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Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction
Optics Express, Vol. 15, Issue 10, pp. 6200-6209 (2007)
http://dx.doi.org/10.1364/OE.15.006200
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– Abstract
1. Introduction
2. Imaging System
3. Signal-to-Noise Ratio and Angular Compounding
4. Imaging
5. Conclusion
– References and links
– Abstract
1. Introduction
2. Imaging System
3. Signal-to-Noise Ratio and Angular Compounding
4. Imaging
5. Conclusion
– References and links
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Abstract
We present a novel method for rapidly acquiring optical coherence tomography (OCT) images at multiple backscattering angles. By angularly compounding these images, high levels of speckle reduction were achieved. Signal-to-noise ratio (SNR) improvements of 3.4 dB were obtained from a homogeneous tissue phantom, which was in good agreement with the predictions of a statistical model of speckle that incorporated the optical parameters of the imaging system. In addition, the fast acquisition rate of the system (10 kHz A-line repetition rate) allowed angular compounding to be performed in vivo without significant motion artifacts. Speckle-reduced OCT images of human dermis show greatly improved delineation of tissue microstructure.
© 2007 Optical Society of America
1. Introduction
Optical coherence tomography (OCT) has emerged as a powerful tool for non-invasively
probing the microstructure of biological tissue at high-speed. As with other imaging
modalities that employ coherent detection, such as synthetic aperture radar and
B-mode ultrasound, OCT images are confounded by speckle noise. Speckle imposes a
grainy texture on images that reduces the signal-to-noise level to near unity values [1]. In medical imaging contexts, speckle can reduce the
effective spatial resolution, concealing subtle differences in scattering properties
known to be crucial for differentiating normal from diseased tissue states.
J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999). [CrossRef]
Speckle results from the coherent addition of waves with a random phase distribution.
Photons propagating along different paths within biological tissue acquire random
phase shifts as a result of scattering by a wide variety of structures including
cell membranes, mitochondria, and nuclei. One fundamental difference between speckle
and other noise sources of OCT systems such as detector noise and shot noise is
that, in the absence of sample and probe motion, the speckle noise pattern is static
in time.
The crux of the speckle reduction problem in OCT consists of obtaining uncorrelated
measurements of speckle without significantly affecting spatial resolution. Whilst
digital image processing methods [2–3] have met with some success, they are
fundamentally limited by their reliance on statistical relationships between
neighboring pixels. As such, they typically involve a compromise between the extent
of speckle reduction and the loss in spatial resolution. Spatial averaging in the
transverse dimension could be performed in conjunction with a corresponding decrease
in the focused spot size, but with this method speckle reduction would be achieved
at the expense of a decreased confocal parameter. The angular compounding method of
speckle reduction exploits the decorrelation of speckle with respect to the angle at
which light is backscattered, thereby presenting the potential for skirting spatial
resolution compromises [4–7]. This method involves incoherently averaging
images that are acquired from different backscattering angles. It was recently
demonstrated that angular compounding allows for speckle reduction levels of 8 dB,
as implemented with a massively-parallel detection system [7]. This system had one main drawback, however: the slow (25
Hz) A-line rate of the system rendered it unsuitable for imaging in
vivo.
D. C. Adler, T. H. Ko, and J. G. Fujimoto. “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter.” Opt. Lett. 29, 2878 (2004). [CrossRef]
J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. 42, 1427–1439 (1997). [CrossRef] [PubMed]
A. E. Desjardins, B. J. Vakoc, G. J. Tearney, and B. E. Bouma, “Speckle Reduction in OCT using Massively-Parallel Detection and Frequency-Domain Ranging,” Opt. Express 14, 4736–4745 (2006). [CrossRef] [PubMed]
We present a novel method for acquiring OCT images that allows for both high levels
of speckle reduction by means of angular compounding and high A-line rates. The
number of angular images acquired by the system could be tuned in order to achieve a
compromise between imaging speed and the level of speckle reduction. As a system
suitable for imaging in vivo, it represents a significant
development from the parallel-detection approach to angle-resolved OCT.
