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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 11 — Jun. 3, 2013
  • pp: 13758–13772
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Snapshot 3D optical coherence tomography system using image mapping spectrometry

Thuc-Uyen Nguyen, Mark C Pierce, Laura Higgins, and Tomasz S Tkaczyk  »View Author Affiliations


Optics Express, Vol. 21, Issue 11, pp. 13758-13772 (2013)
http://dx.doi.org/10.1364/OE.21.013758


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Abstract

A snapshot 3-Dimensional Optical Coherence Tomography system was developed using Image Mapping Spectrometry. This system can give depth information (Z) at different spatial positions (XY) within one camera integration time to potentially reduce motion artifact and enhance throughput. The current (x,y,λ) datacube of (85×356×117) provides a 3D visualization of sample with 400 μm depth and 13.4 μm in transverse resolution. Axial resolution of 16.0 μm can also be achieved in this proof-of-concept system. We present an analysis of the theoretical constraints which will guide development of future systems with increased imaging depth and improved axial and lateral resolutions.

© 2013 OSA

1. Introduction

Although Fourier-Domain OCT (FD-OCT) is now firmly established and widely used, both spectral-domain and swept-source/optical frequency domain imaging embodiments still require scanning elements. Moving parts can limit the system’s compactness, which is an important factor in systems miniaturized for endoscopic applications, and can introduce motion artifacts. The artifacts caused by movements and vibrations of the sample or of the scanning mechanism itself can result in blurred or non-continuous images, and potentially inaccurate clinical interpretation [4

4. R. de Kinkelder, J. Kalkman, D. J. Faber, O. Schraa, P. H. B. Kok, F. D. Verbraak, and T. G. van Leeuwen, “Heartbeat-induced axial motion artifacts in optical coherence tomography measurements of the retina.” Invest Ophthalmol Vis Sci 52, 3908–3913 (2011) [CrossRef] [PubMed] .

]. This effect is worsened when the samples are dynamic objects [5

5. J. F. de Boer, B. Cense, B. H. Park, M. C. Pierce, G. J. Tearney, and B. E. Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography.” Opt Lett 28, 2067–2069 (2003) [CrossRef] [PubMed] .

, 6

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

]. Snapshot imaging modalities capture light in parallel instead of raster scanning a focused beam, potentially allowing imaging with reduced illumination power or increased frame rate [7

7. N. Hagen, R. T. Kester, L. Gao, and T. S. Tkaczyk, “Snapshot advantage: a review of the light collection improvement for parallel high-dimensional measurement systems.” Opt Eng 51(2012) [CrossRef] .

]. Efforts to reduce the number of scanning elements has led to line-illumination [8

8. Y. Chen, A. D. Aguirre, P.-L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S.-W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology.” Endoscopy 39, 599–605 (2007) [CrossRef] [PubMed] .

, 9

9. Y. Nakamura, S. Makita, M. Yamanari, M. Itoh, T. Yatagai, and Y. Yasuno, “High-speed three-dimensional human retinal imaging by line-field spectral domain optical coherence tomography,” Opt. Express 15, 7103–7116 (2007) [CrossRef] [PubMed] .

] and full-field [10

10. A. Dubois, J. Moreau, and C. Boccara, “Spectroscopic ultrahigh-resolution full-field optical coherence microscopy.” Opt Express 16, 17082–17091 (2008) [CrossRef] [PubMed] .

] approaches in OCT. The former technique images a line on the sample and reference mirror, thus requires only one scanning axis to obtain a 3D structure [11

11. B. Grajciar, M. Pircher, A. Fercher, and R. Leitgeb, “Parallel Fourier domain optical coherence tomography for in vivo measurement of the human eye.” Opt Express 13, 1131–1137 (2005) [CrossRef] [PubMed] .

13

13. S. Witte, M. Baclayon, E. J. G. Peterman, R. F. G. Toonen, H. D. Mansvelder, and M. L. Groot, “Single-shot two-dimensional full-range optical coherence tomography achieved by dispersion control.” Opt Express 17, 11335–11349 (2009) [CrossRef] [PubMed] .

]. Full-Field OCT as exempli-fied by AC Boccara et al. can provide real time in vivo imaging without lateral scanning, albeit with acquisition of multiple phase-shifted images, rather than single-shot [14

14. K. Grieve, A. Dubois, M. Simonutti, M. Paques, J. Sahel, J.-F. L. Gargasson, and C. Boccara, “In vivo anterior segment imaging in the rat eye with high speed white light full-field optical coherence tomography.” Opt Express 13, 6286–6295 (2005) [CrossRef] [PubMed] .

]. Subhash et al demonstrated a version of FF-OCT where the requisite phase-stepped images are all captured in a single camera snap-shot by distributing each image to a separate region of the image sensor [15

15. M. S. Hrebesh, R. Dabu, and M. Sato, “In vivo imaging of dynamic biological specimen by real-time single-shot full-field optical coherence tomography,” Opt Comm 282, 674–683 (2009) [CrossRef] .

, 16

16. H. M. Subhash, “Review article: Full-field and single-shot full-field optical coherence tomography: A novel technique for biomedical imaging applications,” Advances in Optical Technologies 2012(2012) [CrossRef] .

]. This method could, therefore, provide snapshot en face (XY) FF-OCT imaging at a single axial (Z) location; however, generation of a 3D volume required recording of multiple camera acquisitions. To the best of our knowledge, the IMS-OCT approach introduced here is the first implementation of OCT which can provide a complete 3D (XYZ) volume with a single camera snap-shot.

Hyperspectral imaging methods capture spectral information at each spatial (XY) location in a 2D scene, but have traditionally required spectral or spatial scanning to acquire the full spectral datacube. Several snapshot hyperspectral imagers have been developed and commercialized by different research groups and companies. Among the prominent techniques are Computed Tomography Imaging Spectrometer (CTIS) [17

17. B. K. Ford, C. E. Volin, S. M. Murphy, R. M. Lynch, and M. R. Descour, “Computed tomography-based spectral imaging for fluorescence microscopy.” Biophys J 80, 986–993 (2001) [CrossRef] [PubMed] .

