Optical coherence microscopy (OCM) is an interferometric technique providing 3D images of biological samples with micrometric resolution and penetration depth of several hundreds of micrometers. OCM differs from optical coherence tomography (OCT) in that it uses a high numerical aperture (NA) objective to achieve high lateral resolution. However, the high NA also reduces the depth-of-field (DOF), scaling with 1/NA2. Interferometric synthetic aperture microscopy (ISAM) is a computed imaging technique providing a solution to this trade-off between resolution and DOF. An alternative hardware method to achieve an extended DOF is to use a non-Gaussian illumination. Extended focus OCM (xfOCM) uses a Bessel beam to obtain a narrow and extended illumination volume. xfOCM detects back-scattered light using a Gaussian mode in order to maintain good sensitivity. However, the Gaussian detection mode limits the DOF. In this work, we present extended ISAM (xISAM), a method combining the benefits of both ISAM and xfOCM. xISAM uses the 3D coherent transfer function (CTF) to generalize the ISAM algorithm to different system configurations. We demonstrate xISAM both on simulated and experimental data, showing that xISAM attains a combination of high transverse resolution and extended DOF which has so far been unobtainable through conventional ISAM or xfOCM individually.
Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Optical coherence tomography (OCT) has enabled rapid, highly sensitive, in vivo, volumetric imaging of tissues and cells and has been widely applied both in biomedical research and clinical settings [1–4]. OCM is based on the same principles as OCT but achieves higher transverse resolution by using higher-NA optics. This increase of resolution comes however at the expense of a reduced DOF, which is proportional to 1/NA2.
Various solutions have been proposed to overcome this inherent trade-off between resolution and DOF. Computational techniques have been developed to obtain a larger DOF [5–8] (for a review of these methods, see [9,10]). ISAM was presented as a solution to the inverse scattering problem, yielding spatially invariant transverse resolution throughout the entire volume [6,11,12]. Developments of ISAM such as real-time [13,14] or automated ISAM  have further extended the capabilities of this method.
Alternative hardware implementations of an extended DOF include dynamic focus control [16,17], mechanical depth scanning , and multi-channel imaging . Another approach is to use non-Gaussian beam illumination via wavefront engineering using axicon lenses, phase plates, digital micro-mirror devices or spatial light modulators [16,20–27]. xfOCM uses an axicon to generate a Bessel-beam illumination and a decoupled Gaussian detection to guarantee an efficient collection of the scattered light. However, the use of a Gaussian detection comes at the cost of a reduced DOF. In this way, xfOCM achieves a better trade-off between lateral resolution and DOF than conventional OCM systems with equal Gaussian illumination and detection modes. xfOCM has been proven to be a powerful tool in many biomedical applications [21,28–32]. Nevetherless, xfOCM could still benefit from a further improvement of DOF.
In this paper, we propose to combine ISAM and xfOCM to simultaneously optimize transverse resolution, DOF and signal-to-noise ratio (SNR) to achieve unprecedented OCM imaging performance. Current ISAM implementations assume equal Gaussian illumination and detection apertures and are therefore not suitable for xfOCM systems. In 2012, Sheppard et al. showed that the ISAM reconstruction was not an exact but an approximate solution to the inverse scattering problem, as it assumes exact backscattering (i.e., incident light is backscattered in the same direction) . A more general theory based on the 3D coherent transfer function (CTF) was proposed, enabling different system configurations to be explored.
In the present work, we exploit the 3D CTF formalism suggested in  in order to generalize the ISAM principle to decoupled apertures such as in xfOCM. We term our method extended ISAM (xISAM) from the combination of xfOCM and ISAM. While the ISAM reconstruction achieves focal-plane resolution throughout the volume, the xfOCM scheme allows illuminating the sample along an extended axial range. This results in a higher transverse resolution and SNR away from the focus as compared to conventional OCM systems. ISAM reconstruction critically relies on phase stability, which depends on the SNR [34–36]. Hence, xfOCM allows ISAM reconstruction at greater depths. We describe xISAM and validate it both on simulated and experimental data, showing a clear sensitivity advantage over conventional ISAM away from the focal plane.
