OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 36 — Dec. 20, 2012
  • pp: 8731–8744

Fluorescence molecular tomography using a two-step three-dimensional shape-based reconstruction with graphics processing unit acceleration

Daifa Wang, Huiting Qiao, Xiaolei Song, Yubo Fan, and Deyu Li  »View Author Affiliations


Applied Optics, Vol. 51, Issue 36, pp. 8731-8744 (2012)
http://dx.doi.org/10.1364/AO.51.008731


View Full Text Article

Enhanced HTML    Acrobat PDF (1265 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

In fluorescence molecular tomography, the accurate and stable reconstruction of fluorescence-labeled targets remains a challenge for wide application of this imaging modality. Here we propose a two-step three-dimensional shape-based reconstruction method using graphics processing unit (GPU) acceleration. In this method, the fluorophore distribution is assumed as the sum of ellipsoids with piecewise-constant fluorescence intensities. The inverse problem is formulated as a constrained nonlinear least-squares problem with respect to shape parameters, leading to much less ill-posedness as the number of unknowns is greatly reduced. Considering that various shape parameters contribute differently to the boundary measurements, we use a two-step optimization algorithm to handle them in a distinctive way and also stabilize the reconstruction. Additionally, the GPU acceleration is employed for finite-element-method-based calculation of the objective function value and the Jacobian matrix, which reduces the total optimization time from around 10 min to less than 1 min. The numerical simulations show that our method can accurately reconstruct multiple targets of various shapes while the conventional voxel-based reconstruction cannot separate the nearby targets. Moreover, the two-step optimization can tolerate different initial values in the existence of noises, even when the number of targets is not known a priori. A physical phantom experiment further demonstrates the method’s potential in practical applications.

© 2012 Optical Society of America

OCIS Codes
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.6280) Medical optics and biotechnology : Spectroscopy, fluorescence and luminescence
(170.6960) Medical optics and biotechnology : Tomography

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: August 27, 2012
Revised Manuscript: November 10, 2012
Manuscript Accepted: November 26, 2012
Published: December 19, 2012

Virtual Issues
Vol. 8, Iss. 1 Virtual Journal for Biomedical Optics

Citation
Daifa Wang, Huiting Qiao, Xiaolei Song, Yubo Fan, and Deyu Li, "Fluorescence molecular tomography using a two-step three-dimensional shape-based reconstruction with graphics processing unit acceleration," Appl. Opt. 51, 8731-8744 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-36-8731