,18

18. B. Ford, M. Descour, and R. Lynch, “Large-image-format computed tomography imaging spectrometer for fluorescence microscopy.” Opt Express 9, 444–453 (2001) [CrossRef] [PubMed] .

], Coded Aperture Snapshot Spectral Imager (CASSI) [19

19. C. A. Fernandez, A. Wagadarikar, D. J. Brady, S. C. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” 7184, 71840Z–71840Z-11 (2009).

, 20

20. C. F. Cull, K. Choi, D. J. Brady, and T. Oliver, “Identification of fluorescent beads using a coded aperture snapshot spectral imager.” Appl Opt 49, B59–B70 (2010) [CrossRef] [PubMed] .

], Image-Replicating Imaging Spectrometer (IRIS) [21

21. A. Gorman, D. W. Fletcher-Holmes, and A. R. Harvey, “Generalization of the Lyot filter and its application to snapshot spectral imaging.” Opt Express 18, 5602–5608 (2010) [CrossRef] [PubMed] .

], HyperPixel Array TM Imager (Bodkin Design & Engineering, LLC) [22

22. A. Bodkin, A. Sheinis, A. Norton, J. Daly, C. Roberts, S. Beaven, and J. Weinheimer, eds., Video-rate chemical identification and visualization with snapshot hyperspectral imaging, vol. 8374 (2012).

], HyperVideo (Opto-Knowledge Systems, Inc.) [23

23. J. Kriesel, G. Scriven, N. Gat, S. Nagaraj, P. Willson, and V. Swaminathan, eds., Snapshot hyperspectral fovea vision system (HyperVideo)(2012).

] and the IMS which will be discussed in Section 2. CTIS and CASSI require extensive computations which slow down the acquisition and data reconstruction, and generate computational artifacts, while IRIS has low light throughput and is limited by its prism [24

24. L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy.” Opt Express 18, 14330–14344 (2010) [CrossRef] [PubMed] .

]. Meanwhile, the HyperPixel Array, HyperVideo and IMS produce direct spatial and spectral imaging by separating an image into spatially different zones without any data reconstruction. Due to its intrinsic pupil geometry, the HyperPixel Array Imager is limited in its number of spectral samples. As a result, this technique is unsuitable for OCT which requires high spectral resolution to obtain clinically significant imaging depths. The second technique, HyperVideo, can provide a higher spectral resolution which is more desirable for OCT applications. However, since this technique relies on a specially designed fiber bundle, the spatial sampling is directly limited by the number of elements in the bundle.

We previously developed a snapshot hyperspectral imaging platform based on principles of imaging mapping/slicing spectrometery (IMS) [25

25. L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact image slicing spectrometer (ISS) for hyperspectral fluorescence microscopy.” Opt Express 17, 12293–12308 (2009) [CrossRef] [PubMed] .

]. Here, we report on the development and use of IMS to acquire a full 3D OCT volume in a single snapshot image capture. The lateral (XY) dimension is acquired by use of wide-field Koehler illumination, while depth (Z) information is encoded in the interference fringe pattern captured by the IMS system’s spectral (λ) dimension. To the best of our knowledge, this is the first demonstration of a snapshot 3D-OCT imaging using a hyperspectral imaging technique to provide volumetric data. This system also has the capability of increasing spectral sampling through system redesign.

2. Principles

Fig. 1 The concept of combining Full-field OCT (FF-OCT) and IMS systems to develop snapshot 3D-OCT. A low coherence source travels to both sample and reference arms. Back-scattered light creates interference and is fed into the the IMS system. The mapper slices the image and regroups different regions into separate pupils. A large camera captures spectral and spatial data imaged by a lenslet array.

3. System Design

3.1. Inteferometry Arm

Fig. 2 System layout. (a): System schematic. BS: beam splitter, IO: interferometry objective, LA: lenslet array, PP: pupil plane, RC: reference camera, RA: reference arm, SA: sample arm. (b): Complete system on optical table.

3.2. IMS Arm

Since general IMS modalities have been previously reported in literature [24

24. L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy.” Opt Express 18, 14330–14344 (2010) [CrossRef] [PubMed] .

28

28. R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications.” Appl Opt 49, 1886–1899 (2010) [CrossRef] [PubMed] .

], key redesigns to meet OCT imaging’ criteria are highlighted.

Different from other IMS congurations [25

25. L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact image slicing spectrometer (ISS) for hyperspectral fluorescence microscopy.” Opt Express 17, 12293–12308 (2009) [CrossRef] [PubMed] .

], this OCT-adapted IMS system has the mapper positioned perpendicular to the incoming beam to achieve a uniform focal plane across the mapper surface. This setup reduces sensitivity to artifacts like sub-field image vignetting and pupil plane distortions, thus simplifying the mapper facet angle calculations [29

29. L. S. Gao and T. S. Tkaczyk, “Correction of vignetting and distortion errors induced by two-axis light beam steering,” Optical Engineering 51(2012) [CrossRef] .

]. In addition, this configuration minimizes adjacent facet blockage, as individual facets have different heights, which can potentially block parts of the light from other facets if the mapper is placed at an angle to the incoming beam.

Each facet deflects light to different angles toward the collecting lens L4 (f=80mm). Lens L4 organizes the high NA incoming beams into different pupils, with the specific destination pupil depending on the mapper’s facet tilts. A beam expander consisting of two lenses, a 2” diameter, 50 mm focal length (L5) and a 3” diameter, 200 mm focal length (L6) lens, is used to match the pupil array size to the image sensor dimensions without clipping of the large array.

