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29 March 2021 Common-path interferometer for digital holographic Doppler spectroscopy of living biological tissues
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Abstract

Significance: Common-path interferometers have the advantage of producing ultrastable interferometric fringes compared with conventional interferometers, such as Michelson or Mach–Zehnder that are sensitive to environmental instabilities. Isolating interferometric measurements from mechanical disturbances is important in biodynamic imaging because Doppler spectroscopy of intracellular dynamics requires extreme stability for phase-sensitive interferometric detection to capture fluctuation frequencies down to 10 mHz.

Aim: The aim of this study was to demonstrate that Doppler spectra produced from a common-path interferometer using a grating and a spatial filter (SF) are comparable to, and more stable than, spectra from conventional biodynamic imaging.

Approach: A common-path interferometer using a holographic diffraction grating and an SF was employed with a low-coherence source. Simulations evaluated the spatial resolution. DLD-1 (human colon adenocarcinoma) spheroids were used as living target tissue samples. Power spectra under external vibrations and drug-response spectrograms were compared between common-path and Fourier-domain holographic systems.

Results: The common-path holography configuration shows enhanced interferometric stability against mechanical vibrations through common-mode rejection while maintaining sensitivity to Doppler frequency fluctuations caused by intracellular motions.

Conclusions: A common-path interferometer using a grating and an SF can provide enhanced interferometric stability in tissue-dynamics spectroscopy for drug screening assays.

Tissue-dynamics spectroscopy (TDS) is a fluctuation spectroscopy based on Doppler light scattering from intracellular dynamics1,2 that tracks changes in intracellular motions in response to applied xenobiotics or cancer therapeutics.3,4 The motion sensitivity required for TDS applications also requires extreme mechanical stability for phase-sensitive measurements of Doppler frequency shifts down to 10 mHz. Previous TDS systems used a Mach–Zehnder off-axis holographic configuration that enabled en face optical coherence tomography.5 However, dual-arm interferometers such as Michelson and Mach–Zehnder interferometers are subject to environmental influences such as mechanical vibrations and thermal drifts.

Common-path interferometers611 have been widely applied in a variety of applications, including optical coherence tomography12,13 and digital holography,1419 due to their inherent insensitivity to vibrations. In a common-path configuration, the object and reference waves share the same optical path from the sample interaction volume to the detector. A variety of configurations using a 4-f image system and a diffraction grating at the Fourier plane have been developed.20 In this letter, we propose and demonstrate a common-path interferometer using a grating and a spatial filter (SF) to enable stable TDS of living thick biological tissue for drug screening assays.

The common-path interferometric configuration is developed for reflective mode operation as shown in Fig. 1. A Superlum S840-B-I-20 superluminescent diode, with a center wavelength at 840 nm and a short coherence length of 10  μm, is used as the light source. The probe beam illuminates the target at an oblique angle of 34 deg relative to the backscatter direction. Intracellular transport is isotropic relative to the direction of the incoming wave vector, and the average Doppler frequency shift is zero. The knee frequency of the fluctuation spectrum represents the maximum Doppler frequency shift within the ensemble of scatterers, and the maximum is at the backscattering angle of 180 deg, but we use the slight angled illumination on the sample to eliminate the beam splitter and increase the intensity of the collected light. A 10× microscope objective lens with a long working distance of 30.5 mm and a numerical aperture of 0.26 is employed to collect the light scattered from the target. After relaying the light by lenses L1 and L2 at a magnification of 3:2, a holographic transmission grating (G) at the first Fourier plane (FP1) splits the light into identical +1 and 1 diffraction orders. The phase grating (HOLO/OR LTD) has a 73% transmission efficiency (with a partially quenched zero-order) and a beam separation angle of 2.01 deg. The light is transformed by lens L3 to a second image plane (IP2) where only the first orders pass through the SF. The other diffraction orders are blocked by the SF consisting of a small reference aperture of 0.6 mm diameter and a large object aperture of 3.0 mm in diameter. The two apertures are separated laterally by 5.3 mm. The +1 diffractive order through the large aperture is the object wave, whereas the 1 diffraction order through the small aperture produces the reference wave. An optical Fourier transform is performed by the Fourier lens L4, and the reference and object waves share the same path to create stable interference fringes at the second Fourier plane (FP2). The hologram with a size of 800 by 800 pixels is recorded by a CMOS camera (Basler acA1920-155um) with 12-bit depth at 25 frames per second and an exposure time of 10 ms.

Fig. 1

Experimental set-up of CDH system. MO, microscope objective; M, mirror; FPn, Fourier plane n; IPn, image plane n; Ln, lens n; G, grating; SF, spatial filter, f1=10  cm, f2=15  cm.

