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. 2010 Oct 11;18(21):22010-9.
doi: 10.1364/OE.18.022010.

Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography

Xuan Liu  1 , Jin U Kang
Affiliations

Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography

Xuan Liu et al. Opt Express. .

Abstract

We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD camera pixels. OCT images are reconstructed by solving an optimization problem that minimizes the l(1) norm of a transformed image to enforce sparsity, subject to data consistency constraints. CS could allow an array detector with fewer pixels to reconstruct high resolution OCT images while reducing the total amount of data required to process the images.

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Figures

Fig. 1
Fig. 1
Schematic of CP SD OCT.
Fig. 2
Fig. 2
Illustration of random undersampling.
Fig. 3
Fig. 3
Simulated PSF (1,j) when randomly undersampling 20% of CCD pixels.
Fig. 4
Fig. 4
(a) Spectral interferogram obtained by using a mirror as sample; (b) illustration of different sampling schemes (red circles = random undersampling; green squares = uniform undersampling; blue stars = complete sampling).
Fig. 5
Fig. 5
M-scans obtained by standard SD OCT image reconstruction algorithms using different sampling schemes: (a) complete sampling, (b) uniform density undersampling, (c) random undersampling.
Fig. 6
Fig. 6
(a) A-scan obtained from random undersampled spectrum after the 1st CG iteration, (b) A-scan obtained from random undersampled spectrum after the 11th CG iteration, (c) blue curve: A-scan which is the solution of Eq. (2), red curve: A-scan obtained with complete spectral data; (d) M-scan obtained by CS.
Fig. 7
Fig. 7
OCT image of onion cells: (a) obtained using complete spectral data; (b), (c), and (d) obtained by sampling 62.5%, 50%, 37.5% of the pixels and pursuing sparsity in pixel domain; (e), (f), and (g) obtained by sampling 62.5%, 50%, 37.5% of the pixels and pursuing sparsity in wavelet domain.
Fig. 8
Fig. 8
(a) Profile of sample surface obtained from Fig. 7(a); (b) histogram of Δ(P) when sampling 62.5%, 50% and 37.5% of the pixels.
See this image and copyright information in PMC

References

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