotland, “Optical tomography: forward and inverse problems,” arXiv preprint arXiv:0907.2586 (2009).
  12. V. C. Kavuri, Z.-J. Lin, F. Tian, and H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed. Opt. Express 3(5), 943–957 (2012).
    [Crossref] [PubMed]
  13. S. Okawa, Y. Hoshi, and Y. Yamada, “Improvement of image quality of time-domain diffuse optical tomography with l sparsity regularization,” Biomed. Opt. Express 2(12), 3334–3348 (2011).
    [Crossref] [PubMed]
  14. J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
    [Crossref] [PubMed]
  15. F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
    [Crossref] [PubMed]
  16. M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Sig. Proc. 55(12), 5695–5702 (2007).
    [Crossref]
  17. K. Schnass and P. Vandergheynst, “Dictionary preconditioning for greedy algorithms,” IEEE Trans. Sig. Proc. 56(5), 1994–2002 (2008).
    [Crossref]
  18. L. Zelnik-Manor, K. Rosenblum, and Y. C. Eldar, “Sensing matrix optimization for block-sparse decoding,” Signal Processing, IEEE Transactions on 59(9), 4300–4312 (2011).
    [Crossref]
  19. J. Chen, V. Venugopal, and X. Intes, “Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates,” Biomed. Opt. Express 2(4), 871–886 (2011).
    [Crossref] [PubMed]
  20. J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
    [Crossref] [PubMed]
  21. X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
    [Crossref] [PubMed]
  22. J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
    [Crossref] [PubMed]
  23. J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 1060091 (2012).
    [Crossref] [PubMed]
  24. N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
    [Crossref] [PubMed]

2015 (3)

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
[Crossref] [PubMed]

2014 (3)

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

A. Jin, B. Yazici, and V. Ntziachristos, “Light illumination and detection patterns for fluorescence diffuse optical tomography based on compressive sensing,” IEEE Trans. Image Process. 23(6), 2609–2624 (2014).
[Crossref] [PubMed]

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

2013 (2)

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

2012 (4)

J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 1060091 (2012).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

V. C. Kavuri, Z.-J. Lin, F. Tian, and H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed. Opt. Express 3(5), 943–957 (2012).
[Crossref] [PubMed]

V. Venugopal, J. Chen, M. Barroso, and X. Intes, “Quantitative tomographic imaging of intermolecular FRET in small animals,” Biomed. Opt. Express 3(12), 3161–3175 (2012).
[Crossref] [PubMed]

2011 (4)

L. Zelnik-Manor, K. Rosenblum, and Y. C. Eldar, “Sensing matrix optimization for block-sparse decoding,” Signal Processing, IEEE Transactions on 59(9), 4300–4312 (2011).
[Crossref]

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

J. Chen, V. Venugopal, and X. Intes, “Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates,” Biomed. Opt. Express 2(4), 871–886 (2011).
[Crossref] [PubMed]

S. Okawa, Y. Hoshi, and Y. Yamada, “Improvement of image quality of time-domain diffuse optical tomography with l sparsity regularization,” Biomed. Opt. Express 2(12), 3334–3348 (2011).
[Crossref] [PubMed]

2010 (4)

J. Chen, V. Venugopal, F. Lesage, and X. Intes, “Time-resolved diffuse optical tomography with patterned-light illumination and detection,” Opt. Lett. 35(13), 2121–2123 (2010).
[Crossref] [PubMed]

V. Venugopal, J. Chen, F. Lesage, and X. Intes, “Full-field time-resolved fluorescence tomography of small animals,” Opt. Lett. 35(19), 3189–3191 (2010).
[Crossref] [PubMed]

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
[Crossref] [PubMed]

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

2008 (1)

K. Schnass and P. Vandergheynst, “Dictionary preconditioning for greedy algorithms,” IEEE Trans. Sig. Proc. 56(5), 1994–2002 (2008).
[Crossref]

2007 (1)

M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Sig. Proc. 55(12), 5695–5702 (2007).
[Crossref]

2003 (1)

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Abe, K.

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

Abran, M.

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
[Crossref] [PubMed]

Ahn, S.

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

Arridge, S.

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

Bai, J.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

Barroso, M.

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

V. Venugopal, J. Chen, M. Barroso, and X. Intes, “Quantitative tomographic imaging of intermolecular FRET in small animals,” Biomed. Opt. Express 3(12), 3161–3175 (2012).
[Crossref] [PubMed]

Bassi, A.

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

Bélanger, S.

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
[Crossref] [PubMed]

Canti, G.

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

Casanova, C.

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
[Crossref] [PubMed]

Chance, B.

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Chen, J.

Chen, Y.

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Cong, W.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

D’Andrea, C.

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

Darne, C.

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

Ducros, N.

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

Dutta, J.

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

Elad, M.

M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Sig. Proc. 55(12), 5695–5702 (2007).
[Crossref]

Eldar, Y. C.

