Abstract

The three-dimensional volumetric imaging capability of optical coherence tomography (OCT) leads to the generation of large amounts of data, which necessitates high speed acquisition followed by high dimensional image processing and visualization. This signal acquisition and processing pipeline demands high A-scan rates on the front end, which has driven researchers to push A-scan acquisition rates into the MHz regime. To this end, the optical time-stretch approach uses a mode locked laser (MLL) source, dispersion in optical fiber, and a single analog-to-digital converter (ADC) to achieve multi-MHz A-scan rates. While enabling impressive performance this Nyquist sampling approach is ultimately constrained by the sampling rate and bandwidth of the ADC. Additionally such an approach generates massive amounts of data. Here we present a compressed sensing (CS) OCT system that uses a MLL, electro-optic modulation, and optical dispersion to implement data compression in the physical domain and rapidly acquire real-time compressed measurements of the OCT signals. Compression in the analog domain prior to digitization allows for the use of lower bandwidth ADCs, which reduces cost and decreases the required data capacity of the sampling interface. By leveraging a compressive A-scan optical sampling approach and the joint sparsity of C-scan data we demonstrate 14.4-MHz to 144-MHz A-scan acquisition speeds using a sub-Nyquist 1.44 Gsample/sec ADC sampling rate. Furthermore we evaluate the impact of data compression and resulting imaging speed on image quality.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Compressive sensing based high-speed time-stretch optical microscopy for two-dimensional image acquisition

Qiang Guo, Hongwei Chen, Zhiliang Weng, Minghua Chen, Sigang Yang, and Shizhong Xie
Opt. Express 23(23) 29639-29646 (2015)

Energy-guided learning approach to compressive FD-OCT

Shimon Schwartz, Chenyi Liu, Alexander Wong, David A. Clausi, Paul Fieguth, and Kostadinka Bizheva
Opt. Express 21(1) 329-344 (2013)