4. Image mapper development

4.1. Mapper fabrication method

Fabricated in-house, the image mapper is made of high purity aluminum (5N 99.999%) for high malleability and reflectivity. The earlier mappers used in IMS were fabricated using a raster-fly cutting technique on a four-axis Nanotech Ultra Precision milling machine [24

24. L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy.” Opt Express 18, 14330–14344 (2010) [CrossRef] [PubMed] .

]. Here we used a ruling technique which has been shown recently to exhibit several advantages over raster-fly cutting [30

30. A. D. Elliott, L. Gao, A. Ustione, N. Bedard, R. Kester, D. W. Piston, and T. S. Tkaczyk, “Real-time hyperspectral fluorescence imaging of pancreatic β-cell dynamics with the image mapping spectrometer (IMS).” J Cell Sci (2012) [CrossRef] .

].

Fig. 3 Image mapper fabrication. (a): Mapper in fabrication. The substrate is mounted on the Nanotech milling machine. Two tools are placed on spindle prior to cutting facets. (b): Reflection of ruler’s straight edge on the finished mapper. (c): Mapper looking from the top. Different facet tilts are shown as variations in depth of cuts. (d): Examination of mapper’s facets with white-light interferometer. (e): Mapper looking from the front. (f): Enlarged section of mapper looking from the front showing finer cuts for individual facets.

For fabrication, the aluminum substrate is mounted on a stage which can be translated along the machine’s y axis as shown in Fig. 3(a). To obtain sub-micron accuracy in tilt angles and surface flatness across each facet, two tools are mounted on the machine’s spindle. For the initial rough cuts, a carbide tool creates seventeen 1.5 mm wide passes across the 1” square substrate by maintaining the carbide tip stationary and orthogonal to the mapper substrate. During that time, the mapper substrate moves along the y axis with depth (x) values varying along the pathway. For the fine cuts, the machine spindle creates pre-programmed tilts (x-tilts) before the 75-μm diamond tool cuts into the substrate to create 20 uniform 75-μm wide facets, within each 1.5 mm wide carbide-tool pass. In a similar fashion to the rough cut, the mapper substrate travels across the stationary and tilted diamond tip with very fine cutting depths, ranging from 20 μm down to 2 μm in multiple iterations.

While the IMS-OCT mapper is designed to have 300 facets, each 75 μm in width, the actual fabricated component includes 40 extra facets as a safety factor in fabrication, and also to enable testing of the system in alternative configurations. As a result, some sub-fields contain images from 3 facets while others have 4. Divided into identical ”blocks” of 100 facets, the entire mapper with 17 rough-cut passes thus comprises 3.4 blocks. Each facet in a single block has a unique two-dimensional angle to deflect light towards the collecting lens. Figure 3(b) shows a ruler’s straight edge being reflected as a zig-zag pattern on the 17 rough-cut passes; a few of the individual facets can be seen in the white-light interferometry image in Fig. 3.

4.2. Mapper design and pupil distribution

Each block of 100 facets is tilted so that light from the interferometer is reflected into 35 sub-pupils [Fig. 4]. In this first-generation design, we collect light from only every 4th facet, i.e. facets 1, 5, 9 ... [Fig. 4(b–c)]; light from the remaining facets is discarded outside the lenslet array in order to maintain enough void space in between pupils for subsequent dispersion. This results in 85 out of the 340 facets being used to direct light from the OCT interferometer to the camera. Starting from one end of the mapper, the first 20 facets share the same y-tilt and therefore redirect light onto the same horizontal row at the lenslet array. Facets spaced 20 steps apart (eg. facets 1 and 21 in Fig. 4(c)) have the same x-tilt and thus, redirect light to a common column. This geometry is repeated across the entire surface of the mapper such that the corresonding facets within each block (facets 1, 101, 201 ...) have identical x and y tilts, and therefore direct light to the same sub-pupil. Since facets 1, 101 and 201 are 100 facets × 75 μm = 7.5 mm apart at the mapper, this distance between the images of facets 1 and 101 at the image plane creates the necessary empty space to be filled in with later dispersion from the diffraction grating.

Fig. 4 Mapper facet and pupil distribution. (a): Facet tilt directions relative to mapper. (b): Pupil distribution from one block of mapper (100 facets). Facets whose numbers are not shown are discarded in the leftmost and rightmost columns. (c): Grouping and order of facets. Facet of the same y-tilt correspond to light grouped in the same row; and those of the same x-tilt correspond to the same columns. Thus two facets which are 100 facets apart have the exact same x and y tilts.

There are two categories of cross-talk occurring in the system. The first arises from diffraction due to the 75-μm wide mapper’s facets, leading to light leaking from one sub-pupil to neighboring sub-pupils. We term this effect spatial cross-talk[28

28. R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications.” Appl Opt 49, 1886–1899 (2010) [CrossRef] [PubMed] .

]. With the use of a pupil mask in the pupil array plane, the spatial cross-talk level was reported to be 6% [26

26. L. Gao, N. Bedard, N. Hagen, R. T. Kester, and T. S. Tkaczyk, “Depth-resolved image mapping spectrometer (IMS) with structured illumination.” Opt Express 19, 17439–17452 (2011) [CrossRef] [PubMed] .

]. This level strongly depends on the surface quality of the mapper’s facets and the sub-pupil separation. When previously using the raster-fly cutting technique, the mapper facets were not perfectly flat, but had an optical power which broadened the beam and increased light leaking [28

28. R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications.” Appl Opt 49, 1886–1899 (2010) [CrossRef] [PubMed] .

]. The new ruling technique used in this paper achieved facet flatness in the sub-micron range, ensuring that cross-talk caused by facets’ non-uniformity is minimized. The second type of cross-talk (spectral), arises from the dispersion of individual sub-images within the tightly-packed array, with the red end of one spectrum potentially overlapped with the blue end of the next. To minimize spectral cross-talk, a band-pass filter (OD6) was inserted into the system, leading to spectral leakage in the 0.001% range.