2. Materials & methods
2.1. Optical setup
The layout of the xfOCM instrument used in this work has been described elsewhere [28–30]. It consists of a Mach-Zehnder interferometer with separate illumination and detection paths, as displayed in Fig. 1. A broadband light source (Ti:Sapphire laser, Femtolasers) with a spectral bandwidth of 135 nm centered at 800 nm is coupled into a long single mode, polarization maintaining fiber with a mode field diameter of 4.2 μm (HB 750, Fibercore Ltd., Southampton). Propagation through the fiber broadens the pulses from the femtosecond laser source to reduce peak power. Light from the fiber is collimated by the lens LC1 (f = 8 mm) and split into reference (blue) and sample (green) arms by a first beam-splitter (BS1). In the sample arm, the beam passes either a normal lens (f = 75 mm) for the Gaussian illumination mode, or an axicon lens (Del Mar Photonics, 175° apex angle) for the Bessel illumination mode. The beam is then relayed through a scanning system to the tube lens (LT, f = 164 mm) and the objective (LS, 10×, 0.3 NA, Zeiss Neofluoar). The back-scattered light (red) is collected by the same objective and recombined to the reference beam by means of a second beam-splitter (BS2). The detection path is composed of a first lens LD (f = 200 mm) and a second lens LC2 (f = 20 mm) coupling the signal into a detection fiber. Finally, a custom spectrometer consisting of a transmission grating (1200 lines/mm), an objective lens (f = 135 mm) and a line detector (2048 pixels, Atmel AViiVA) records the interference pattern at a rate of 20 kHz. The illumination power, measured at the intermediate image plane between lens LT and the scan unit (see Fig. 1), was ∼ 10 mW.
2.2. Coherent transfer function calculation
Following Sheppard et al. , we use the framework of the 3D CTF for a more accurate and generalized ISAM reconstruction which can be applied to different system configurations including decoupled illumination and detection apertures. The theory underlying 3D image formation and the CTF has been well studied in [37–39] and is based on the concept of generalized aperture introduced by McCutchen in 1964 . Villiger and Lasser proposed a model for image formation in OCM setups using the generalized aperture and the CTF . For each wavenumber channel of the system, the CTF is given by the convolution of the generalized illumination and detection apertures.
2.3. xISAM algorithm
For equal Gaussian illumination and detection apertures, the CTF has a maximum value on a sphere with a radius of twice the wavenumber k, corresponding to the maximum length of the scattering vector for a reflection geometry. Hence,37], the curvature along qr is related to defocusing. The principle of ISAM is to perform a resampling in the frequency domain to flatten this spherical curvature and thus compensate for the defocus [6,11,12]. ISAM uses Eq. 1 for the k → qz resampling and assumes that the scattering comes from exact backscattering, corresponding to the maximum of the CTF.
Sheppard et al. proposed a more accurate reconstruction taking the mean position of the CTF . Taking this approach as a key ingredient, we use the center of mass of the 3D CTF at each lateral frequency as resampling curve for our xISAM reconstruction. Figure 2 illustrates this principle for two different systems, one with identical Gaussian illumination and detection modes (OCM) and one with decoupled Bessel illumination and Gaussian detection modes (xfOCM). Obviously, xfOCM data are incompletely represented in classical ISAM, which uses a spherical resampling surface. In contrast, our method allows refocusing data acquired with potentially any system configuration.
Figure 3 illustrates the processing steps for xISAM reconstruction. We start from the tomogram SOCM(x, y, z) recovered from the recorded interference signal using the conventional λ → k mapping and 1D Fourier transform. A phase correction is first performed to ensure phase stability for the reconstruction [36, 41]. In this work, we used the phase correction presented in  using a glass coverslip placed on top of the sample as reference. More elaborate algorithms not requiring a reference object could be used to correct for phase instabilities, as presented in [42–44] for example. The next step is a coordinate change from z to z′ consisting of a circular shift of the data so that the focal plane appears at z′ = 0. As shown in Fig. 3, the shifted data is also zero-padded with 2nzo lines to prevent aliasing, where nzo is the number of pixels from the focus to the center of S(x, y, z). The spatial frequency domain OCM signal is then obtained via 3D Fourier transformation. The xISAM k → qz interpolation is then performed using a resampling grid derived from the CTF as explained in the previous section. Figure 3 shows how the xISAM resampling compensate for the defocus by flattening the phase. Finally, a 3D inverse Fourier transform and an inverse coordinate change allow retrieving the defocus-corrected tomogram SxISAM(x, y, z).