2. Imaging System
2.1 Interferometer
The angle-resolved OCT system consisted of a fiber-optic interferometer, a light
source, and detection electronics [Fig. 1(a)]. The interferometer directed light
simultaneously to a transmissive sample arm, which delivered and received light
from the sample, and to a transmissive reference arm. The light source was a
wavelength-swept laser. Interference between light from the reference and sample
arms was detected as a function of source wavelength with a balanced,
polarization-diverse detection circuit [8].
S. H. Yun, G. J. Tearney, B. J. Vakoc, M. Shishkov, W. Y. Oh, A. E. Desjardins, M. J. Suter, R. C. Chan, J. A. Evans, I-K Yang, N. S. Nishioka, J. F. de Boer, and B. E. Bouma, “Comprehensive volumetric optical microscopy in vivo ,” Nat. Med. 12, 1429–1433 (2006). [CrossRef] [PubMed]
Optics at the head of the sample arm allowed for light from different
backscattering angles to be detected selectively (Fig. 1b). The light beam incident on the sample was
focused by an achromatic doublet lens with a diameter of 25 mm and a focal
length of 35 mm. The spatial intensity profile of the collimated beam
originating from collimator C1 in the sample arm was measured directly using a
CCD. From this profile, the transverse spot size
(1/e
2) and confocal parameter in air were calculated
to be 14.3 μm and 0.984 mm, respectively. Light reflected from the
sample was diverted by a beam splitter; subsequently, it was deflected by the
galvanometer mirror M1 (Cambridge Technology) that was placed at the foci of two
achromatic doublet lenses. A collimator placed after the second lens served to
couple light back into the interferometer. As such, there was a direct
correspondence between the angle of M1 and the backscattering angle from which
the received light originated. The angular deflection of M1 was chosen so that
light backscattered in the range of 172 to 188 degrees was collected. Deflection
of the sample arm light transversely across the sample was performed by a second
galvanometer mirror M2 (Cambridge Technology) that was scanned with a saw-tooth
waveform. The deflections of M1 and M2 were synchronized with data acquisition
so that individual cycles of M1 and M2 corresponded to integral numbers of
A-lines.
Fig. 1. Angle Resolved OCT System Schematic (a) and sample arm optics (b). PC:
polarization controller; Circ: circulator; C: collimator; P: polarizer;
PS: polarization splitter; BD: balanced receiver; SOA: semiconductor
optical amplifier; DG: diffraction grating; BS: beam splitter; M:
mirror; L: achromatic doublet lens. L1: f = 35 mm; L2:
f = 50 mm; L3: f = 35mm; L4:
f = 75 mm. The dashed region in (b) is oriented
perpendicularly with respect to the plane of the interferometer.
The laser source employed a semiconductor optical amplifier and an intra-cavity
filter [9]. It generated polarized light with an output power of 25
mW averaged across the tuning range of 1212 to 1347 nm. The intra-cavity filter
of the laser light source contained a 48 facet polygon mirror with a rotation
rate of at 12500 rpm (Lincoln Laser), resulting in an A-line rate of 10 kHz. The
filter employed a double-pass configuration in which light was deflected from
the polygon mirror onto a stationary mirror prior to retracing its path [10]. The average reference arm power incident on individual
detectors within the balanced receivers (Thorlabs PDB-110C) was 20
μW. The system sensitivity was 101 dB, as measured from an axial
reflectivity profile of an attenuated mirror corresponding to a single
backscattering angle. The penetration depth corresponding to this image is
expected to be lower than that expected from a conventional OFDI system with a
sensitivity of 110 dB [11], due in part to the loss of 6 dB in sensitivity from
the beam splitter within the sample arm. The Nyquist-limited ranging depth was
2.85 mm in air.