References

  • View by:
  • |
  • |
  • |

  1. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
    [Crossref]
  2. P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
    [Crossref]
  3. J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical coherence tomography of ocular diseases (Slack, 2004).
  4. K. Zhang and J. U. Kang, “Real-time 4D signal processing and visualization using graphics processing unit on a regular nonlinear-k Fourier-domain OCT system,” Opt. Express 18(11), 11772–11784 (2010).
    [Crossref]
  5. I. Grulkowski, M. Gora, M. Szkulmowski, I. Gorczynska, D. Szlag, S. Marcos, A. Kowalczyk, and M. Wojtkowski, “Anterior segment imaging with Spectral OCT system using a high-speed CMOS camera,” Opt. Express 17(6), 4842–4858 (2009).
    [Crossref]
  6. S. H. Yun, G. J. Tearney, J. F. De Boer, and B. E. Bouma, “Pulsed-source and swept-source spectral-domain optical coherence tomography with reduced motion artifacts,” Opt. Express 12(23), 5614–5624 (2004).
    [Crossref]
  7. K. Zhang, W. Wang, J. Han, and J. U. Kang, “A surface topology and motion compensation system for microsurgery guidance and intervention based on common-path optical coherence tomography,” IEEE Trans. Biomed. Eng. 56(9), 2318–2321 (2009).
    [Crossref]
  8. R. Leitgeb, C. Hitzenberger, and A. Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography,” Opt. Express 11(8), 889–894 (2003).
    [Crossref]
  9. D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
    [Crossref]
  10. R. A. Leitgeb, L. Schmetterer, W. Drexler, A. F. Fercher, R. J. Zawadzki, and T. Bajraszewski, “Real-time assessment of retinal blood flow with ultrafast acquisition by color Doppler Fourier domain optical coherence tomography,” Opt. Express 11(23), 3116–3121 (2003).
    [Crossref]
  11. R. Huber, D. C. Adler, V. J. Srinivasan, and J. G. Fujimoto, “Fourier domain mode locking at 1050 nm for ultra-high-speed optical coherence tomography of the human retina at 236,000 axial scans per second,” Opt. Lett. 32(14), 2049–2051 (2007).
    [Crossref]
  12. S. Moon and D. Y. Kim, “Ultra-high-speed optical coherence tomography with a stretched pulse supercontinuum source,” Opt. Express 14(24), 11575–11584 (2006).
    [Crossref]
  13. K. Goda, A. Fard, O. Malik, G. Fu, A. Quach, and B. Jalal, “High-throughput optical coherence tomography at 800 nm,” Opt. Express 20(18), 19612–19617 (2012).
    [Crossref]
  14. J. Xu, C. Zhang, K. K. Y. Wong, and K. K. Tsia, “Megahertz all-optical swept-source optical coherence tomography based on broadband amplified optical time-stretch,” Opt. Lett. 39(3), 622–625 (2014).
    [Crossref]
  15. E. J. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005).
    [Crossref]
  16. E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
    [Crossref]
  17. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
    [Crossref]
  18. R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(4), 118–124 (2007).
    [Crossref]
  19. E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
    [Crossref]
  20. M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58(6), 1182–1195 (2007).
    [Crossref]
  21. Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
    [Crossref]
  22. X. Liu and J. U. Kang, “Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography,” Opt. Express 18(21), 22010–22019 (2010).
    [Crossref]
  23. D. Xu, Y. Huang, and J. U. Kang, “Volumetric (3D) compressive sensing spectral domain optical coherence tomography,” Biomed. Opt. Express 5(11), 3921–3934 (2014).
    [Crossref]
  24. A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
    [Crossref]
  25. M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express 2(9), 2690–2697 (2011).
    [Crossref]
  26. B. T. Bosworth and M. A. Foster, “High-speed ultrawideband photonically enabled compressed sensing of sparse radio frequency signals,” Opt. Lett. 38(22), 4892–4895 (2013).
    [Crossref]
  27. B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
    [Crossref]
  28. B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
    [Crossref]
  29. J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.
  30. C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
    [Crossref]
  31. J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.
  32. M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
    [Crossref]
  33. P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” Multiscale Model. Simul. 4(4), 1168–1200 (2005).
    [Crossref]
  34. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. on Imag. Sci. 2(1), 183–202 (2009).
    [Crossref]
  35. 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. of Sel. Topics in Signal Process. 1(4), 586–597 (2007).
    [Crossref]
  36. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
    [Crossref]
  37. D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proc. Natl. Acad. Sci. U. S. A. 106(45), 18914–18919 (2009).
    [Crossref]

2017 (2)

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
[Crossref]

2015 (2)

2014 (2)

2013 (2)

A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
[Crossref]

B. T. Bosworth and M. A. Foster, “High-speed ultrawideband photonically enabled compressed sensing of sparse radio frequency signals,” Opt. Lett. 38(22), 4892–4895 (2013).
[Crossref]

2012 (2)

K. Goda, A. Fard, O. Malik, G. Fu, A. Quach, and B. Jalal, “High-throughput optical coherence tomography at 800 nm,” Opt. Express 20(18), 19612–19617 (2012).
[Crossref]

D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
[Crossref]

2011 (1)

2010 (4)

Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
[Crossref]

X. Liu and J. U. Kang, “Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography,” Opt. Express 18(21), 22010–22019 (2010).
[Crossref]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

K. Zhang and J. U. Kang, “Real-time 4D signal processing and visualization using graphics processing unit on a regular nonlinear-k Fourier-domain OCT system,” Opt. Express 18(11), 11772–11784 (2010).
[Crossref]

2009 (4)

I. Grulkowski, M. Gora, M. Szkulmowski, I. Gorczynska, D. Szlag, S. Marcos, A. Kowalczyk, and M. Wojtkowski, “Anterior segment imaging with Spectral OCT system using a high-speed CMOS camera,” Opt. Express 17(6), 4842–4858 (2009).
[Crossref]

K. Zhang, W. Wang, J. Han, and J. U. Kang, “A surface topology and motion compensation system for microsurgery guidance and intervention based on common-path optical coherence tomography,” IEEE Trans. Biomed. Eng. 56(9), 2318–2321 (2009).
[Crossref]