JBO_26_3_030501_f001.png

The scattered wave field magnified by the microscope objective MO and the lens L1 is denoted as U(x1,y1) at the image plane IP1. For an ideal binary phase grating with a duty cycle of 0.5 and a π-phase depth, the transmission function of the grating is given as

Eq. (1)

g(x1,y1)=m=+sin(mπ/2)mπ/2exp[j2πmx1Λ],
where m is the diffraction order and Λ is the period of the grating. The diffraction efficiency vanishes for all even values of m and has a maximum of about 40.5% for each of the +1 and 1 orders. In the 4f imaging system of the lenses L2 and L3, the wave Ua(x2,y2) after the SF at IP2 is given as

Eq. (2)

Ua(x2,y2)U(x2+x0,y2)circ[r+/Ro]+U(x2x0,y2)circ[r/Rr],
where x0=λf2/Λ, λ is the wavelength, r+=(x2x0)2+y22, r=(x2+x0)2+y22, and Ro and Rr are the radii of the object and reference aperture, which are centered at +x0 and x0 in the x direction from the center, respectively.

The last lens L4 performs the Fourier transform, and the reference wave Ur(x2,y2) and object wave Uo(x2,y2) at the camera plane FP2 can be expressed as

Eq. (3)

Uo(x2,y2)exp(j2πx0x2λf1)FT[U]Jinc(Roρλf1),

Eq. (4)

Ur(x2,y2)exp(j2πx0x2λf1)FT[U]Jinc(Rrρλf1),
where FT[U] is the Fourier transform of U, Jinc(x)=J1(x)/x in which J1(x) is a Bessel function of the first kind, denotes convolution, and ρ=(x2)2+(y2)2. The reconstruction of the hologram is performed by an inverse Fourier transform and consists of a zero-order term and two conjugate diffraction terms. One diffraction term is reconstructed at (2x0,0) as

Eq. (5)

FT1exp(j4πx0x2λf1){FT[U(x2λf1,y2λf1)]Jinc(Rrρλf1)}*{FT[U(x2λf1,y2λf1)]Jinc(Roρλf1)}.

In common-path digital holography (CDH), the resolution and phase sensitivity of the reconstructed image are determined by the reference-wave term of FT[U]Jinc(Rrρ/λf1) in Eq. (4). In Fourier-domain digital holography (FDH),21 on the other hand, this term is replaced by the plane reference wave and the quality of the reconstructed image is mainly determined by the object wave FT[U]. Therefore, the spatial resolution in FDH is higher than that for CDH.

The digital holograms recorded at the camera plane are numerically transformed using a two-dimensional fast Fourier transform (FFT). An example of target image reconstructions using CDH is shown in Fig. 2(a), where the target is matte white paper containing an oblique cut-out line. The figure shows images of the reference and object beams for the two types of reference aperture at the image plane IP2, and their reconstructed images are shown after the FFT. As shown in Eq. (5), the first-order image when using the small reference aperture with a diameter of 0.6 mm is similar to the target image at IP2, whereas the first-order image for the reference aperture with the same diameter as the object aperture is the autocorrelation of the object. Therefore, it is necessary to optimize the size of the reference aperture for biodynamic imaging to maximize the throughput while maintaining acceptable resolution for biodynamic imaging applications.

Fig. 2

(a) Images for the two types of reference aperture at the IP2 image plane and their reconstructed images. (b) MTFs for four different ratios of Rr/Ro to estimate the resolution of the CDH system. Inset: simulated reconstruction of a slanted-edge phase target with a π-phase depth for Rr/Ro=20%.

JBO_26_3_030501_f002.png

Simulations were performed using Eq. (5) to estimate the optimal size of the reference aperture by measuring the modulation transfer function (MTF). The MTF of an imaging system is defined as the output modulation, Mo, divided by the input modulation, Mi, as a function of spatial frequency. A vertical knife-edge phase target with a π-phase depth and a tilt angle of 2 deg is used as the object image for the simulation, and the MTFs are evaluated from the Fourier transform of the line spread functions22 using the reconstructed images obtained by varying the ratio Rr/Ro from 0.01 to 0.4, as shown in Fig. 2(b).

The reconstructed image for the large reference aperture (100% size ratio) is the autocorrelation of the object image and has no phase information. However, a reference aperture with a smaller diameter at the image plane IP2 acts like a pin hole that makes the reference wave at the FP2 more similar to a plane wave, increasing the spatial resolution, as shown in the graph in Fig. 2(b). However, smaller diameters limit the optical fluence at FP2, which decreases the signal-to-noise ratio and sensitivity in biodynamic imaging. We selected nominal performance using a reference aperture with a 20% ratio relative to the size of the object aperture, and its spatial resolution is estimated to be 8.5 line pairs per mm (lp/mm) in Fig. 2(b). A smaller reference aperture can be used to increase the resolution and phase sensitivity if a source with a higher power or a camera with a higher sensitivity were used. When the reference aperture uses 5% or 10% size ratios, the resolution in Fig. 2(b) can reach 33 or 17  lp/mm, respectively.