S. H. Yun, C. Boudoux, G. J. Tearney, and B. E. Bouma, “High-speed wavelength-swept semiconductor laser with a polygon-scanner-based wavelength filter,” Opt. Lett. 28, 1981–1983 (2003). [CrossRef] [PubMed]
W. Y. Oh, S. H. Yun, G. J. Tearney, and B. E. Bouma. 115 kHz tuning repetition rate ultrahigh-speed wavelength-swept semiconductor laser. Opt. Lett. 30, 3159–3161 (2005). [CrossRef] [PubMed]
S. Yun, G. Tearney, J. de Boer, N. Iftimia, and B. Bouma, “High-speed optical frequency-domain imaging,” Opt. Express 11, 2953–2963 (2003) [CrossRef] [PubMed]
Analog input was digitized at 12 bits by a digital acquisition card operating at
10 MS/s per channel (National Instruments PCI-6115). Triggers for A-line
acquisition were obtained optically: a fraction of the light from the reference
arm was diverted to a fiber Bragg grating, and the reflected light was detected
by an InGaAs photoreceiver (New Focus 1811). For each A-line, 950 wavelength
samples were acquired. The signals from the photoreceiver were converted to
trigger pulses using a custom circuit that allowed for variation of the pulse
delay. Imaging was performed with 40 angular samples per spatial volume element.
With a total of 86,400 A-lines acquired for the image, each angle-resolved image
comprised 2,160 A-lines.
2.2 Image Reconstruction
For each backscattering angle, axial reflectivity profiles were obtained by
Fourier transform of the interference signals. The data processing steps that
were performed prior to Fourier transform were background subtraction, mapping
from wavelength- to k-space, digital dispersion correction
[12], and multiplication with a Hamming window [11, 12]. These steps were performed using compiled code written
in C; subsequent processing was performed with Matlab (Mathworks).
S. Yun, G. Tearney, J. de Boer, N. Iftimia, and B. Bouma, “High-speed optical frequency-domain imaging,” Opt. Express 11, 2953–2963 (2003) [CrossRef] [PubMed]
B. J. Vakoc, S. H. Yun, J. F. de Boer, G. J. Tearney, and B. E. Bouma, “Phase-resolved optical frequency domain imaging,” Opt. Express 13, 5483–5493 (2005). [CrossRef] [PubMed]
Images corresponding to different backscattering angles were offset from one
another in the axial dimension, due to slight misalignments in the sample arm.
As a calibration step, this offset was measured for each angular image. Prior to
angular compounding, the inverses of the measured offsets were digitally applied
to the angular images so that they were aligned in the axial dimension with the
reference image. The collection efficiency of the sample arm corresponding to
each backscattering angle was measured, and was employed as a normalizing factor
before angular compounding was performed.
Angular compounding consisted of averaging the squared magnitudes of the
reconstructed complex reflectance signals acquired at different backscattering
angles. Reflectivity magnitudes were mapped to a logarithmic scale to generate
two-dimensional images. For all angular compounding operations, angles that were
evenly spaced across the full angular range of the system were chosen. For
instance, for compounding across 3 angular samples, angles of -172 degrees, 180
degrees, and 188 degrees were chosen. Images derived without angular compounding
corresponded to a backscattering angle of 180 degrees.
3. Signal-to-Noise Ratio and Angular Compounding
The signal-to-noise ratio (SNR) relates the mean power reflectance,
⟨I⟩, to the standard deviation of the power reflectance, σ
I
: SNR = ⟨I⟩/σ
I
, where the average is performed over pixels from an optically homogeneous
region [7]. We note that the SNR differs from the system sensitivity,
which is the minimum detectable reflectance in the absence of speckle. The dominant
contribution to signal variation within scattering regions is typically speckle.
When angular compounding is performed, the SNR is increased, and the magnitude of
the increase is determined by the extent to which angular samples are correlated. We
emphasize that the SNR differs from the system sensitivity, as the latter is a
measure of the minimum detectable reflectance in the absence of speckle.