D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proc. Natl. Acad. Sci. U. S. A. 106(45), 18914–18919 (2009).
[Crossref]

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. on Imag. Sci. 2(1), 183–202 (2009).
[Crossref]

2008 (1)

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[Crossref]

2007 (4)

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58(6), 1182–1195 (2007).
[Crossref]

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. of Sel. Topics in Signal Process. 1(4), 586–597 (2007).
[Crossref]

R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(4), 118–124 (2007).
[Crossref]

R. Huber, D. C. Adler, V. J. Srinivasan, and J. G. Fujimoto, “Fourier domain mode locking at 1050 nm for ultra-high-speed optical coherence tomography of the human retina at 236,000 axial scans per second,” Opt. Lett. 32(14), 2049–2051 (2007).
[Crossref]

2006 (3)

S. Moon and D. Y. Kim, “Ultra-high-speed optical coherence tomography with a stretched pulse supercontinuum source,” Opt. Express 14(24), 11575–11584 (2006).
[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
[Crossref]

2005 (3)

E. J. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005).
[Crossref]

P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
[Crossref]

P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” Multiscale Model. Simul. 4(4), 1168–1200 (2005).
[Crossref]

2004 (1)

2003 (3)

Adler, D. C.

Avestimehr, A. S.

M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
[Crossref]

Bai, F.

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

Bajraszewski, T.

Baraniuk, R. G.

R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(4), 118–124 (2007).
[Crossref]

Beck, A.

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. on Imag. Sci. 2(1), 183–202 (2009).
[Crossref]

Beg, M. F.

A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
[Crossref]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express 2(9), 2690–2697 (2011).
[Crossref]

Bosworth, B. T.

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
[Crossref]

B. T. Bosworth and M. A. Foster, “High-speed ultrawideband photonically enabled compressed sensing of sparse radio frequency signals,” Opt. Lett. 38(22), 4892–4895 (2013).
[Crossref]

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

Bouma, B. E.

Boyd, S.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

Candes, E. J.

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

E. J. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005).
[Crossref]

Chin, S.

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
[Crossref]

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

Choi, D.

D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
[Crossref]

Chu, E.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

Clark, T. R.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

Combettes, P. L.

P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” Multiscale Model. Simul. 4(4), 1168–1200 (2005).
[Crossref]

De Boer, J. F.

Donoho, D.

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58(6), 1182–1195 (2007).
[Crossref]

Donoho, D. L.

D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proc. Natl. Acad. Sci. U. S. A. 106(45), 18914–18919 (2009).
[Crossref]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
[Crossref]

Drexler, W.

Duker, J. S.

J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical coherence tomography of ocular diseases (Slack, 2004).

Eckstein, J.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

Fard, A.

Fercher, A.

Fercher, A. F.

Figueiredo, M. A. T.

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. of Sel. Topics in Signal Process. 1(4), 586–597 (2007).
[Crossref]

Foster, M. A.

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

B. T. Bosworth and M. A. Foster, “High-speed ultrawideband photonically enabled compressed sensing of sparse radio frequency signals,” Opt. Lett. 38(22), 4892–4895 (2013).
[Crossref]

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

Fu, G.

Fujimoto, J. G.

Gibson, S.

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

Goda, K.

Gora, M.

Gorczynska, I.

Grulkowski, I.

Guo, Z.

Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
[Crossref]

Han, J.

K. Zhang, W. Wang, J. Han, and J. U. Kang, “A surface topology and motion compensation system for microsurgery guidance and intervention based on common-path optical coherence tomography,” IEEE Trans. Biomed. Eng. 56(9), 2318–2321 (2009).
[Crossref]

Hassibi, B.

M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
[Crossref]

Hiro-Oka, H.

D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
[Crossref]

Hitzenberger, C.

Hitzenberger, C. K.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

Huang, Y.

Huber, R.

Jalal, B.

Jian, Y.

Kang, J. U.

Khajehnejad, M. A.