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. E. E. Graves, J. Ripoll, R. Weissleder, and V. Ntziachristos, “A sub-millimeter resolution fluorescence molecular imaging system for small animal imaging,” Med. Phys. 30, 901–911 (2003). [CrossRef]
  2. F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: review of instruments, methods, and applications,” J. Photochem. Photobiol. B 98, 77–94 (2010). [CrossRef]
  3. Y. Lin, H. Yan, O. Nalcioglu, and G. Gulsen, “Quantitative fluorescence tomography with functional and structural a priori information,” Appl. Opt. 48, 1328–1336 (2009). [CrossRef]
  4. S. C. Davis, H. Dehghani, J. Wang, S. Jiang, B. W. Pogue, and K. D. Paulsen, “Image-guided diffuse optical fluorescence tomography implemented with Laplacian-type regularization,” Opt. Express 15, 4066–4082 (2007). [CrossRef]
  5. D. Hyde, E. L. Miller, D. H. Brooks, and V. Ntziachristos, “Data specific spatially varying regularization for multi-modal fluorescence molecular tomography,” IEEE Trans. Med. Imaging 29, 365–374 (2010). [CrossRef]
  6. M. Li, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Reconstruction of fluorescence molecular tomography using a neighborhood regularization,” IEEE Trans. Biomed. Eng. 59, 1799–1803 (2012). [CrossRef]
  7. J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57, 1459–1476 (2012). [CrossRef]
  8. P. Mohajerani, A. A. Eftekhar, J. Huang, and A. Adibi, “Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results,” Appl. Opt. 46, 1679–1685 (2007). [CrossRef]
  9. D. Han, J. Tian, S. Zhu, J. Feng, C. Qin, B. Zhang, and X. Yang, “A fast reconstruction algorithm for fluorescence molecular tomography with sparsity regularization,” Opt. Express 18, 8630–8646 (2010). [CrossRef]
  10. M. Schweiger, S. R. Arridge, O. Dorn, A. Zacharopoulos, and V. Kolehmainen, “Reconstructing absorption and diffusion shape profiles in optical tomography using a level set technique,” Opt. Lett. 31, 471–473 (2006). [CrossRef]
  11. M. Schweiger, O. Dorn, A. Zacharopoulos, I. Nissila, and S. R. Arridge, “3D level set reconstruction of model and experimental data in diffuse optical tomography,” Opt. Express 18, 150–164 (2010). [CrossRef]
  12. K. Liu, X. Yang, D. Liu, C. Qin, J. Liu, Z. Chang, M. Xu, and J. Tian, “Spectrally resolved three-dimensional bioluminescence tomography with a level-set strategy,” J. Opt. Soc. Am. A 27, 1413–1423 (2010). [CrossRef]
  13. D. Álvarez, P. Medina, and M. Moscoso, “Fluorescence lifetime imaging from time resolved measurements using a shape-based approach,” Opt. Express 17, 8843–8855 (2009). [CrossRef]
  14. M. E. Kilmer, E. L. Miller, A. Barbaro, and D. Boas, “Three-dimensional shape-based imaging of absorption perturbation for diffuse optical tomography,” Appl. Opt. 42, 3129–3144 (2003). [CrossRef]
  15. A. Zacharopoulos, M. Schweiger, V. Kolehmainen, and S. Arridge, “3D shape based reconstruction of experimental data in diffuse optical tomography,” Opt. Express 17, 18940–18956 (2009). [CrossRef]
  16. G. Boverman, E. L. Miller, D. H. Brooks, D. Isaacson, Q. Fang, and D. A. Boas, “Estimation and statistical bounds for three-dimensional polar shapes in diffuse optical tomography,” IEEE Trans. Med. Imaging 27, 752–765 (2008). [CrossRef]
  17. S. Wang, and A. P. Dhawan, “Shape-based multi-spectral optical image reconstruction through genetic algorithm based optimization,” Comput. Med. Imaging Graph. 32, 429–441 (2008). [CrossRef]
  18. S. Babaeizadeh and D. H. Brooks, “Electrical impedance tomography for piecewise constant domains using boundary element shape-based inverse solutions,” IEEE Trans. Med. Imaging 26, 637–647 (2007). [CrossRef]
  19. A. D. Zacharopoulos, S. R. Arridge, O. Dorn, V. Kolehmainen, and J. Sikora, “Three-dimensional reconstruction of shape and piecewise constant region values for optical tomography using spherical harmonic parametrization and a boundary element method,” Inverse Problems 22, 1509–1532(2006). [CrossRef]
  20. P. Venkataraman, Applied Optimization with Matlab Programming (Wiley, 2002).
  21. H. Gao, H. Zhao, W. Cong, and G. Wang, “Bioluminescence tomography with Gaussian prior,” Biomed. Opt. Express 1, 1259–1277 (2010). [CrossRef]
  22. A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized born ratio,” IEEE Trans. Med. Imag. 24, 1377–1386 (2005). [CrossRef]
  23. X. Song, D. Wang, N. Chen, J. Bai, and H. Wang, “Reconstruction for free-space fluorescence tomography using a novel hybrid adaptive finite element algorithm,” Opt. Express 15, 18300–18317 (2007). [CrossRef]
  24. M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite elementmethod for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22, 1779–1792 (1995). [CrossRef]
  25. NVIDIA Corporation, NVIDIA CUDA C Programming Guide 4.0 (2011).
  26. Q. Fang and D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express 17, 20178–20190 (2009). [CrossRef]
  27. E. Alerstam, W. C. Y. Lo, T. D. Han, J. Rose, S. Andersson-Engels, and L. Lilge, “Next-generation acceleration and codeoptimization for light transport in turbid media using GPUs,” Biomed. Opt. Express 1, 658–675 (2010). [CrossRef]
  28. A. Sassaroli, “Fast perturbation Monte Carlo method for photon migration in heterogeneous turbid media,” Opt. Lett. 36, 2095–2097 (2011). [CrossRef]
  29. B. Zhang, X. Yang, F. Yang, C. Qin, D. Han, X. Ma, K. Liu, and J. Tian, “The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography,” Opt. Express 18, 20201–20214 (2010). [CrossRef]
  30. NVIDIA Corporation, NVIDIA’s Next Generation CUDA Compute Architecture: Fermi (2010).
  31. NVIDIA Corporation, CUDA Toolkit 4.0 CUBLAS Library (2011).
  32. A. Kak and M. Slaney, Computerized Tomographic Imaging (IEEE, 1987).
  33. N. Deliolanis, T. Lasser, D. Hyde, A. Soubret, J. Ripoll, and V. Ntziachristos, “Free-space fluorescence molecular tomography utilizing 360° geometry projections,” Opt. Lett. 32, 382–384 (2007). [CrossRef]
  34. D. Wang, X. Liu, F. Liu, and J. Bai, “Full-angle fluorescence diffuse optical tomography with spatially coded parallel excitation,” IEEE Trans. Inf. Technol. Biomed. 14, 1346–1354 (2010). [CrossRef]
  35. D. Wang, X. Liu, Y. Chen, and J. Bai, “In-vivo fluorescence molecular tomography based on optimal small animal surface reconstruction,” Chin. Opt. Lett. 8, 82–85 (2010). [CrossRef]
  36. T. Lasser and V. Ntziachristos, “Optimization of 360° projection fluorescence molecular tomography,” Medical Image Anal. 11, 389–399 (2007). [CrossRef]
  37. H. Dehghani, S. R. Arridge, M. Schweiger, and D. T. Deply, “Optical tomography in the presence of void regions,” J. Opt. Soc. Am. A 17, 1659–1670 (2000). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited