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

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2014

2013

2012

2010

2009

S. J. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57, 2479–2493 (2009).
[CrossRef]

2006

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (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, 489–509 (2006).
[CrossRef]

2004

2003

Alley, M.

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).

Bie, H.

Bizheva, K.

Boppart, S. A.

Candes, E. 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, 489–509 (2006).
[CrossRef]

Chen, T.

Chen, Y.

Choma, M.

Clausi, D. A.

Demmel, J.

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).

Donoho, D. L.

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

Drexler, W.

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]

W. Drexler and J. G. Fujimoto, Optical Coherence Tomography: Technology and Applications (Springer, 2008).

Duker, J. S.

Fang, L.

Farsiu, S.

Fercher, A. F.

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]

Fieguth, P.

Figueiredo, M.

S. J. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57, 2479–2493 (2009).
[CrossRef]

Fujimoto, J. G.

Hitzenberger, C. K.

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]

Huang, Y.

Huo, T.

Izatt, J.

Izatt, J. A.

Kang, J. U.

Keutzer, K.

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).

Ko, T. H.

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, 239–303 (2003).
[CrossRef]

Lee, S.

S. Lee and S. J. Wright, “Implementing algorithm for signal and image reconstruction on graphical processing units,” (University of Wisconsin-Madison, 2008).

Li, H.

D. Yang, G. D. Peterson, and H. Li, “Compressed sensing and Cholesky decomposition on FPGAs and GPUs,” Parallel Comput. 38, 421–437 (2012).
[CrossRef]

Li, S.

Li, X.

Liu, C.

Liu, X.

Lustig, M.

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).

Marks, D. L.

Murphy, M.

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).

Nie, Q.

Nowak, R.

S. J. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57, 2479–2493 (2009).
[CrossRef]

Oldenburg, A. L.

Peterson, G. D.

D. Yang, G. D. Peterson, and H. Li, “Compressed sensing and Cholesky decomposition on FPGAs and GPUs,” Parallel Comput. 38, 421–437 (2012).
[CrossRef]

Reynolds, J. J.

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, 489–509 (2006).
[CrossRef]

Sarunic, M.

Schwartz, S.

Srinivasan, V. J.

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, 489–509 (2006).
[CrossRef]

Toth, C. A.

Vasanawala, S.

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).

Vaswani, N.

Wang, C.

Wojtkowski, M.

Wong, A.

Wright, S. J.

S. J. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57, 2479–2493 (2009).
[CrossRef]

S. Lee and S. J. Wright, “Implementing algorithm for signal and image reconstruction on graphical processing units,” (University of Wisconsin-Madison, 2008).

Xu, D.

Xue, P.

Yang, C.

Yang, D.

D. Yang, G. D. Peterson, and H. Li, “Compressed sensing and Cholesky decomposition on FPGAs and GPUs,” Parallel Comput. 38, 421–437 (2012).
[CrossRef]

Zhang, N.

Zheng, J.

Appl. Opt.

Biomed. Opt. Express

IEEE Trans. Inf. Theory

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (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, 489–509 (2006).
[CrossRef]

IEEE Trans. Med. Imaging

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).

IEEE Trans. Signal Process.

S. J. Wright, R. Nowak, and M. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57, 2479–2493 (2009).
[CrossRef]

Opt. Express

Opt. Lett.

Parallel Comput.

D. Yang, G. D. Peterson, and H. Li, “Compressed sensing and Cholesky decomposition on FPGAs and GPUs,” Parallel Comput. 38, 421–437 (2012).
[CrossRef]

Proc. SPIE

D. Xu, Y. Huang, and J. U. Kang, “Compressive sensing spectral domain optical coherence tomography with dispersion compensation,” Proc. SPIE 8949, 89490N (2014).
[CrossRef]

Rep. Prog. Phys.

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]

Other

W. Drexler and J. G. Fujimoto, Optical Coherence Tomography: Technology and Applications (Springer, 2008).

S. Lee and S. J. Wright, “Implementing algorithm for signal and image reconstruction on graphical processing units,” (University of Wisconsin-Madison, 2008).

Supplementary Material (3)

» Media 1: MOV (1762 KB)     
» Media 2: MOV (1943 KB)     
» Media 3: MOV (2018 KB)     

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Figures (4)

Fig. 1.
Fig. 1.

Data processing flow chart of CS SD OCT with dispersion compensation on the triple-GPU architecture.

Fig. 2.
Fig. 2.

(a) Average reconstruction time of one A-scan using different methods. (b) Benchmark line rate test of different methods.

Fig. 3.
Fig. 3.

(a) Comparison of PSFs obtained with different methods. (b) Difference between the results of CS-Disp-FFT and CS-Disp-Matrix.

Fig. 4.
Fig. 4.

B-scan results of (a)–(c) an orange and (d)–(f) human skin with 2 cm water-induced dispersion using different methods: (a) and (d) classical dispersion-compensation method on 100% data; (b) and (e) CS-FFT without dispersion compensation; (c) and (f) CS-Disp-FFT with dispersion compensation. The scale bars represent 100 μm. See Media 1 and Media 2 for real-time displays of CS-Disp-FFT results of the orange and skin imaging, respectively. See Media 3 for real-time display of the CS-FFT result of the orange imaging for a comparison of image quality and speed.

Tables (1)

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Table 1. PSNR (dB) of the B-scans in Fig. 4

Equations (5)

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minxFuxyu22+τΨx1.
xd=F×(y.*Θ),
xd=F×yd.
xd=minxFuxyud22+τΨx1.
PSNR=10log10(max2(f(x))/var),

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