A. E. Desjardins, B. J. Vakoc, G. J. Tearney, and B. E. Bouma, “Speckle Reduction in OCT using Massively-Parallel Detection and Frequency-Domain Ranging,” Opt. Express 14, 4736–4745 (2006). [CrossRef] [PubMed]
In this section, we present a computational framework that relates the SNR
improvement resulting from angular compounding to the system optical parameters. It
is constructed from more detailed analyses of OCT speckle statistics that do not
incorporate angular measurements [13]. We employ a linear systems framework, because the measured
backscattered field S and the actual backscattered field
G corresponding to a particular spatial volume element are related
by the convolution operator ⊗:
B. Karamata, K. Hassler, M. Laubscher, and T. Lasser, “Speckle statistics in optical coherence tomography,” J. Opt. Soc. Am. A 22, 593–596 (2005). [CrossRef]
The parameter x is related to the angular backscattering angle
θ and the focal length of the lens
f as x = f
tan(θ). Simply put, it is the radial displacement of
a beam that has been backscattered at angle θ, as
measured on the side of the imaging lens L1 opposite to the sample (Fig. 2). The angular response kernel K is
the field magnitude of the collimation beam, and it is responsible for the
correlations between angular samples. K operates separately on the
real and imaginary parts of G, so that S is
complex-valued. It was measured by directing light from the collection collimator at
an area camera, and calculating the square root of the measured radial intensity
distribution.
Using the linear systems framework above, SNR improvements can be determined
numerically using a numerical simulation consisting of the following steps:
- a) Simulating the speckle field G by drawing n independent and identically-distributed (I.I.D.) random values from a standard normal distribution, independently for real and imaginary parts of G, where n is the number of measured angular data points.
- b) Calculating the effect of the angular response by convolving G with K;
- c) Calculating the power reflectance for each angular data point by taking the magnitude-squared of the n values obtained from step b), so that the resulting probability distribution for a given backscattering angle is exponential;
- d) Calculating the SNR improvements for averages over different numbers of angular data points, ranging from 1 to n. Averages are performed with the results of part c);
- e) Repeating steps a) to d) and averaging the results. The number of repetitions was chosen to be 10,000.
The SNR is known to increase in proportion to the square root of the number of
uncorrelated angular samples [1]. For the case where angular correlations are present, we
define the effective number of uncorrelated angles as the square
root of the SNR improvement obtained when all angular data points are included in
angular compounding. In this simulation, the number of effective angles is
determined by two factors: the diameter of the lens, which determined the minimum
and maximum values of x, and the width of the collection beam, as
determined by K. We note that there were no arbitrary parameters in
the simulation.
J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999). [CrossRef]
4. Imaging
4.1 Phantom
A solid optical phantom was constructed by mixing TiO2 powder
(Sigma-Aldrich, 634662) with two-part silicone (GE, RTV 615). Centrifuging was
performed prior to curing in order to remove large TiO2 clusters.
This phantom was well suited to measurements of SNR and angular correlation
measurements due to its homogeneity.
A representative angular distribution for a resolution element located 20
μm beneath the surface is shown in Fig. 3(a), from which correlations between adjacent
angular samples are readily observed. The corresponding cross-correlation
function is shown in Fig. 3(b). The SNR obtained from reflectivities
corresponding to a single polarization channel was found to increase from 1.2
with a single angular sample, to a 2.6 with compounding across 40 angular
samples. It follows that the effective number of uncorrelated angles for this
system is 6.8. The relationship between the SNR and the number of compounded
samples was in good agreement with the numerical simulations described in
Section 3 (Fig. 4). The measured increase in SNR was slightly lower
than the simulated values for small numbers of compounded angles, however, which
suggests that correlations between reflectivities widely separated in angular
space were higher than those predicted by the model. That the measured number of
effective angles was in agreement with simulation indicates that the SNR
increase was limited by the lens aperture and the collection beam width, rather
than by intrinsic angular correlations of the backscattered light. We can
expect, therefore, that a greater increase in SNR could be obtained either by
increasing the lens aperture or by decreasing the collection beam width.