As a proof-of-concept, we simulated data composed of point scatterers aligned along the optical axis. OCM and xfOCM signals S(x, y, k) were calculated as the convolution of the scattering potential η(x, y, z) and the system (complex) point-spread-function (PSF) h(x, y, z, k):
Figures 4 and 5 show the results of the simulations for the OCM and xfOCM modalities, respectively. In both cases, the unprocessed tomogram and the xISAM reconstructed image are shown, as well as the phase associated with an out-of-focus scatterer (red arrow) before and after xISAM processing. Line profiles through another scatterer are also plotted alongside a profile through the in-focus scatterer. For better comparison, the profiles were normalized relative to their energy.
As seen in the line profiles and as known from , xfOCM increases the focal range with respect to OCM (blue curves in Figs. 4 and 5). The xISAM reconstruction allows recovering the focal-plane transverse resolution almost perfectly in the OCM case (orange, Fig. 4). On the other hand, the reconstruction of the xfOCM signal (Fig. 5) is not as ideal but still shows a significant improvement with a reduction of the side lobes to the benefit of the central peak. The reason for this difference is presumably that the reconstruction of xfOCM data is more delicate due to the more complicated shape of the resampling curve. In other words, it is more difficult to perfectly flatten the CTF and thus completely compensate for the defocus. This imprecision is visible in Fig. 5 in the phase profile after reconstruction, which is not perfectly flat. Finally, we note for both cases that despite the refocusing, the reconstructions still drop in intensity with increasing distance from the focal plane due to reduced signal collection.
3.2. Microbeads sample
To validate the xISAM procedure experimentally, we first imaged 1 μm-sized polystyrene microspheres embedded in silicone. This technical phantom was prepared by drying the water suspended beads, re-suspending them in the silicone and adding a curing agent. Prior to baking, air bubbles were removed from the sample using a vacuum bell.
Figure 6 shows the acquired OCM and xfOCM data and their respective xISAM reconstruction. En face views at three different depths and a maximum projection along the 256 B-scans in the x-direction are shown for all four cases. Beads at ±45 μm depth in the OCM tomogram are almost not discernible in the unprocessed data but are well refocused after xISAM resampling. Compared with classical OCM, the xfOCM scheme exhibits a larger focal range allowing the observation of scatterers positioned at ± 50 μm with improved contrast. In accordance with the simulated data, the reconstruction of the xfOCM tomograms redistributes the energy from the side lobes towards the central peak. This energy redistribution leads to a general improvement of the image quality. We note that a jitter caused by the scanning mirrors is apparent in the en face views at the focal plane, but did not prevent a successful reconstruction. The capability of xISAM is well observable in the maximum projections. Even though the reconstruction of OCM data yields focal-plane resolution at all depths, the xfOCM and even more particularly its xISAM reconstruction display a clear SNR advantage in out-of-focus planes.
To better characterize the performance of OCM, xfOCM and xISAM, we plotted in Fig. 7 several lines profiles along different beads. The results are in excellent agreement with the simulations presented in Figs. 4–5, showing a clear refocusing for the OCM configuration and an energy increase in the central peak with a diminution of the side lobes for the xfOCM configuration. To further compare the four cases, we defined an image quality metrics as the sum of the 20 highest pixels at each depth. xISAM achieves a clear improvement for both system configurations. Undeniably, the extended focus configuration brought by the Bessel beam illumination procures a substantial advantage in terms of signal collection. This demonstrates the usefulness of combining the ISAM concept with a hardware implementation to obtain a greatly extended focal range.