5. Data processing

5.1. Data acquisition

The Apogee camera is connected to a laptop via a USB cable and controlled with the LabVIEW 2009 environment. For alignment and other fast acquisition purposes, the camera can bin images prior to acquisition and/or capture 12-bit images. Otherwise, operating in snap-shot mode, the camera can produce a full-frame 16-bit image containing 4096×4096 pixels. The image is stored and opened in Matlab for further data processing. All images shown in this manuscript were acquired with an exposure time of 125 ms.

5.2. Calibration

Unlike many other hyperspectral modalities, IMS does not demand extensive computation requirements to generate spectrally-resolved images [31

31. I. Abdulhalim, “Competence between spatial and temporal coherence in full field optical coherence tomography and interference microscopy,” Journal of Optics A: Pure and Applied Optics 8(2006) [CrossRef] .

]. Post-processing for IMS-OCT includes one-time data extraction and alignments, followed by standard spectral-domain OCT calibration.

IMS calibration

IMS calibration focuses on rearranging the sub-images into the correct positions, including (1) extracting the sub-images from the raw 2D image, (2) calibrating the dispersed spectra for every sub-image, (3) aligning and correcting all sub-images and (4) performing flat-field correction. The flowchart of the calibration steps is shown in Fig. 5.

Fig. 5 Initial calibration steps. This one-time calibration series is to performed to convert the raw 2D image into a (x,y,λ) datacube for subsequent image acquisitions.

As light from the 25 sub-pupils is recorded by the CCD sensor, the raw image includes 85 vertically oriented sub-images of the mapper facets, each of which is horizontally dispersed by the diffraction grating. Initial data processing starts with subtracting the background density to remove stray light by blocking signals from both reference and sample arms, then extracting individual sub-images and creating a 3D matrix of (x,y,n) where x is the transverse data obtained by stacking multiple sub-images, y is the image along the length of each facet, and n is the dispersed spectral information in pixels [Fig. 5(a)]. All of the blank space which separates the sub-images after dispersion is discarded. Since it is entirely determined by the design of the mapper facets’ tilts, the order of these sub-images is easily redistributed.

After the 85 sub-images are rearranged and corrected to obtain the transverse full-field image, spectral calibration is required. Spectra are recorded by both of the IMS-OCT system and an Ocean Optics spectrometer as a calibrated reference channel. Since dispersion from the IMS grating is approximately linear in wavelength, and the LED light source has a simple Gaussian shape, the wavelength-pixel relationship can be interpolated based on the spectrum measured from the reference spectrometer. This calibration step generates the (x,y,λ) datacube which is ready for OCT calibration [Fig. 5(b)].

An image of a test object containing straight lines is used to vertically align the sub-images [Fig. 5(c)]. Vertical offset and magnification difference among individual sub-images are corrected with a linear approximation, i.e. disregarding the insignificant effects from distortion and magnification variation along one sub-image. Flat-field correction is carried out to compensate for uneven intensity, mostly caused by the mapper facets’ surface variation. Figure 5(d) shows a cross-sectional image at the center wavelength, extracted from the flat-field correction. This non-uniformity of the facets’ reflectivity is used to compensate for the variations for the whole spectrum.

OCT calibration

Given the estimated spectral values from the linear calibration mentioned above, the spectral bins-calibrated wavelength relationship is fitted to a polynomial for finer calibration [Fig. 6(d)]. The spectra are then zero-padded and interpolated so that they are evenly spaced in wavenumber (k) [Fig. 6(e)]. The DC (non-interferometric) component is removed from each set of recorded fringes by subtracting the spectrum obtained when the sample reflector is positioned far beyond the expected imaging depth, i.e. when fringes are not present. This method minimizes the effect of any spectral variations in the light path, effectively removing most of the DC component from the depth profile [Fig. 6(f)]. A Fourier transform of the resampled spectra generates the OCT axial scattering profile (A-line) for each individual spectral line (Fig. 6[g]). The spectral phase obtained from an image of a simple reflector is used to iteratively adjust spectral values based on the process described by Mujat et al.[32

32. M. Mujat, B. H. Park, B. Cense, T. C. Chen, and J. F. de Boer, “Autocalibration of spectral-domain optical coherence tomography spectrometers for in vivo quantitative retinal nerve fiber layer birefringence determination.” J Biomed Opt 12, 041205 (2007) [CrossRef] [PubMed] .

]. The calculated nonlinearity in phase ϕ(k) is removed to compensate for errors in spectrometer calibration or dispersion mismatch between sample and reference arms [33

33. C. Dorrer, “Influence of the calibration of the detector on spectral interferometry,” J. Opt. Soc. Am. B 16, 1160–1168 (1999) [CrossRef] .

, 34

34. L. Lepetit, G. Chriaux, and M. Joffre, “Linear techniques of phase measurement by femtosecond spectral interferometry for applications in spectroscopy,” J. Opt. Soc. Am. B 12, 2467–2474 (1995) [CrossRef] .

]. Since the full spectrum of the LED used here is relatively narrow (50 nm), dispersion mismatch effects are relatively insignificant. A flat mirror was mounted on an axial translation stage to record different sample positions for a one-time depth calibration. The corrected pixel-wavelength assignments and depth scale are then applied on all subsequent data sets. After the one-time calibration steps mentioned above, any raw image taken by the system can be readily processed for fast data reconstruction. A predefined mask extracts the sub-images; and wave-number interpolation is taken place with the known wavelength array. Converting into Fourier space, the depth profiles of all spatial points can be quantitatively reconstructed and visualized.

Fig. 6 OCT calibration steps. a: A segment of a raw sub-image with horizontal features from sample and vertical interferometric fringes. b: One spectral cross-section taken from (a). c: Calibrated spectra corresponding to the raw image in (a). Spectra along the facets form a gradient from black (610 nm) to white (640 nm). d: The initial wavelength-pixel relationship is fitted to a third-order polynomial. e: The calibrated wavelength after zero-padding to 512 data points to prepare for depth reconstruction. f: A spectrum of inteferometric fringes with DC components removed. g: Depth profile reconstructed from the fringes shown in f. h: Relationship between wavelengths and the array indices. For a narrow spectral band such as that used here, this relationship is almost linear.