4.2 Human Skin
For imaging of human skin (finger tip) in vivo, a glass window
angled at 40 degrees with respect to the incident beam was placed beneath the
focusing lens to provide mechanical stabilization and to reduce specular
reflections. Angular compounding resulted in a dramatic increase in image
quality (Fig. 5). In terms of spatial resolution, the image
acquired from a single angular sample [Fig. 5(a)] is equivalent to the one that would be
obtained from a conventional OFDI system. As the number of compounded angular
samples was increased [Figs. 5(b)–5(f)], the boundary between the
stratum corneum and the papillary dermis became more pronounced and the sweat
ducts within the stratum corneum became more clearly delineated (Fig. 5 insets). Qualitatively, there was no appreciable
increase in image quality for angular compounding performed across more than 7
angles. This result was expected, given that the effective number of
uncorrelated angular samples was found to be 6.7 from the measurements of the
optical phantom.
Fig. 5. OCT images of human finger tip acquired in vivo with no
angular compounding (a); angular compounding with 2(b), 4(c), 7(d),
16(e) and 32 (f) angular samples. Speckle reduction with angular
compounding enhances the contrast between neighboring structures, as
highlighted by the insets corresponding to the dashed region in (a). The
scale bar corresponds to 250 μm. The transverse extension of
the image is 7 mm. S: sweat duct emerging from the lower papillary
dermis (PD) into the upper stratum corneum (SC).
The statistical decorrelations observed between consecutive A-lines as the
orientation of the angular scanning mirror was changed derive primarily from
angular decorrelations, rather than from motion of the sample. Two pieces of
evidence support this assertion. First, the measured angular correlations
correspond well to those expected from the statistical model. Secondly,
acquiring images without scanning the galvanometer mirror, but with the same
number of averaged A-lines does not result in speckle reduction for in vivo
imaging of human dermis (Fig. 6). This result suggests that statistical
decorrelations that result from the motion of the sample between consecutive
A-lines are very small relative to those resulting from observing the sample
from different backscattering angles.
Fig. 6. OCT images of human finger tip acquired in-vivo with 2160 A-lines (a);
and with 40×2160 = 86400 A-lines (b). In case (b), sets of 40
consecutive A-lines were averaged, yielding 2160 A-lines in the
displayed image. The angular scanning galvanometer was held fixed in
both cases, so that images were obtained from a single angular sample.
The scale bar corresponds to 250 μm. The transverse extension
of the image is 7 mm.
5. Conclusion
The speckle-reduction capabilities of a novel angle-resolved OCT system that enabled
the resolution of backscattered light within an angular range of 172 to 188 degrees
were demonstrated. Discrimination of different backscattering angles was achieved by
means of a galvanometer mirror placed at the focus of a telescope in the sample arm,
so that the backscattering angle of the collected light varied sequentially in time
as the mirror was rotated. By averaging images acquired at different backscattering
angles, an SNR improvement of 3.4 dB was obtained, which resulted in a substantial
improvement in image quality when applied to imaging of human skin in
vivo. The magnitude of the SNR improvement and the correlations between
angular samples were well predicted by a statistical model of speckle that
incorporated the system optical parameters.
In this system, the number of angular samples corresponding to particular spatial
volume elements was a tunable parameter, which contrasts with the previously
demonstrated angle-resolved OCT system that employed parallel-detection. With the
parallel-detection system, A-lines from all angles were acquired simultaneously at a
rate of 25 Hz; with this sequential-detection system, individual A-lines were
acquired at 10 kHz, and an effective A-line rate of 250 Hz was obtained with 40
angular samples. As a result, the artifacts and signal attenuation that result from
sample arm movement were significantly reduced in the sequential-detection system,
enabling imaging of biological samples in vivo. By utilizing recently developed OCT
systems having A-line rates in the hundreds of kilohertz [10, 14], angle-resolved OCT with sequential-detection may
ultimately enable real-time speckle reduction performed at video rates. As such,
angular compounding could be leveraged as a means of converting advances in A-line
speed into improvements in speckle reduction without sacrificing the image frame
rate.