We used living DLD-1 (human colon adenocarcinoma) spheroids as a target tissue sample to validate that the CDH system is more stable to external vibrations and more sensitive to biological movements than the noncommon-path FDH system. Tumor spheroids were cultured in a rotating bioreactor for 7–to 14 days until 300 to 600  μm diameter spheroids were formed, and then immobilized with low-gel-temperature agarose in a 96-well plate. To investigate stability against external vibrations, biodynamic imaging of fresh tumor spheroids was performed in both the CDH and FDH modes with and without external vibrations. External vibration was generated by driving a motor with adjustable coupling to the optical table. The speed of the motor was increased until it noticeably affected the interference fringes observed in the hologram at about 6000 rpm. Holograms were recorded for 82 s at a frame rate of 25 frames per second while imaging a fresh tumor spheroid of about 300  μm diameter. Cross-sections (lateral position versus time) in FDH and CDH modes with external vibrations are shown in Fig. 3(a). In the cross-section of FDH in the conventional Mach–Zehnder configuration, the total intensity fluctuates over time caused by the table vibrations, whereas in the common-path CDH, the intensity at each pixel only fluctuates over time due to intracellular motility, and the total intensity is stable.

Fig. 3

(a) DLD-1 living tumor spheroid cross section as a function of position (horizontal axis) and time (vertical axis) in FDH and CDH modes with external vibrations. (b) Power spectra corresponding to (a) compared to those without external vibrations. (c) Power spectra of slowly falling paper. (d) Power spectra of dynamic sample (DLD-1) and static sample (white paper) in FDH and CDH.

JBO_26_3_030501_f003.png

Fluctuation power spectra are acquired by performing temporal FFTs of the time series of multiple reconstructions performed at the specified frame rate as shown in Fig. 3(b). The line plots without markers are the power spectra corresponding to Fig. 3(a), and the plots with markers are for those with no vibration, demonstrating that the CDH system is more stable to external vibration than the FDH system. To compare the effects of external vibrations quantitatively, the relative deviation at a frequency is defined as the absolute difference between the value of the spectral density with and without vibration divided by the value of the spectral density with no vibration. The averages of relative deviations in three bands (lower than 0.1 Hz, between 0.1 and 1 Hz, higher than 1 Hz) were measured to be 4.6%, 3.1%, and 80%, respectively for FDH, whereas 1.8%, 2.5%, and 2.4%, respectively, for CDH when using this high-frequency vibration source. The power spectrum in FDH has considerable noise, especially in the high frequency band, whereas the power spectrum in CDH is relatively smooth for all frequency bands. This curve shows the typical shape of living tissue,23 with a knee at low frequency (0.1 Hz), a power-law roll-off at intermediate frequency, and a floor near the Nyquist frequency. To simulate low-frequency noise, a slowly falling paper target (caused by paper floating of the surface of a water-filled well as the water evaporates) is used as a slow phase modulation source. As shown in Fig. 3(c), the FDH power spectra show peaks at 0.23 and 0.29 Hz for two different paper speeds, but no peaks in the CDH mode. This illustrates the insensitivity of CDH to global phase drift because of common-mode rejection, which is the central reason why it is stable against external mechanical disturbances. Figure 3(d) compares the power spectrum of a dynamic DLD-1 spheroid with the power spectrum of static white paper in FDH and CDH at roughly the same backscatter brightness. Compared to the spectral density of static paper, the white noise in FDH is 2.2 times higher than that in CDH due to the difference in stability against external vibrations. The detection bandwidth in CDH is 4.2 times higher than that in FDH, whereas the dynamic range (DR) of biological samples in FDH is 3.2 times higher than that in CDH due to the more efficient photon collection in FDH. Therefore, the CDH system is insensitive to global phase drift while remaining sensitive to speckle-scale phase fluctuations caused by interfering Doppler frequency shifts from intracellular motions.

To validate that the CDH system is an effective modality for TDS, tissue-response spectrograms tracking time changes in intracellular dynamics in response to drug perturbations were acquired in both FDH and CDH modes as shown in Fig. 4. To make this comparison, the FDH system was operated with maximum vibration isolation to achieve the highest stability relative to CDH. Spectrograms are generated from spectral changes relative to the average baseline spectrum in fluctuation power spectra as a function of time. After six measurements of predose baselines, postdose responses capture the signatures caused by the treatment and are measured 15 times every hour. Cytoskeletal (nocodazole and paclitaxel) and metabolic (iodoacetate) drugs were used, and medium containing 0.1% carrier dimethyl sulfoxide (DMSO) (for drug solubility) was used as a negative control. Nocodazole disrupts microtubules by inhibiting the polymerization of microtubules, whereas paclitaxel stabilizes microtubules by preventing depolymerization. Iodoacetate inhibits the glycolysis that contributes to the rapid growth of cancer cells through the supply of ATP.