6. Experiments

6.1. Depth Assessment

Depth analysis is important in the assessment of axial resolution and depth range. Figure 7 shows data from a flat, reflective surface taken from the large 3D datacube at multiple depth positions, as the sample is mounted on a translation stage for this calibration experiment.

Fig. 7 Snapshot 3D-OCT system’s depth assessment. a: Different depth positions of a flat, reflecting mirror mounted on a translation stage. b: Measured axial resolution from one representative transverse location. c: Relationship between peak pixel position and mirror physical position. Note that at the position around 400 μm, peak positions become undetectable, indicating the end of the imaging depth. d: Linear regression of the relationship between peak pixel position and mirror position.

Adjacent positions are 25.4 μm apart [Fig. 7(a)]. After zero-padding and phase linearization processes, the average axial resolution was measured to be 20.9 μm over the depth range [Fig. 7(b)]. Axial resolution of 16.0 μm can be obtained near the zero optical path difference (OPD) position. In addition, the axial position of each coherence peak is plotted against translation stage position in Fig. 7(c). This confirms the expected depth range of approximately 400 μm. The physical depth and pixel value relationship is established and fitted to a linear equation [Fig. 7(c)]. The measured SNR for the coherence peak at a depth of 50 μm was 43dB.

6.2. 3D Visualization

In this first-generation system, the performance is evaluated by imaging a USAF resolution target with clear tape on the front surface to produce 3D structures. The raw image of 4096×4096 pixels can be seen in Fig. 8(a), while both the target’s bars and interferometric fringes due to reflections at the clear tape can be observed in Fig. 8(b).

Fig. 8 Simultaneous spatial and spectral visualization. a: Spatial features from resolution target. b: Interferometric fringes caused by resolution target. c: Interferometric fringes caused by clear tape.

The current system provides a datacube (85×356×127) from 85 facets of 356 pixels in length, being dispersed in 127 pixels. The final image shown in Fig. 9 demonstrates a simple experiment in which 3D structure can be visualized after calibration algorithms are applied. The result was shown in the open-source MicroView 3D Image Viewer (Parallax Innovations).

Fig. 9 3D structure recorded in snapshot mode from the 3D-OCT system. a: Reconstructed structure of clear tape on USAF target. b,c: Its XZ and YZ cross-sections. d: Transverse image from the reference camera.

Multiple surfaces can be observed along the depth in the 3D display as well as in the XZ and YZ cross-sections. Note that the dark bands on the 1st and 3rd surfaces from the right shown in Fig. 9(c) come from the resolution target’s spatial features. The second surface from the right was created by the interference between the tape’s two reflective surfaces, thus indicates the tape’s actual thickness. The bright DC component is left intact in Fig. 9 for illustration.

Fig. 10 System evaluation with simple 3D structural sample. a: A 2D image of an US dime taken with reference camera. b: Corresponding transverse surface acquired with snapshot 3D-OCT system. c: Transverse surfaces at different depths. d: Cross-sections along the depth range.

To investigate the potential for the IMS-OCT system to image biological samples, a 3D volume of a piece of onion was acquired. The power at the sample was measured to be 3.1 mW/cm2. Figure 11(a) shows the regular en face 2D image taken from the reference camera, while Figure 11(b) displays the reconstructed en face image obtained from the OCT system. Five transverse slices in the XY plane at various locations along the axial (Z) axis are shown in Figure 11(c), indicating different structures within the onion depth.

Fig. 11 3D snapshot of a layer of onion placed on top of a highly scattering metal surface. a: Image of a layer of onion (bottom) on a metal surface (top) acquired with the reference camera. b: Transverse surface acquired with snapshot 3D-OCT system. c: Representative transverse sections at different (z) depths.

7. Discussion

7.1. Resolutions, Imaging Depth and Camera Pixel Count

The depth range, axial resolution, and FOV for the system reported here were chosen to enable a first proof-of-concept demonstration of the IMS-OCT concept for 3D volumetric imaging. This setup is able to provide 85 sub-images in 25 sub-fields; each sub-image carries spatial features along one mapper facet’s length as well as the interferometric fringes created from the reference mirror and sample. The lateral resolution (13.4 μm) and depth range (400 μm) meet the expected performance, while the averaged axial resolution of 20.9 μm is slightly larger than the expected value mostly due to the broadening effect along the depth of range in OCT. However, the measured axial resolution near zero OPD position (16.0 μm) where the broadening effect is insignificant meets the theoretical calulation of 15.9 μm.

Fig. 12 Effect of camera pixel count on 3D datacube size for a system operating at 10 μm axial resolution.

7.2. Optical improvements for next system generation

8. Conclusion

In conclusion, this paper demonstrates a proof-of-concept 3D-OCT system that is capable of generating a 3D volumetric datacube in snapshot mode with simple calibration. The system can capture a datacube of (85×356×117) with expected performance specifications. A non-scanning, snapshot 3D imaging modality may be capable of acquiring images with reduced motion artifacts, particularly in weakly scattering samples. The high-performance system is being developed for a longer depth penetration of 1 mm and higher transverse and axial resolutions to provide better quality of depth visualization.

Acknowledgments

This work is supported by the John S. Dunn Foundation Collaborative Research Award Program and the National Institutes of Health under grant R21 EB011598.

References and links

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D. C. Adler, C. Zhou, T.-H. Tsai, J. Schmitt, Q. Huang, H. Mashimo, and J. G. Fujimoto, “Three-dimensional endomicroscopy of the human colon using optical coherence tomography.” Opt Express 17, 784–796 (2009) [CrossRef] [PubMed] .

2.