3.3. Mandarin sample
We further demonstrate our method taking a mandarin as a sample for the xISAM reconstruction. A slice of mandarin was cut with a razor blade. The cut surface with fresh pulp was then simply covered by a coverslide. As for the microspheres measurement, this sample was imaged with the conventional OCM configuration and the xfOCM system. Both tomograms were then resampled using the xISAM procedure. The results are shown in Fig. 8. The en face views exhibit sharper features in both reconstructions with respect to the unprocessed data. Moreover, the maximum projections show a clear improvement in both resolution and contrast (see zoomed-in areas). Similarly to the microspheres measurement, the benefit of the xfOCM scheme in terms of signal collection away from the focus is evident in these projections. The xISAM processing brings an additional gain of transverse resolution even far from the focal plane, as can be seen in the zoomed-in areas.
4. Discussion & conclusion
We introduced xISAM, a new computational method for high-resolution volumetric imaging combining the approaches of xfOCM and ISAM. Using the framework of the CTF as suggested in , we generalized the ISAM technique to different system configurations. This was achieved by taking the center of mass of the CTF as resampling curve for the k → qz interpolation. Our method was applied to a setup with NA = 0.3 that allowed both OCM and xfOCM modalities. Moreover, xISAM can be applied to any system configuration provided that the CTF can be calculated or measured.
For the experimental data, a phase correction procedure was applied based on a glass coverslip. Other algorithms not requiring the use of a reference object might need to be implemented for future applications, for instance based on existing techniques such as presented in [42–44]. Moreover, it was demonstrated that performing an aberration correction alongside with ISAM yields superior results [45, 46]. Aberrations are caused by imperfections of the setup or the sample itself, and can significantly degrade the resolution and image quality. These effects are increasingly important with increasing NA. To apply xISAM to other OCM configurations such as dark-field [47–49] or visible OCM  using a NA ≥ 0.8, an aberration correction step becomes necessary. Various methods have been developed to address this issue, two notable examples being computational adaptive optics (CAO) [15,45,51] and subaperture correlation based digital AO [52,53]. Future developments of our method aim at including such an aberration correction procedure.
In summary, we first demonstrated here the principle of xISAM on simulated data of point scatterers. Our method achieved a recovery of the focal-plane resolution for the OCM configuration and a diminution of the side lobes and increase of the central peak in the extended focus scheme, improving the image quality. We then performed validation on real experimental measurements of polystyrene beads and a mandarin slice. The experimental results were in good agreement with the simulations. We noted that xfOCM exhibits a significant signal collection advantage over OCM data, even after ISAM reconstruction. The increased signal collection away from the focus offers higher phase stability and thus enables reconstruction at greater depths. This better SNR over depth shows the benefit of implementing a hardware-based DOF in addition to performing a software reconstruction. Combining both methods further increases the focal range, enabling high-resolution imaging even at large depths.
This work was partially funded by the Swiss National Science Foundation (grants 205321L_135353 & 205320L_150191). A.B. and T.L. acknowledge support from the Seventh Framework Programme and the Horizon 2020 Framework Programme of the European Union via grants 602812 and 686271, respectively.
We would like to thank Martin Villiger for all the experimental data presented in this work.