M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
[Crossref]

Kim, D. Y.

Kowalczyk, A.

Lasser, T.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

Lebed, E.

A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
[Crossref]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express 2(9), 2690–2697 (2011).
[Crossref]

Leitgeb, R.

Leitgeb, R. A.

Li, C.

Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
[Crossref]

Liu, J.

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

Liu, X.

Lustig, M.

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58(6), 1182–1195 (2007).
[Crossref]

Mackenzie, P. J.

Maleki, A.

D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proc. Natl. Acad. Sci. U. S. A. 106(45), 18914–18919 (2009).
[Crossref]

Malik, O.

Marcos, S.

McKenna, T. P.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

Mididoddi, C. K.

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

Montanari, A.

D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proc. Natl. Acad. Sci. U. S. A. 106(45), 18914–18919 (2009).
[Crossref]

Moon, S.

Nowak, R. D.

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. of Sel. Topics in Signal Process. 1(4), 586–597 (2007).
[Crossref]

Ohbayashi, K.

D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
[Crossref]

Parikh, N.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

Pauly, J. M.

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58(6), 1182–1195 (2007).
[Crossref]

Peleato, B.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

Puliafito, C. A.

J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical coherence tomography of ocular diseases (Slack, 2004).

Quach, A.

Romberg, J.

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Sarunic, M. V.

A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
[Crossref]

M. Young, E. Lebed, Y. Jian, P. J. Mackenzie, M. F. Beg, and M. V. Sarunic, “Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography,” Biomed. Opt. Express 2(9), 2690–2697 (2011).
[Crossref]

Schmetterer, L.

Schuman, J. S.

J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical coherence tomography of ocular diseases (Slack, 2004).

Shimizu, K.

D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
[Crossref]

Song, L.

Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
[Crossref]

Srinivasan, V. J.

Stroud, J. R.

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
[Crossref]

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

Szkulmowski, M.

Szlag, D.

Tao, T.

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

E. J. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005).
[Crossref]

Tearney, G. J.

Teboulle, M.

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. on Imag. Sci. 2(1), 183–202 (2009).
[Crossref]

Tomlins, P. H.

P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
[Crossref]

Tran, D. N.

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

Tran, T. D.

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses,” Opt. Lett. 40(13), 3045–3048 (2015).
[Crossref]

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

Tsia, K. K.

Wajs, V. R.

P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” Multiscale Model. Simul. 4(4), 1168–1200 (2005).
[Crossref]

Wakin, M. B.

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[Crossref]

Wang, C.

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

Wang, G.

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

Wang, L. V.

Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
[Crossref]

Wang, R. K.

P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
[Crossref]

Wang, W.

K. Zhang, W. Wang, J. Han, and J. U. Kang, “A surface topology and motion compensation system for microsurgery guidance and intervention based on common-path optical coherence tomography,” IEEE Trans. Biomed. Eng. 56(9), 2318–2321 (2009).
[Crossref]

Wojtkowski, M.

Wong, K. K. Y.

Wright, S. J.

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. of Sel. Topics in Signal Process. 1(4), 586–597 (2007).
[Crossref]

Wu, A. B.

A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
[Crossref]

Xu, D.

Xu, J.

Xu, W.

M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
[Crossref]

Young, M.

Yun, S. H.

Zawadzki, R. J.

Zhang, C.

Zhang, K.

K. Zhang and J. U. Kang, “Real-time 4D signal processing and visualization using graphics processing unit on a regular nonlinear-k Fourier-domain OCT system,” Opt. Express 18(11), 11772–11784 (2010).
[Crossref]

K. Zhang, W. Wang, J. Han, and J. U. Kang, “A surface topology and motion compensation system for microsurgery guidance and intervention based on common-path optical coherence tomography,” IEEE Trans. Biomed. Eng. 56(9), 2318–2321 (2009).
[Crossref]

Biomed. Opt. Express (2)

FNT in Machine Learning (1)

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” FNT in Machine Learning 3(1), 1–122 (2010).
[Crossref]