K. Yi, M. Mujat, B. H. Park, W. Sun, J. W. Miller, J. M. Seddon, L. H. Young, J. F. de Boer, and T. C. Chen, “Spectral domain optical coherence tomography for quantitative evaluation of drusen and associated structural changes in non-neovascular age-related macular degeneration.” Br J Ophthalmol 93, 176–181 (2009) [CrossRef] .

3.

E. Osiac, A. Saftoiu, D. I. Gheonea, I. Mandrila, and R. Angelescu, “Optical coherence tomography and Doppler optical coherence tomography in the gastrointestinal tract.” World J Gastroenterol 17, 15–20 (2011) [CrossRef] [PubMed] .

4.

R. de Kinkelder, J. Kalkman, D. J. Faber, O. Schraa, P. H. B. Kok, F. D. Verbraak, and T. G. van Leeuwen, “Heartbeat-induced axial motion artifacts in optical coherence tomography measurements of the retina.” Invest Ophthalmol Vis Sci 52, 3908–3913 (2011) [CrossRef] [PubMed] .

5.

J. F. de Boer, B. Cense, B. H. Park, M. C. Pierce, G. J. Tearney, and B. E. Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography.” Opt Lett 28, 2067–2069 (2003) [CrossRef] [PubMed] .

6.

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

7.

N. Hagen, R. T. Kester, L. Gao, and T. S. Tkaczyk, “Snapshot advantage: a review of the light collection improvement for parallel high-dimensional measurement systems.” Opt Eng 51(2012) [CrossRef] .

8.

Y. Chen, A. D. Aguirre, P.-L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S.-W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology.” Endoscopy 39, 599–605 (2007) [CrossRef] [PubMed] .

9.

Y. Nakamura, S. Makita, M. Yamanari, M. Itoh, T. Yatagai, and Y. Yasuno, “High-speed three-dimensional human retinal imaging by line-field spectral domain optical coherence tomography,” Opt. Express 15, 7103–7116 (2007) [CrossRef] [PubMed] .

10.

A. Dubois, J. Moreau, and C. Boccara, “Spectroscopic ultrahigh-resolution full-field optical coherence microscopy.” Opt Express 16, 17082–17091 (2008) [CrossRef] [PubMed] .

11.

B. Grajciar, M. Pircher, A. Fercher, and R. Leitgeb, “Parallel Fourier domain optical coherence tomography for in vivo measurement of the human eye.” Opt Express 13, 1131–1137 (2005) [CrossRef] [PubMed] .

12.

Y. Watanabe, K. Yamada, and M. Sato, “Three-dimensional imaging by ultrahigh-speed axial-lateral parallel time domain optical coherence tomography.” Opt Express 14, 5201–5209 (2006) [CrossRef] [PubMed] .

13.

S. Witte, M. Baclayon, E. J. G. Peterman, R. F. G. Toonen, H. D. Mansvelder, and M. L. Groot, “Single-shot two-dimensional full-range optical coherence tomography achieved by dispersion control.” Opt Express 17, 11335–11349 (2009) [CrossRef] [PubMed] .

14.

K. Grieve, A. Dubois, M. Simonutti, M. Paques, J. Sahel, J.-F. L. Gargasson, and C. Boccara, “In vivo anterior segment imaging in the rat eye with high speed white light full-field optical coherence tomography.” Opt Express 13, 6286–6295 (2005) [CrossRef] [PubMed] .

15.

M. S. Hrebesh, R. Dabu, and M. Sato, “In vivo imaging of dynamic biological specimen by real-time single-shot full-field optical coherence tomography,” Opt Comm 282, 674–683 (2009) [CrossRef] .

16.

H. M. Subhash, “Review article: Full-field and single-shot full-field optical coherence tomography: A novel technique for biomedical imaging applications,” Advances in Optical Technologies 2012(2012) [CrossRef] .

17.

B. K. Ford, C. E. Volin, S. M. Murphy, R. M. Lynch, and M. R. Descour, “Computed tomography-based spectral imaging for fluorescence microscopy.” Biophys J 80, 986–993 (2001) [CrossRef] [PubMed] .

18.

B. Ford, M. Descour, and R. Lynch, “Large-image-format computed tomography imaging spectrometer for fluorescence microscopy.” Opt Express 9, 444–453 (2001) [CrossRef] [PubMed] .

19.

C. A. Fernandez, A. Wagadarikar, D. J. Brady, S. C. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” 7184, 71840Z–71840Z-11 (2009).

20.

C. F. Cull, K. Choi, D. J. Brady, and T. Oliver, “Identification of fluorescent beads using a coded aperture snapshot spectral imager.” Appl Opt 49, B59–B70 (2010) [CrossRef] [PubMed] .

21.

A. Gorman, D. W. Fletcher-Holmes, and A. R. Harvey, “Generalization of the Lyot filter and its application to snapshot spectral imaging.” Opt Express 18, 5602–5608 (2010) [CrossRef] [PubMed] .

22.

A. Bodkin, A. Sheinis, A. Norton, J. Daly, C. Roberts, S. Beaven, and J. Weinheimer, eds., Video-rate chemical identification and visualization with snapshot hyperspectral imaging, vol. 8374 (2012).

23.

J. Kriesel, G. Scriven, N. Gat, S. Nagaraj, P. Willson, and V. Swaminathan, eds., Snapshot hyperspectral fovea vision system (HyperVideo)(2012).

24.

L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy.” Opt Express 18, 14330–14344 (2010) [CrossRef] [PubMed] .

25.

L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact image slicing spectrometer (ISS) for hyperspectral fluorescence microscopy.” Opt Express 17, 12293–12308 (2009) [CrossRef] [PubMed] .

26.

L. Gao, N. Bedard, N. Hagen, R. T. Kester, and T. S. Tkaczyk, “Depth-resolved image mapping spectrometer (IMS) with structured illumination.” Opt Express 19, 17439–17452 (2011) [CrossRef] [PubMed] .

27.