W. Y. Oh, S. H. Yun, G. J. Tearney, and B. E. Bouma. 115 kHz tuning repetition rate ultrahigh-speed wavelength-swept semiconductor laser. Opt. Lett. 30, 3159–3161 (2005). [CrossRef] [PubMed]
R. Huber, M. Wojtkowski, and J. G. Fujimoto, “Fourier Domain Mode Locking (FDML): A new laser operating regime and applications for optical coherence tomography,” Opt. Express 14, 3225–3237 (2006). [CrossRef] [PubMed]
Acknowledgments
This research was supported in part by the National Institutes of Health, contract
R01 CA103769, and by the Terumo Corporation.
References and links
J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999). [CrossRef] | |
D. C. Adler, T. H. Ko, and J. G. Fujimoto. “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter.” Opt. Lett. 29, 2878 (2004). [CrossRef] | |
D. L. Marks, T. S. Ralston, and S. A. Boppart, “Speckle reduction by I-divergence regularization in optical coherence tomography,” J. Opt. Soc. Am. A 22, 2366–2371 (2005). [CrossRef] | |
J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. 42, 1427–1439 (1997). [CrossRef] [PubMed] | |
M. Bashkansky and J. Reintjes, “Statistics and reduction of speckle in optical coherence tomography,” Opt. Lett. 25, 545–547 (2000). [CrossRef] | |
N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt. 8, 260–263 (2003). [CrossRef] [PubMed] | |
A. E. Desjardins, B. J. Vakoc, G. J. Tearney, and B. E. Bouma, “Speckle Reduction in OCT using Massively-Parallel Detection and Frequency-Domain Ranging,” Opt. Express 14, 4736–4745 (2006). [CrossRef] [PubMed] | |
S. H. Yun, G. J. Tearney, B. J. Vakoc, M. Shishkov, W. Y. Oh, A. E. Desjardins, M. J. Suter, R. C. Chan, J. A. Evans, I-K Yang, N. S. Nishioka, J. F. de Boer, and B. E. Bouma, “Comprehensive volumetric optical microscopy in vivo ,” Nat. Med. 12, 1429–1433 (2006). [CrossRef] [PubMed] | |
S. H. Yun, C. Boudoux, G. J. Tearney, and B. E. Bouma, “High-speed wavelength-swept semiconductor laser with a polygon-scanner-based wavelength filter,” Opt. Lett. 28, 1981–1983 (2003). [CrossRef] [PubMed] | |
W. Y. Oh, S. H. Yun, G. J. Tearney, and B. E. Bouma. 115 kHz tuning repetition rate ultrahigh-speed wavelength-swept semiconductor laser. Opt. Lett. 30, 3159–3161 (2005). [CrossRef] [PubMed] | |
S. Yun, G. Tearney, J. de Boer, N. Iftimia, and B. Bouma, “High-speed optical frequency-domain imaging,” Opt. Express 11, 2953–2963 (2003) [CrossRef] [PubMed] | |
B. J. Vakoc, S. H. Yun, J. F. de Boer, G. J. Tearney, and B. E. Bouma, “Phase-resolved optical frequency domain imaging,” Opt. Express 13, 5483–5493 (2005). [CrossRef] [PubMed] | |
B. Karamata, K. Hassler, M. Laubscher, and T. Lasser, “Speckle statistics in optical coherence tomography,” J. Opt. Soc. Am. A 22, 593–596 (2005). [CrossRef] | |
R. Huber, M. Wojtkowski, and J. G. Fujimoto, “Fourier Domain Mode Locking (FDML): A new laser operating regime and applications for optical coherence tomography,” Opt. Express 14, 3225–3237 (2006). [CrossRef] [PubMed] |
OCIS Codes
(120.3890) Instrumentation, measurement, and metrology : Medical optics instrumentation
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.4500) Medical optics and biotechnology : Optical coherence tomography