L. Zelnik-Manor, K. Rosenblum, and Y. C. Eldar, “Sensing matrix optimization for block-sparse decoding,” Signal Processing, IEEE Transactions on 59(9), 4300–4312 (2011).
[Crossref]

Fang, Q.

J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 1060091 (2012).
[Crossref] [PubMed]

Hoshi, Y.

Intes, X.

Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
[Crossref] [PubMed]

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 1060091 (2012).
[Crossref] [PubMed]

V. Venugopal, J. Chen, M. Barroso, and X. Intes, “Quantitative tomographic imaging of intermolecular FRET in small animals,” Biomed. Opt. Express 3(12), 3161–3175 (2012).
[Crossref] [PubMed]

J. Chen, V. Venugopal, and X. Intes, “Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates,” Biomed. Opt. Express 2(4), 871–886 (2011).
[Crossref] [PubMed]

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

J. Chen, V. Venugopal, F. Lesage, and X. Intes, “Time-resolved diffuse optical tomography with patterned-light illumination and detection,” Opt. Lett. 35(13), 2121–2123 (2010).
[Crossref] [PubMed]

V. Venugopal, J. Chen, F. Lesage, and X. Intes, “Full-field time-resolved fluorescence tomography of small animals,” Opt. Lett. 35(19), 3189–3191 (2010).
[Crossref] [PubMed]

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
[Crossref] [PubMed]

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Jin, A.

A. Jin, B. Yazici, and V. Ntziachristos, “Light illumination and detection patterns for fluorescence diffuse optical tomography based on compressive sensing,” IEEE Trans. Image Process. 23(6), 2609–2624 (2014).
[Crossref] [PubMed]

Joshi, A. A.

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

Kavuri, V. C.

Leahy, R. M.

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

Lesage, F.

Lin, Z.-J.

Liu, F.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

Liu, H.

Lu, Y.

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

Luo, J.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

Nioka, S.

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Ntziachristos, V.

A. Jin, B. Yazici, and V. Ntziachristos, “Light illumination and detection patterns for fluorescence diffuse optical tomography based on compressive sensing,” IEEE Trans. Image Process. 23(6), 2609–2624 (2014).
[Crossref] [PubMed]

Okawa, S.

Ozturk, M. S.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Periasamy, A.

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

Pian, Q.

Ripoll, J.

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Rosenblum, K.

L. Zelnik-Manor, K. Rosenblum, and Y. C. Eldar, “Sensing matrix optimization for block-sparse decoding,” Signal Processing, IEEE Transactions on 59(9), 4300–4312 (2011).
[Crossref]

Schnass, K.

K. Schnass and P. Vandergheynst, “Dictionary preconditioning for greedy algorithms,” IEEE Trans. Sig. Proc. 56(5), 1994–2002 (2008).
[Crossref]

Sevick-Muraca, E. M.

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

Shi, J.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

Tian, F.

Valentini, G.

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

Vandergheynst, P.

K. Schnass and P. Vandergheynst, “Dictionary preconditioning for greedy algorithms,” IEEE Trans. Sig. Proc. 56(5), 1994–2002 (2008).
[Crossref]

Venugopal, V.

Wang, G.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

Yamada, Y.

Yang, F.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Yang, H.

Yao, R.

Yazici, B.

A. Jin, B. Yazici, and V. Ntziachristos, “Light illumination and detection patterns for fluorescence diffuse optical tomography based on compressive sensing,” IEEE Trans. Image Process. 23(6), 2609–2624 (2014).
[Crossref] [PubMed]

Yodh, A. G.

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Zelnik-Manor, L.

L. Zelnik-Manor, K. Rosenblum, and Y. C. Eldar, “Sensing matrix optimization for block-sparse decoding,” Signal Processing, IEEE Transactions on 59(9), 4300–4312 (2011).
[Crossref]

Zhang, J.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

Zhao, L.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

Biomed. Opt. Express (4)

IEEE Trans. Biomed. Eng. (1)

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

IEEE Trans. Image Process. (1)

A. Jin, B. Yazici, and V. Ntziachristos, “Light illumination and detection patterns for fluorescence diffuse optical tomography based on compressive sensing,” IEEE Trans. Image Process. 23(6), 2609–2624 (2014).
[Crossref] [PubMed]

IEEE Trans. Sig. Proc. (2)

M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Sig. Proc. 55(12), 5695–5702 (2007).
[Crossref]

K. Schnass and P. Vandergheynst, “Dictionary preconditioning for greedy algorithms,” IEEE Trans. Sig. Proc. 56(5), 1994–2002 (2008).
[Crossref]

J. Biomed. Opt. (4)

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15(1), 016006 (2010).
[Crossref] [PubMed]

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, and C. D’Andrea, “Fluorescence molecular tomography of an animal model using structured light rotating view acquisition,” J. Biomed. Opt. 18(2), 020503 (2013).
[Crossref] [PubMed]

J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 1060091 (2012).
[Crossref] [PubMed]

Med. Phys. (2)