N. Bedard, N. Hagen, L. Gao, and T. S. Tkaczyk, “Image mapping spectrometry: calibration and characterization.” Opt Eng 51(2012) [CrossRef] [PubMed] .

28.

R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications.” Appl Opt 49, 1886–1899 (2010) [CrossRef] [PubMed] .

29.

L. S. Gao and T. S. Tkaczyk, “Correction of vignetting and distortion errors induced by two-axis light beam steering,” Optical Engineering 51(2012) [CrossRef] .

30.

A. D. Elliott, L. Gao, A. Ustione, N. Bedard, R. Kester, D. W. Piston, and T. S. Tkaczyk, “Real-time hyperspectral fluorescence imaging of pancreatic β-cell dynamics with the image mapping spectrometer (IMS).” J Cell Sci (2012) [CrossRef] .

31.

I. Abdulhalim, “Competence between spatial and temporal coherence in full field optical coherence tomography and interference microscopy,” Journal of Optics A: Pure and Applied Optics 8(2006) [CrossRef] .

32.

M. Mujat, B. H. Park, B. Cense, T. C. Chen, and J. F. de Boer, “Autocalibration of spectral-domain optical coherence tomography spectrometers for in vivo quantitative retinal nerve fiber layer birefringence determination.” J Biomed Opt 12, 041205 (2007) [CrossRef] [PubMed] .

33.

C. Dorrer, “Influence of the calibration of the detector on spectral interferometry,” J. Opt. Soc. Am. B 16, 1160–1168 (1999) [CrossRef] .

34.

L. Lepetit, G. Chriaux, and M. Joffre, “Linear techniques of phase measurement by femtosecond spectral interferometry for applications in spectroscopy,” J. Opt. Soc. Am. B 12, 2467–2474 (1995) [CrossRef] .

35.

M. Wojtkowski, V. Srinivasan, T. Ko, J. Fujimoto, A. Kowalczyk, and J. Duker, “Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation.” Opt Express 12, 2404–2422 (2004) [CrossRef] [PubMed] .

OCIS Codes
(110.4500) Imaging systems : Optical coherence tomography
(170.3880) Medical optics and biotechnology : Medical and biological imaging

ToC Category:
Imaging Systems

History
Original Manuscript: February 25, 2013
Revised Manuscript: April 26, 2013
Manuscript Accepted: May 22, 2013
Published: May 31, 2013

Virtual Issues
Vol. 8, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Thuc-Uyen Nguyen, Mark C Pierce, Laura Higgins, and Tomasz S Tkaczyk, "Snapshot 3D optical coherence tomography system using image mapping spectrometry," Opt. Express 21, 13758-13772 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-11-13758