IEEE International Symposium on Inf. Theory, ISIT (1)

M. A. Khajehnejad, W. Xu, A. S. Avestimehr, and B. Hassibi, “Weighted l1 minimization for sparse recovery with prior information,” IEEE International Symposium on Inf. Theory, ISIT 2009, iaw023 (2017).
[Crossref]

IEEE J. of Sel. Topics in Signal Process. (1)

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. of Sel. Topics in Signal Process. 1(4), 586–597 (2007).
[Crossref]

IEEE Photonics J. (1)

C. K. Mididoddi, F. Bai, G. Wang, J. Liu, S. Gibson, and C. Wang, “High throughput photonic time stretch optical coherence tomography with data compression,” IEEE Photonics J. 9(4), 1–15 (2017).
[Crossref]

IEEE Signal Process. Mag. (2)

R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(4), 118–124 (2007).
[Crossref]

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[Crossref]

IEEE Trans. Biomed. Eng. (2)

K. Zhang, W. Wang, J. Han, and J. U. Kang, “A surface topology and motion compensation system for microsurgery guidance and intervention based on common-path optical coherence tomography,” IEEE Trans. Biomed. Eng. 56(9), 2318–2321 (2009).
[Crossref]

A. B. Wu, E. Lebed, M. V. Sarunic, and M. F. Beg, “Quantitative evaluation of transform domains for compressive sampling-based recovery of sparsely sampled volumetric OCT images,” IEEE Trans. Biomed. Eng. 60(2), 470–478 (2013).
[Crossref]

IEEE Trans. Inf. Theory (3)

E. J. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005).
[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
[Crossref]

J. Biomed. Opt. (1)

Z. Guo, C. Li, L. Song, and L. V. Wang, “Compressed sensing in photoacoustic tomography in vivo,” J. Biomed. Opt. 15(2), 021311 (2010).
[Crossref]

J. Phys. D: Appl. Phys. (1)

P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
[Crossref]

Magn. Reson. Med. (1)

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58(6), 1182–1195 (2007).
[Crossref]

Multiscale Model. Simul. (1)

P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” Multiscale Model. Simul. 4(4), 1168–1200 (2005).
[Crossref]

Opt. Express (10)

D. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Opt. Express 3(12), 3067–3086 (2012).
[Crossref]

R. Leitgeb, C. Hitzenberger, and A. Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography,” Opt. Express 11(8), 889–894 (2003).
[Crossref]

R. A. Leitgeb, L. Schmetterer, W. Drexler, A. F. Fercher, R. J. Zawadzki, and T. Bajraszewski, “Real-time assessment of retinal blood flow with ultrafast acquisition by color Doppler Fourier domain optical coherence tomography,” Opt. Express 11(23), 3116–3121 (2003).
[Crossref]

S. H. Yun, G. J. Tearney, J. F. De Boer, and B. E. Bouma, “Pulsed-source and swept-source spectral-domain optical coherence tomography with reduced motion artifacts,” Opt. Express 12(23), 5614–5624 (2004).
[Crossref]

S. Moon and D. Y. Kim, “Ultra-high-speed optical coherence tomography with a stretched pulse supercontinuum source,” Opt. Express 14(24), 11575–11584 (2006).
[Crossref]

B. T. Bosworth, J. R. Stroud, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “High-speed flow microscopy using compressed sensing with ultrafast laser pulses,” Opt. Express 23(8), 10521–10532 (2015).
[Crossref]

I. Grulkowski, M. Gora, M. Szkulmowski, I. Gorczynska, D. Szlag, S. Marcos, A. Kowalczyk, and M. Wojtkowski, “Anterior segment imaging with Spectral OCT system using a high-speed CMOS camera,” Opt. Express 17(6), 4842–4858 (2009).
[Crossref]

K. Zhang and J. U. Kang, “Real-time 4D signal processing and visualization using graphics processing unit on a regular nonlinear-k Fourier-domain OCT system,” Opt. Express 18(11), 11772–11784 (2010).
[Crossref]

