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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 9 — Sep. 1, 2012
  • pp: 2131–2141

Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise

Hector R. A. Basevi, Kenneth M. Tichauer, Frederic Leblond, Hamid Dehghani, James A. Guggenheim, Robert W. Holt, and Iain B. Styles  »View Author Affiliations


Biomedical Optics Express, Vol. 3, Issue 9, pp. 2131-2141 (2012)
http://dx.doi.org/10.1364/BOE.3.002131


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Abstract

Bioluminescence Tomography attempts to quantify 3-dimensional luminophore distributions from surface measurements of the light distribution. The reconstruction problem is typically severely under-determined due to the number and location of measurements, but in certain cases the molecules or cells of interest form localised clusters, resulting in a distribution of luminophores that is spatially sparse. A Conjugate Gradient-based reconstruction algorithm using Compressive Sensing was designed to take advantage of this sparsity, using a multistage sparsity reduction approach to remove the need to choose sparsity weighting a priori. Numerical simulations were used to examine the effect of noise on reconstruction accuracy. Tomographic bioluminescence measurements of a Caliper XPM-2 Phantom Mouse were acquired and reconstructions from simulation and this experimental data show that Compressive Sensing-based reconstruction is superior to standard reconstruction techniques, particularly in the presence of noise.

© 2012 OSA

OCIS Codes
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.6280) Medical optics and biotechnology : Spectroscopy, fluorescence and luminescence
(170.6960) Medical optics and biotechnology : Tomography

ToC Category:
Image Reconstruction and Inverse Problems

History
Original Manuscript: June 7, 2012
Revised Manuscript: June 27, 2012
Manuscript Accepted: July 4, 2012
Published: August 15, 2012

Virtual Issues
BIOMED 2012 (2012) Biomedical Optics Express

Citation
Hector R. A. Basevi, Kenneth M. Tichauer, Frederic Leblond, Hamid Dehghani, James A. Guggenheim, Robert W. Holt, and Iain B. Styles, "Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise," Biomed. Opt. Express 3, 2131-2141 (2012)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-9-2131


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References

  1. S. Arridge and J. Hebden, “Optical imaging in medicine: II. modelling and reconstruction,” Phys. Med. Biol.42, 841–853 (1997). [CrossRef] [PubMed]
  2. C. Kuo, O. Coquoz, T. Troy, H. Xu, and B. Rice, “Three-dimensional reconstruction of in vivo bioluminescent sources based on multispectral imaging,” J. Biomed. Opt.12, 024007 (2007). [CrossRef] [PubMed]
  3. R. G. Baraniuk, “Compressive sensing,” IEEE Signal. Process. Mag.24, 118–124 (2007). [CrossRef]
  4. E. Candes and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl.23, 969–985 (2007). [CrossRef]
  5. H. Rauhut, “Compressive sensing and structured random matrices,” in Theoretical Foundations and Numerical Methods for Sparse Recovery, M. Massimo, ed. (deGruyter, 2010), pp. 1–92. [CrossRef]
  6. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process.1, 586–597 (2007). [CrossRef]
  7. M. Lustig, D. Donoho, and J. M. Pauly, “Sparse mri: The application of compressed sensing for rapid mr imaging,” Magn. Reson. Med.58, 1182–1195 (2007). [CrossRef] [PubMed]
  8. Y. Lu, X. Zhang, A. Douraghy, D. Stout, J. Tian, T. F. Chan, and A. F. Chatziioannou, “Source reconstruction for spectrally-resolved bioluminescence tomography with sparse a priori information,” Opt. Express17, 8062–8080 (2009). [CrossRef] [PubMed]
  9. X. He, J. Liang, X. Wang, J. Yu, X. Qu, X. Wang, Y. Hou, D. Chen, F. Liu, and J. Tian, “Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method,” Opt. Express18, 24825–24841 (2010). [CrossRef] [PubMed]
  10. W. Cong and G. Wang, “Bioluminescence tomography based on the phase approximation model,” J. Opt. Soc. Am. A27, 174–179 (2010). [CrossRef]
  11. J. Yu, F. Liu, J. Wu, L. Jiao, and X. He, “Fast source reconstruction for bioluminescence tomography based on sparse regularization,” IEEE Trans. Biomed. Eng.57, 2583–2586 (2010). [CrossRef] [PubMed]
  12. H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation part 1: l1 regularization,” Opt. Express18, 1854–1871 (2010). [CrossRef] [PubMed]
  13. H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation part 2: total variation and l1 data fidelity,” Opt. Express18, 2894–2912 (2010). [CrossRef] [PubMed]
  14. X. He, Y. Hou, D. Chen, Y. Jiang, M. Shen, J. Liu, Q. Zhang, and J. Tian, “Sparse regularization-based reconstruction for bioluminescence tomography using a multilevel adaptive finite element method,” Int. J. Biomed. Imaging2011, 203537 (2011). [CrossRef]
  15. K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt.16, 046016 (2011). [CrossRef] [PubMed]
  16. Q. Zhang, H. Zhao, D. Chen, X. Qu, X. He, X. Chen, W. Li, Z. Hu, J. Liu, and J. Liang, “Source sparsity based primal-dual interior-point method for three-dimensional bioluminescence tomography,” Opt. Commun.284, 5871–5876 (2011). [CrossRef]
  17. C. Lawson and R. Hanson, Solving Least Squares Problems (SIAM, 1995). [CrossRef]
  18. H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using nirfast: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. En.25, 711–732 (2009). [CrossRef]
  19. F. Leblond, K. M. Tichauer, R. W. Holt, F. El-Ghussein, and B. W. Pogue, “Toward whole-body optical imaging of rats using single-photon counting fluorescence tomography,” Opt. Lett.36, 3723–3725 (2011). [CrossRef] [PubMed]

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