References and links
1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical Coherence Tomography,” Science 254(5035), 1178–1181 (1991). [CrossRef] [PubMed]
2. W. Drexler, U. Morgner, R. K. Ghanta, F. X. Kärtner, J. S. Schuman, and J. G. Fujimoto, “Ultrahigh-resolution ophthalmic optical coherence tomography,” Nat. Med. 7(4), 502–507 (2001). [CrossRef] [PubMed]
3. J. G. Fujimoto, “Optical coherence tomography for ultrahigh resolution in vivo imaging,” Nat. Biotech. 21(11), 1361–1367 (2003). [CrossRef]
4. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography,” Reports on Progress in Physics 66, 239–303 (2003). [CrossRef]
5. Y. Yasuno, J.-I. Sugisaka, Y. Sando, Y. Nakamura, S. Makita, M. Itoh, and T. Yatagai, “Non-iterative numerical method for laterally superresolving Fourier domain optical coherence tomography,” Opt. Express 14(3), 1006–1020 (2006). [CrossRef] [PubMed]
7. L. Yu, J. Zhang, J. Su, Q. Wang, S. Guo, and Z. Chen, “Improved lateral resolution in optical coherence tomography by digital focusing using two-dimensional numerical diffraction method,” Opt. Express 15(12), 7634–7641 (2007). [CrossRef] [PubMed]
9. F. A. South, Y.-Z. Liu, P. S. Carney, and S. A. Boppart, “Computed Optical Interferometric Imaging: Methods, Achievements, and Challenges,” IEEE J. of Selected Topics in Quantum Electronics 22(3), 6800911 (2016). [CrossRef] [PubMed]
12. T. S. Ralston, D. L. Marks, P. S. Carney, and S. A. Boppart, “Inverse scattering for optical coherence tomography,” J. Opt. Soc. Am. A 23(5), 1027–1037 (2006). [CrossRef]
14. A. Ahmad, N. D. Shemonski, S. G. Adie, H.-S. Kim, W.-M. W. Hwu, P. S. Carney, and S. A. Boppart, “Real-time in vivo computed optical interferometric tomography,” Nat. Photonics 7(6), 444–448 (2013). [CrossRef] [PubMed]
15. Y. Xu, Y.-Z. Liu, S. A. Boppart, and P. S. Carney, “Automated interferometric synthetic aperture microscopy and computational adaptive optics for improved optical coherence tomography,” Appl. Opt. 55(8), 2034–2041 (2016). [CrossRef] [PubMed]
16. F. Lexer, C. K. Hitzenberger, W. Drexler, S. Molebny, H. Sattmann, M. Sticker, and A. F. Fercher, “Dynamic coherent focus OCT with depth-independent transversal resolution,” J. Mod. Opt. 46(3), 541–553 (1999). [CrossRef]
17. A. Divetia, T.-H. Hsieh, J. Zhang, Z. Chen, M. Bachman, and G.-P. Li, “Dynamically focused optical coherence tomography for endoscopic applications,” Appl. Phys. Lett. 86, 103902 (2005). [CrossRef]
18. A. D. Aguirre, J. Sawinski, S.-W. Huang, C. Zhou, W. Denk, and J. G. Fujimoto, “High speed optical coherence microscopy with autofocus adjustment and a miniaturized endoscopic imaging probe,” Opt. Express 18(5), 4222–4239 (2010). [CrossRef] [PubMed]
19. B. A. Standish, K. K. C. Lee, A. Mariampillai, N. R. Munce, M. K. K. Leung, V. X. D. Yang, and I. A. Vitkin, “In vivo endoscopic multi-beam optical coherence tomography,” Phys. in Med. and Biol. 55, 615–622 (2010). [CrossRef]
20. Z. Ding, H. Ren, Y. Zhao, J. S. Nelson, and Z. Chen, “High-resolution optical coherence tomography over a large depth range with an axicon lens,” Opt. Lett. 27(4), 243–245 (2002). [CrossRef]
21. R. A. Leitgeb, M. Villiger, A. H. Bachmann, L. Steinmann, and T. Lasser, “Extended focus depth for Fourier domain optical coherence microscopy,” Opt. Lett. 31(16), 2450–2452 (2006). [CrossRef] [PubMed]
22. C. Blatter, B. Grajciar, C. M. Eigenwillig, W. Wieser, B. R. Biedermann, R. Huber, and R. A. Leitgeb, “Extended focus high-speed swept source OCT with self-reconstructive illumination,” Opt. Express 19(13), 12141–12155 (2011). [CrossRef] [PubMed]
23. L. Liu, C. Liu, W. C. Howe, C. J. R. Sheppard, and N. Chen, “Binary-phase spatial filter for real-time swept-source optical coherence microscopy,” Opt. Lett. 32(16), 2375–2377 (2007). [CrossRef] [PubMed]