X. Liu and J. U. Kang, “Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography,” Opt. Express 18(21), 22010–22019 (2010).
[Crossref]

K. Goda, A. Fard, O. Malik, G. Fu, A. Quach, and B. Jalal, “High-throughput optical coherence tomography at 800 nm,” Opt. Express 20(18), 19612–19617 (2012).
[Crossref]

Opt. Lett. (4)

Proc. Natl. Acad. Sci. U. S. A. (1)

D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proc. Natl. Acad. Sci. U. S. A. 106(45), 18914–18919 (2009).
[Crossref]

Rep. Prog. Phys. (1)

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

SIAM J. on Imag. Sci. (1)

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. on Imag. Sci. 2(1), 183–202 (2009).
[Crossref]

Other (3)

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. P. McKenna, T. R. Clark, T. D. Tran, S. Chin, and M. A. Foster, “Continuous 119.2-GSample/s photonic compressed sensing of sparse microwave signals,” In CLEO: Science and Innovations (Optical Society of America, 2015) paper STh4F-2.

J. R. Stroud, B. T. Bosworth, D. N. Tran, T. D. Tran, S. Chin, and M. A. Foster, “72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS),” In CLEO: Science and Innovations (Optical Society of America, 2016) paper SM2I-1.

J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical coherence tomography of ocular diseases (Slack, 2004).

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 (4)

Fig. 1.
Fig. 1. Experimental setup for conventional time-stretch MHz OCT is shown on top. A 90-MHz MLL is pulse picked down to a 18-MHz repetition rate and dispersed to over 8 nanoseconds using SMF. This is sent into the OCT interferometer and the returned pulses are detected with a 20-GHz balanced photo-detector and digitized at 40 Gsamples/s. Our CHiRP-CS MHz OCT system is shown at the bottom. Pulses from a 90-MHz MLL are dispersed in DCF, spectral encoded with a PRBS using an EOM, then temporally compressed in SMF. The pulses are temporally multiplexed four times, before and after the modulation, for a final 1.44-GHz repetition rate. The pulses are sent into the OCT interferometer and detected with a 1.6-GHz balanced photo-detector and digitized at 1.44 Gsamples/s. MLL - mode-locked laser, SMF - single mode fiber, DCF - dispersion compensating fiber, EOM - electro-optic modulator, PRBS - psudeo-random binary sequence, BPD - balanced photo-detector.
Fig. 2.
Fig. 2. The full process for reconstructing an image, with an initial GPSR reconstruction is used to seed a weight minimization algorithm that produces the final image, illustrated in Fig. 3(b-c).
Fig. 3.
Fig. 3. C-scan recovery from $80$ compressed measurements, or an 18-MHz A-scan rate. a) The 18-MHz time-stretch OCT image which we use as the ground truth reference. b) The output of the GPSR reconstruction algorithm at the mid-stage of our image reconstruction process. c) The final C-scan reconstruction from our improved weighted minimization algorithm showing the distinct third layer of the image.
Fig. 4.
Fig. 4. a) Example C-scan reconstructions of an 100 x 150 x 192 depth image with 10, 30, 50, and 100 measurements, or 144-MHz, 48-MHz, 28.8-MHz, and 1.44-MHz A-scan rates, respectively. b) The PSNR of the CS reconstruction vs the number of compressed measurements used for reconstruction shows an increase in PSNR around 50 measurements where the third layer becomes clearly visible.

Equations (4)

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

y = a , s + z ,
y ( i , j ) = A ( i , j ) s ( i , j ) + z ( i , j ) ,
X 0 ~ = arg min X 0 C N 3 × N 1 N 2 1 2 k = 1 N 1 N 2 y ( k ) A ( k ) Φ x ( k ) 2 2 + λ 0 k = 1 N 1 N 2 x 0 ( k ) 1 ,
X ~ = arg min X C N 3 × N 1 N 2 1 2 k = 1 N 1 N 2 y ( k ) A ( k ) Φ x ( k ) 2 2 + λ k = 1 N 1 N 2 x ( k ) w , 1 ,

Metrics