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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 31, Iss. 9 — Sep. 1, 2014
  • pp: 2064–2069

Real-time dispersion-compensated image reconstruction for compressive sensing spectral domain optical coherence tomography

Daguang Xu, Yong Huang, and Jin U. Kang  »View Author Affiliations


JOSA A, Vol. 31, Issue 9, pp. 2064-2069 (2014)
http://dx.doi.org/10.1364/JOSAA.31.002064


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Abstract

In this work, we propose a novel dispersion compensation method that enables real-time compressive sensing (CS) spectral domain optical coherence tomography (SD OCT) image reconstruction. We show that dispersion compensation can be incorporated into CS SD OCT by multiplying the dispersion-correcting terms by the undersampled spectral data before CS reconstruction. High-quality SD OCT imaging with dispersion compensation was demonstrated at a speed in excess of 70 frames per s using 40% of the spectral measurements required by the well-known Shannon/Nyquist theory. The data processing and image display were performed on a conventional workstation having three graphics processing units.

© 2014 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.3010) Image processing : Image reconstruction techniques
(170.4500) Medical optics and biotechnology : Optical coherence tomography

ToC Category:
Image Processing

History
Original Manuscript: June 10, 2014
Revised Manuscript: August 4, 2014
Manuscript Accepted: August 5, 2014
Published: August 27, 2014

Citation
Daguang Xu, Yong Huang, and Jin U. Kang, "Real-time dispersion-compensated image reconstruction for compressive sensing spectral domain optical coherence tomography," J. Opt. Soc. Am. A 31, 2064-2069 (2014)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-31-9-2064


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References

  1. W. Drexler and J. G. Fujimoto, Optical Coherence Tomography: Technology and Applications (Springer, 2008).
  2. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography—principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003). [CrossRef]
  3. M. Choma, M. Sarunic, C. Yang, and J. Izatt, “Sensitivity advantage of swept source and Fourier domain optical coherence tomography,” Opt. Express 11, 2183–2189 (2003). [CrossRef]
  4. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006). [CrossRef]
  5. E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006). [CrossRef]
  6. X. Liu and J. U. Kang, “Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography,” Opt. Express 18, 22010–22019 (2010). [CrossRef]
  7. L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3, 927–942 (2012). [CrossRef]
  8. D. Xu, N. Vaswani, Y. Huang, and J. U. Kang, “Modified compressive sensing optical coherence tomography with noise reduction,” Opt. Lett. 37, 4209–4211 (2012). [CrossRef]
  9. N. Zhang, T. Huo, C. Wang, T. Chen, J. Zheng, and P. Xue, “Compressed sensing with linear-in-wavenumber sampling in spectral domain optical coherence tomography,” Opt. Lett. 37, 3075–3077 (2012). [CrossRef]
  10. C. Liu, A. Wong, K. Bizheva, P. Fieguth, and H. Bie, “Homotopic, non-local sparse reconstruction of optical coherence tomography imagery,” Opt. Express 20, 10200–10211 (2012). [CrossRef]
  11. S. Schwartz, C. Liu, A. Wong, D. A. Clausi, P. Fieguth, and K. Bizheva, “Energy-guided learning approach to compressive sensing,” Opt. Express 21, 329–344 (2013). [CrossRef]
  12. D. Xu, Y. Huang, and J. U. Kang, “Compressive sensing with dispersion compensation on non-linear wavenumber sampled spectral domain optical coherence tomography,” Biomed. Opt. Express 4, 1519–1532 (2013). [CrossRef]
  13. D. Xu, Y. Huang, and J. U. Kang, “Compressive sensing spectral domain optical coherence tomography with dispersion compensation,” Proc. SPIE 8949, 89490N (2014). [CrossRef]
  14. D. Xu, Y. Huang, and J. U. Kang, “GPU-accelerated non-uniform fast Fourier transform-based compressive sensing spectral domain optical coherence tomography,” Opt. Express 22, 14871–14884 (2014). [CrossRef]
  15. M. Wojtkowski, V. J. Srinivasan, T. H. Ko, J. G. Fujimoto, A. Kowalczyk, and J. S. Duker, “Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation,” Opt. Express 12, 2404–2422 (2004). [CrossRef]
  16. Y. Chen and X. Li, “Dispersion management up to the third order for real-time optical coherence tomography involving a phase or frequency modulator,” Opt. Express 12, 5968–5978 (2004). [CrossRef]
  17. D. L. Marks, A. L. Oldenburg, J. J. Reynolds, and S. A. Boppart, “Digital algorithm for dispersion correction in optical coherence tomography for homogeneous and stratified media,” Appl. Opt. 42, 204–217 (2003). [CrossRef]
  18. D. Xu, Y. Huang, and J. U. Kang, “Real-time compressive sensing spectral domain optical coherence tomography,” Opt. Lett. 39, 76–79 (2014). [CrossRef]
  19. M. Murphy, M. Alley, J. Demmel, K. Keutzer, S. Vasanawala, and M. Lustig, “Fast l1-SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime,” IEEE Trans. Med. Imaging 31, 1250–1262 (2012).
  20. S. Lee and S. J. Wright, “Implementing algorithm for signal and image reconstruction on graphical processing units,” (University of Wisconsin-Madison, 2008).
  21. D. Yang, G. D. Peterson, and H. Li, “Compressed sensing and Cholesky decomposition on FPGAs and GPUs,” Parallel Comput. 38, 421–437 (2012). [CrossRef]
  22. S. J. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57, 2479–2493 (2009). [CrossRef]

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