ToC Category:
Medical Optics and Biotechnology
History
Original Manuscript: February 13, 2007
Revised Manuscript: April 2, 2007
Manuscript Accepted: April 5, 2007
Published: May 4, 2007
Virtual Issues
Vol. 2, Iss. 6 Virtual Journal for Biomedical Optics
Citation
A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, "Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction," Opt. Express 15, 6200-6209 (2007)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-15-10-6200
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References
- J. M. Schmitt, S. H. Xiang, and K. M. Yung, "Speckle in optical coherence tomography," J. Biomed. Opt. 4, 95-105 (1999). [CrossRef]
- D. C. Adler, T. H. Ko, and J. G. Fujimoto. "Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter." Opt. Lett. 29, 2878 (2004). [CrossRef]
- D. L. Marks, T. S. Ralston, and S. A. Boppart, "Speckle reduction by I-divergence regularization in optical coherence tomography," J. Opt. Soc. Am. A 22, 2366-2371 (2005). [CrossRef]
- J. M. Schmitt, "Array detection for speckle reduction in optical coherence microscopy," Phys. Med. Biol. 42, 1427-1439 (1997). [CrossRef] [PubMed]
- M. Bashkansky and J. Reintjes, "Statistics and reduction of speckle in optical coherence tomography," Opt. Lett. 25, 545-547 (2000). [CrossRef]
- N. Iftimia, B. E. Bouma, and G. J. Tearney, "Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding," J. Biomed. Opt. 8, 260-263 (2003). [CrossRef] [PubMed]
- A. E. Desjardins, B. J. Vakoc, G. J. Tearney, and B. E. Bouma, "Speckle Reduction in OCT using Massively-Parallel Detection and Frequency-Domain Ranging," Opt. Express 14, 4736-4745 (2006). [CrossRef] [PubMed]
- S. H. Yun, G. J. Tearney, B. J. Vakoc, M. Shishkov, W. Y. Oh, A. E. Desjardins, M. J. Suter, R. C. Chan, J. A. Evans, I-K Yang, N. S. Nishioka, J. F. de Boer, and B. E. Bouma, "Comprehensive volumetric optical microscopy in vivo," Nat. Med. 12, 1429-1433 (2006). [CrossRef] [PubMed]
- S. H. Yun, C. Boudoux, G. J. Tearney, and B. E. Bouma, "High-speed wavelength-swept semiconductor laser with a polygon-scanner-based wavelength filter," Opt. Lett. 28, 1981-1983 (2003). [CrossRef] [PubMed]
- W. Y. Oh, S. H. Yun, G. J. Tearney, and B. E. Bouma, 115 kHz tuning repetition rate ultrahigh-speed wavelength-swept semiconductor laser, Opt. Lett. 30, 3159-3161 (2005). [CrossRef] [PubMed]
- S. Yun, G. Tearney, J. de Boer, N. Iftimia, and B. Bouma, "High-speed optical frequency-domain imaging," Opt. Express 11, 2953-2963 (2003) [CrossRef] [PubMed]
- B. J. Vakoc, S. H. Yun, J. F. de Boer, G. J. Tearney, and B. E. Bouma, "Phase-resolved optical frequency domain imaging," Opt. Express 13, 5483-5493 (2005). [CrossRef] [PubMed]
- B. Karamata, K. Hassler, M. Laubscher, and T. Lasser, "Speckle statistics in optical coherence tomography," J. Opt. Soc. Am. A 22, 593-596 (2005). [CrossRef]
- R. Huber, M. Wojtkowski, and J. G. Fujimoto, "Fourier Domain Mode Locking (FDML): A new laser operating regime and applications for optical coherence tomography," Opt. Express 14, 3225-3237 (2006). [CrossRef] [PubMed]
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