25. J. Jang, J. Lim, H. Yu, H. Choi, J. Ha, J.-H. Park, W.-Y. Oh, W. Jang, SD. Lee, and YK. Park, “Complex wavefront shaping for optimal depth-selective focusing in optical coherence tomography,” Opt. Express 21(3), 2890–2902 (2013). [CrossRef] [PubMed]
26. D. Lorenser, C. C. Singe, A. Curatolo, and D. D. Sampson, “Energy-efficient low-Fresnel-number Bessel beams and their application in optical coherence tomography,” Opt. Lett. 39(3), 548–551 (2014). [CrossRef] [PubMed]
28. M. Villiger, J. Goulley, M. Friedrich, A. Grapin-Botton, P. Meda, T. Lasser, and R. A. Leitgeb, “In vivo imaging of murine endocrine islets of Langerhans with extended-focus optical coherence microscopy,” Diabetologia 52, 1599–1607 (2009). [CrossRef] [PubMed]
29. T. Bolmont, A. Bouwens, C. Pache, M. Dimitrov, C. Berclaz, M. Villiger, B. M. Wegenast-Braun, T. Lasser, and P. C. Fraering, “Label-free imaging of cerebral β-amyloidosis with extended-focus optical coherence microscopy,” J. of Neuroscience 32(42), 14538–14556 (2012). [CrossRef]
30. C. Berclaz, J. Goulley, M. Villiger, C. Pache, A. Bouwens, E. Martin-Williams, D. Van de Ville, A. C. Davison, A. Grapin-Botton, and T. Lasser, “Diabetes imaging-quantitative assessment of islets of Langerhans distribution in murine pancreas using extended-focus optical coherence microscopy,” Biomed. Opt. Express 3(6), 1365–1380 (2012). [CrossRef] [PubMed]
31. A. Bouwens, T. Bolmont, D. Szlag, C. Berclaz, and T. Lasser, “Quantitative cerebral blood flow imaging with extended-focus optical coherence microscopy,” Opt. Lett. 39(1), 37–40 (2014). [CrossRef]
32. C. Berclaz, C. Pache, A. Bouwens, D. Szlag, A. Lopez, L. Joosten, S. Ekim, M. Brom, M. Gotthardt, A. Grapin-Botton, and T. Lasser, “Combined Optical Coherence and Fluorescence Microscopy to assess dynamics and specificity of pancreatic beta-cell tracers,” Sci. Rep. 5, 10385 (2015). [CrossRef] [PubMed]
33. C. J. R. Sheppard, S. S. Kou, and C. Depeursinge, “Reconstruction in interferometric synthetic aperture microscopy: comparison with optical coherence tomography and digital holographic microscopy,” J. Opt. Soc. Am. A 29(3), 244–250 (2012). [CrossRef]
34. M. A. Choma, A. K. Ellerbee, S. Yazdanfar, and J. A. Izatt, “Doppler flow imaging of cytoplasmic streaming using spectral domain phase microscopy,” J. Biomed. Opt. 11(2), 024014 (2006). [CrossRef] [PubMed]
35. B. H. Park, M. C. Pierce, B. Cense, S.-H. Yun, M. Mujat, G. J. Tearney, B. E. Bouma, and J. F. de Boer, “Real-time fiber-based multi-functional spectral-domain domain optical coherence tomography at 1.3 μm,” Opt. Express 13(11), 3931–3944 (2005). [CrossRef] [PubMed]
36. N. D. Shemonski, S. G. Adie, Y.-Z. Liu, F. A. South, P. S. Carney, and S. A. Boppart, “Stability in computed optical interferometric tomography (Part I): Stability requirements,” Opt. Express 22(16), 19183–19197 (2014). [CrossRef] [PubMed]
37. M. Villiger and T. Lasser, “Image formation and tomogram reconstruction in optical coherence microscopy,” J. Opt. Soc. Am. A 27(10), 2216–2228 (2010). [CrossRef]
38. C. J. R. Sheppard, M. Gu, Y. Kawata, and S. Kawata, “Three-dimensional transfer functions for high-aperture systems,” J. Opt. Soc. Am. A 11(2) 593–598 (1994). [CrossRef]
39. M. Gu, Advanced Optical Imaging Theory (Springer, 2000). [CrossRef]
40. C. W. McCutchen, “Generalized Aperture and the Three-Dimensional Diffraction Image,” J. Opt. Soc. Am. 54(2), 240–244 (1964). [CrossRef]
41. T. S. Ralston, D. L. Marks, P. S. Carney, and S. A. Boppart, “Phase Stability Technique for Inverse Scattering in Optical Coherence Tomography,” in 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, (IEEE, 2006), pp. 578–581.