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References

  1. D. C.  Adler, C.  Zhou, T.-H.  Tsai, J.  Schmitt, Q.  Huang, H.  Mashimo, J. G.  Fujimoto, “Three-dimensional endomicroscopy of the human colon using optical coherence tomography.” Opt Express 17, 784–796 (2009). [CrossRef] [PubMed]
  2. K.  Yi, M.  Mujat, B. H.  Park, W.  Sun, J. W.  Miller, J. M.  Seddon, L. H.  Young, J. F.  de Boer, T. C.  Chen, “Spectral domain optical coherence tomography for quantitative evaluation of drusen and associated structural changes in non-neovascular age-related macular degeneration.” Br J Ophthalmol 93, 176–181 (2009). [CrossRef]
  3. E.  Osiac, A.  Saftoiu, D. I.  Gheonea, I.  Mandrila, R.  Angelescu, “Optical coherence tomography and Doppler optical coherence tomography in the gastrointestinal tract.” World J Gastroenterol 17, 15–20 (2011). [CrossRef] [PubMed]
  4. R.  de Kinkelder, J.  Kalkman, D. J.  Faber, O.  Schraa, P. H. B.  Kok, F. D.  Verbraak, T. G.  van Leeuwen, “Heartbeat-induced axial motion artifacts in optical coherence tomography measurements of the retina.” Invest Ophthalmol Vis Sci 52, 3908–3913 (2011). [CrossRef] [PubMed]
  5. J. F.  de Boer, B.  Cense, B. H.  Park, M. C.  Pierce, G. J.  Tearney, B. E.  Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography.” Opt Lett 28, 2067–2069 (2003). [CrossRef] [PubMed]
  6. R.  Leitgeb, C.  Hitzenberger, A.  Fercher, “Performance of Fourier domain vs. time domain optical coherence tomography.” Opt Express 11, 889–894 (2003). [CrossRef] [PubMed]
  7. N.  Hagen, R. T.  Kester, L.  Gao, T. S.  Tkaczyk, “Snapshot advantage: a review of the light collection improvement for parallel high-dimensional measurement systems.” Opt Eng 51(2012). [CrossRef]
  8. Y.  Chen, A. D.  Aguirre, P.-L.  Hsiung, S.  Desai, P. R.  Herz, M.  Pedrosa, Q.  Huang, M.  Figueiredo, S.-W.  Huang, A.  Koski, J. M.  Schmitt, J. G.  Fujimoto, H.  Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology.” Endoscopy 39, 599–605 (2007). [CrossRef] [PubMed]
  9. Y.  Nakamura, S.  Makita, M.  Yamanari, M.  Itoh, T.  Yatagai, Y.  Yasuno, “High-speed three-dimensional human retinal imaging by line-field spectral domain optical coherence tomography,” Opt. Express 15, 7103–7116 (2007). [CrossRef] [PubMed]
  10. A.  Dubois, J.  Moreau, C.  Boccara, “Spectroscopic ultrahigh-resolution full-field optical coherence microscopy.” Opt Express 16, 17082–17091 (2008). [CrossRef] [PubMed]
  11. B.  Grajciar, M.  Pircher, A.  Fercher, R.  Leitgeb, “Parallel Fourier domain optical coherence tomography for in vivo measurement of the human eye.” Opt Express 13, 1131–1137 (2005). [CrossRef] [PubMed]
  12. Y.  Watanabe, K.  Yamada, M.  Sato, “Three-dimensional imaging by ultrahigh-speed axial-lateral parallel time domain optical coherence tomography.” Opt Express 14, 5201–5209 (2006). [CrossRef] [PubMed]
  13. S.  Witte, M.  Baclayon, E. J. G.  Peterman, R. F. G.  Toonen, H. D.  Mansvelder, M. L.  Groot, “Single-shot two-dimensional full-range optical coherence tomography achieved by dispersion control.” Opt Express 17, 11335–11349 (2009). [CrossRef] [PubMed]
  14. K.  Grieve, A.  Dubois, M.  Simonutti, M.  Paques, J.  Sahel, J.-F. L.  Gargasson, C.  Boccara, “In vivo anterior segment imaging in the rat eye with high speed white light full-field optical coherence tomography.” Opt Express 13, 6286–6295 (2005). [CrossRef] [PubMed]
  15. M. S.  Hrebesh, R.  Dabu, M.  Sato, “In vivo imaging of dynamic biological specimen by real-time single-shot full-field optical coherence tomography,” Opt Comm 282, 674–683 (2009). [CrossRef]
  16. H. M.  Subhash, “Review article: Full-field and single-shot full-field optical coherence tomography: A novel technique for biomedical imaging applications,” Advances in Optical Technologies 2012(2012). [CrossRef]
  17. B. K.  Ford, C. E.  Volin, S. M.  Murphy, R. M.  Lynch, M. R.  Descour, “Computed tomography-based spectral imaging for fluorescence microscopy.” Biophys J 80, 986–993 (2001). [CrossRef] [PubMed]
  18. B.  Ford, M.  Descour, R.  Lynch, “Large-image-format computed tomography imaging spectrometer for fluorescence microscopy.” Opt Express 9, 444–453 (2001). [CrossRef] [PubMed]
  19. C. A.  Fernandez, A.  Wagadarikar, D. J.  Brady, S. C.  McCain, T.  Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” 7184, 71840Z–71840Z-11 (2009).
  20. C. F.  Cull, K.  Choi, D. J.  Brady, T.  Oliver, “Identification of fluorescent beads using a coded aperture snapshot spectral imager.” Appl Opt 49, B59–B70 (2010). [CrossRef] [PubMed]
  21. A.  Gorman, D. W.  Fletcher-Holmes, A. R.  Harvey, “Generalization of the Lyot filter and its application to snapshot spectral imaging.” Opt Express 18, 5602–5608 (2010). [CrossRef] [PubMed]
  22. A.  Bodkin, A.  Sheinis, A.  Norton, J.  Daly, C.  Roberts, S.  Beaven, J.  Weinheimer, eds., Video-rate chemical identification and visualization with snapshot hyperspectral imaging, vol. 8374 (2012).
  23. J.  Kriesel, G.  Scriven, N.  Gat, S.  Nagaraj, P.  Willson, V.  Swaminathan, eds., Snapshot hyperspectral fovea vision system (HyperVideo)(2012).
  24. L.  Gao, R. T.  Kester, N.  Hagen, T. S.  Tkaczyk, “Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy.” Opt Express 18, 14330–14344 (2010). [CrossRef] [PubMed]
  25. L.  Gao, R. T.  Kester, T. S.  Tkaczyk, “Compact image slicing spectrometer (ISS) for hyperspectral fluorescence microscopy.” Opt Express 17, 12293–12308 (2009). [CrossRef] [PubMed]
  26. L.  Gao, N.  Bedard, N.  Hagen, R. T.  Kester, T. S.  Tkaczyk, “Depth-resolved image mapping spectrometer (IMS) with structured illumination.” Opt Express 19, 17439–17452 (2011). [CrossRef] [PubMed]
  27. N.  Bedard, N.  Hagen, L.  Gao, T. S.  Tkaczyk, “Image mapping spectrometry: calibration and characterization.” Opt Eng 51(2012). [CrossRef] [PubMed]
  28. R. T.  Kester, L.  Gao, T. S.  Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications.” Appl Opt 49, 1886–1899 (2010). [CrossRef] [PubMed]
  29. L. S.  Gao T. S.  Tkaczyk, “Correction of vignetting and distortion errors induced by two-axis light beam steering,” Optical Engineering 51(2012). [CrossRef]
  30. A. D.  Elliott, L.  Gao, A.  Ustione, N.  Bedard, R.  Kester, D. W.  Piston, T. S.  Tkaczyk, “Real-time hyperspectral fluorescence imaging of pancreatic β-cell dynamics with the image mapping spectrometer (IMS).” J Cell Sci (2012). [CrossRef]
  31. I.  Abdulhalim, “Competence between spatial and temporal coherence in full field optical coherence tomography and interference microscopy,” Journal of Optics A: Pure and Applied Optics 8(2006). [CrossRef]
  32. M.  Mujat, B. H.  Park, B.  Cense, T. C.  Chen, J. F.  de Boer, “Autocalibration of spectral-domain optical coherence tomography spectrometers for in vivo quantitative retinal nerve fiber layer birefringence determination.” J Biomed Opt 12, 041205 (2007). [CrossRef] [PubMed]
  33. C.  Dorrer, “Influence of the calibration of the detector on spectral interferometry,” J. Opt. Soc. Am. B 16, 1160–1168 (1999). [CrossRef]
  34. L.  Lepetit, G.  Chriaux, M.  Joffre, “Linear techniques of phase measurement by femtosecond spectral interferometry for applications in spectroscopy,” J. Opt. Soc. Am. B 12, 2467–2474 (1995). [CrossRef]
  35. M.  Wojtkowski, V.  Srinivasan, T.  Ko, J.  Fujimoto, A.  Kowalczyk, J.  Duker, “Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation.” Opt Express 12, 2404–2422 (2004). [CrossRef] [PubMed]

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