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30(6), 1039–1047 (2003).
[Crossref] [PubMed]

Opt. Lett. (4)

Phys. Med. Biol. (3)

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

PLoS One (1)

K. Abe, L. Zhao, A. Periasamy, X. Intes, and M. Barroso, “Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET,” PLoS One 8(11), e80269 (2013).
[Crossref] [PubMed]

Signal Processing, IEEE Transactions on (1)

L. Zelnik-Manor, K. Rosenblum, and Y. C. Eldar, “Sensing matrix optimization for block-sparse decoding,” Signal Processing, IEEE Transactions on 59(9), 4300–4312 (2011).
[Crossref]

Other (1)

S. Arridge and J. Schotland, “Optical tomography: forward and inverse problems,” arXiv preprint arXiv:0907.2586 (2009).

Supplementary Material (1)

NameDescription
» Visualization 1: AVI (26177 KB)      Full optimal base (40 pattern pairs) in a video

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 (9)

Fig. 1
Fig. 1

(a) Simulated numerical phantom. Two tubes are segmented at 50% isovolume. (b) Subset of the quantized low-frequency imaging base used as illumination and detection bases. Half of the imaging area is taken in each case.

Fig. 2
Fig. 2

(a) Positive and negative optical masks, with regularization parameter ε = 10−2. (b) First nine pattern pairs in the optimal base, figures are scaled to the individual maximum value in each pair (see Visualization 1 for complete optimal base).

Fig. 3
Fig. 3

Normalized inner product of the Jacobian after (a) global preconditioning and (b) separate masks preconditioning. The regularization parameter value is provided as the relative factor compared to the squared maximum singular value of the matrix (with ε = 10−1, 10−2, 10−3 and 10−4).

Fig. 4
Fig. 4

Single-pixel measurements for each combination of illumination/detection patterns (40x40) in the case of no-noise (1st row) and 25dB noise added (2nd row). The first column corresponds to measurements obtained w/o preconditioning (a, e). The other columns correspond to measurements obtained via separate mask preconditioning at ε = 10−1 (b, f), ε = 10−2 (c, g) and ε = 10−3 (d, h).

Fig. 5
Fig. 5

Flow of pattern and measurement subset selection after preconditioning. The whole data set is shown in (a), with squared singular value plot (b), first 9 illumination/detection patterns are kept. 81 absolute measurement values (c) are then sorted as (d), and 15 largest values are selected for reconstruction (e).

Fig. 6
Fig. 6

Visualization of the reconstructions at isovolume of 50% for (a) ground truth, (b) no-preconditioning, (c) separate masks preconditioning and (d) global mask preconditioning.

Fig. 7
Fig. 7

Single-pixel measurements for first 81 pairs of illumination and detection patterns in the case of preconditioning after imaging with quantized low-frequency base (first row) and directly imaging with optimal base (second row). From left to right, 50dB, 40dB, 30dB and 20dB Gaussian noise is added.

Fig. 8
Fig. 8

Wide-field structured light measurements: (a) experimental data using the quantized low frequency base, (b) measurements after separate mask preconditioning.

Fig. 9
Fig. 9

(a)-(c): Reconstruction using directly the experimental measurements; (d)-(f): reconstruction using separate mask preconditioning and subset selection. (a) and (d) are acquired with LSQR; (b) and (d) with L1-norm regularization; (c) and (f) are the curvature plots of L-curves to select the optimal regularization parameters.

Tables (1)

Tables Icon

Table 1 Objective evaluation metrics mean (from 100 trials) for the reconstruction of the numerical phantom at different levels of noise and using different preconditioning approaches.

Equations (12)

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

ϕ x i (r)= Ω g x (r,r ') s i (r')dr',i=1,... N s
ϕ m i (r)= Ω g m (r,r ') ϕ x i (r')η μ axf (r')dr',i=1,... N s
Γ i,j = Ω g m j (r) ϕ x i (r)η μ af (r) dri=1,... N s , j=1,... N d .
Ax=b; with A=[ g em,1 1 ϕ ex,1 1 g em,N 1 ϕ ex,N 1 g em,1 N d ϕ ex,1 1 g em,N N d ϕ ex,N 1 g em,1 1 ϕ ex,1 2 g em,N 1 ϕ ex,N 2 g em,1 N d ϕ ex,1 N s g em,N N d ϕ ex,N N s ] R M×N
A=ΦG=[ ϕ 1 g 1 , ϕ 2 g 2 ,, ϕ N g N ],
μ(A) μ 1 (k,A)K( Φ T Φ I N F 2 + G T G I N F 2 ),
Φ pre T Φ pre I N ; G pre T G pre I N ,
M i =( Λ i +εI ) -1/2 U i T
M i = M i (+) ( M i (-) ).
VE=( V recon V truth )/ V truth .
RMSE= i=1 N ( X recon (i) X truth (i)) 2 / i=1 N ( X truth (i)) 2 ,
m= m 1 m 2 m 3 + m 4 ,

Metrics