42. J. Lee, V. Srinivasan, H. Radhakrishnan, and D. A. Boas, “Motion correction for phase-resolved dynamic optical coherence tomography imaging of rodent cerebral cortex,” Opt. Express 19(22), 21258–21270 (2011). [CrossRef] [PubMed]
43. N. D. Shemonski, S. S. Ahn, Y.-Z. Liu, F. A. South, P. S. Carney, and S. A. Boppart, “Three-dimensional motion correction using speckle and phase for in vivo computed optical interferometric tomography,” Biomed. Opt. Express 5(12), 4131–4143 (2014). [CrossRef]
44. N. D. Shemonski, S. G. Adie, Y.-Z. Liu, F. A. South, P. S. Carney, and S. A. Boppart, “Stability in computed optical interferometric tomography (Part II): in vivo stability assessment,” Opt. Express 22(16), 19314–19326 (2014). [CrossRef] [PubMed]
45. S. G. Adie, B. W. Graf, A. Ahmad, P. S. Carney, and S. A. Boppart, “Computational adaptive optics for broadband optical interferometric tomography of biological tissue,” Proc. Natl. Acad. Sci. U.S.A. 109(19), 7175–7180 (2012). [CrossRef] [PubMed]
46. Y. Z. Liu, N. D. Shemonski, S. G. Adie, A. Ahmad, A. J. Bower, P. S. Carney, and S. A. Boppart, “Computed optical interferometric tomography for high-speed volumetric cellular imaging,” Biomed. Opt. Express 5(9), 2988–3000 (2014). [CrossRef] [PubMed]
48. C. Pache, N. L. Bocchio, A. Bouwens, M. Villiger, C. Berclaz, J. Goulley, M. I. Gibson, C. Santschi, and T. Lasser, “Fast three-dimensional imaging of gold nanoparticles in living cells with photothermal optical lock-in Optical Coherence Microscopy,” Opt. Express 20(19), 21385–21399 (2012). [CrossRef] [PubMed]
49. M. Sison, S. Chakrabortty, J. Extermann, A. Nahas, P. J. Marchand, A. Lopez, T. Weil, and T. Lasser, “3D Time-lapse Imaging and Quantification of Mitochondrial Dynamics,” Sci. Rep. 7, 43275 (2017). [CrossRef] [PubMed]
50. P. J. Marchand, A. Bouwens, D. Szlag, D. Nguyen, A. Descloux, M. Sison, S. Coquoz, J. Extermann, and T. Lasser, “Visible spectrum extended-focus optical coherence microscopy for label-free sub-cellular tomography,” Biomed. Opt. Express 8(7), 3343–3359 (2017). [CrossRef] [PubMed]
51. N. D. Shemonski, F. A. South, Y.-Z. Liu, S. G. Adie, P. S. Carney, and S. A. Boppart, “Computational high-resolution optical imaging of the living human retina,” Nat. Photonics 9, 440–443 (2015). [CrossRef]
52. A. Kumar, W. Drexler, and R. A. Leitgeb, “Subaperture correlation based digital adaptive optics for full field optical coherence tomography,” Opt. Express 21(9), 10850–10566 (2013). [CrossRef] [PubMed]
53. A. Kumar, T. Kamali, R. Platzer, A. Unterhuber, W. Drexler, and R. A. Leitgeb, “Anisotropic aberration correction using region of interest based digital adaptive optics in Fourier domain OCT,” Biomed. Opt. Express 6(4), 1124–1134 (2015). [CrossRef] [PubMed]