Fig. 4

Average spectrograms (three replicates) showing the drug-responses to 0.1% DMSO, iodoacetate, nocodazole, and paclitaxel in FDH and CDH. The drug was applied at t=0. The CDH performance is functionally equivalent to FDH.

JBO_26_3_030501_f004.png

Spectrograms for the negative control in FDH and CDH modes show relatively small response typically seen in continuously proliferating tissue samples, with minor changes over time. The average spectrograms in response to iodoacetate in FDH mode indicates an overall inhibition of cellular activity. In CDH mode, the tissue-response is close to that of FDH mode except for the stronger redshift in CDH at low frequencies. The response to nocodazole showed enhanced low and high frequencies and suppression at mid-frequencies with minor differences between the FDH and CDH modes. The response to paclitaxel closely matches that of nocodazole, except for a weaker response. Therefore, tissue responses to drugs are functionally equivalent between the CDH and FDH modes, whereas the CDH system has the added advantage of stability against environmental influences and may have more flexibility than the FDH system in point-of-care applications despite its lower resolution.

Achieving enhanced interferometric stability using this common-path holography configuration required several trade-offs on biodynamic performance. For instance, the common-path system trades off spatial resolution against holographic fringe contrast on the camera. For the application of imaging tumor spheroids, nominal performance is achieved by limiting the reference aperture to 20% of the image aperture that limits the imaging resolution to 100  μm. This resolution, though insufficient to image individual cells in the tumor, is compatible with the tissue dynamics spectroscopic imaging mode of biodynamic imaging,24 which produces spatial maps of intracellular dynamics across the tumor. Another trade-off is the loss of independent z-control for depth ranging. On the other hand, the low-coherence light source produces a condition of self-coherence-gating that selectively interferes light that shares the same optical path length. This creates a “compressed flythrough” that superposes successive coherence-gated holograms, at increasing depth, onto the digital camera. The Doppler spectra are averaged over all depths of the target weighted by a decreasing exponential function that decays with the reduced extinction coefficient μ. This selectively weights the Doppler information to a depth of 200  μm inside the target with the lateral spatial resolution of 100  μm discussed above. Biodynamic imaging into thick samples with moderate amounts of multiple scattering increases dynamic sensitivity because Doppler frequency shifts accumulate with each scattering event and increases the sensitivity to intracellular motions. Therefore, the 20-μm voxel size of conventional biodynamic imaging is traded for 100-μm voxel size in this common-path system while gaining superior mechanical stability and maintaining full spectral DR for tissue dynamics applications. Measuring changes in intracellular motion in living tissues to extract functional information is a growing area in optical coherence tomography.2528

Disclosures

David Nolte and John Turek have a financial interest in Animated Dynamics, Inc., which is commercializing biodynamic imaging for personalized cancer therapy selection.

Acknowledgments

This work was supported by the National Science Foundation under Grant No. 1911357-CBET.

References

1. 

D. D. Nolte et al., “Holographic tissue dynamics spectroscopy,” J. Biomed. Opt., 16 (8), 087004 (2011). https://doi.org/10.1117/1.3615970 JBOPFO 1083-3668 Google Scholar

2. 

K. Jeong, J. J. Turek and D. D. Nolte, “Speckle fluctuation spectroscopy of intracellular motion in living tissue using coherence-domain digital holography,” J. Biomed. Opt., 15 (3), 030514 (2010). https://doi.org/10.1117/1.3456369 JBOPFO 1083-3668 Google Scholar

3. 

H. Choi et al., “Biodynamic digital holography of chemoresistance in a pre-clinical trial of canine B-cell lymphoma,” Biomed. Opt. Express, 9 (5), 2214 –2228 (2018). https://doi.org/10.1364/BOE.9.002214 BOEICL 2156-7085 Google Scholar

4. 

Z. Li et al., “Intracellular optical doppler phenotypes of chemosensitivity in human epithelial ovarian cancer,” Sci. Rep., 10 (1), 17354 (2020). https://doi.org/10.1038/s41598-020-74336-x SRCEC3 2045-2322 Google Scholar

5. 

K. Jeong, J. J. Turek and D. D. Nolte, “Imaging motility contrast in digital holography of tissue response to cytoskeletal anti-cancer drugs,” Opt. Express, 15 14057 –14064 (2007). https://doi.org/10.1364/OE.15.014057 OPEXFF 1094-4087 Google Scholar

6. 

T. R. Hillman et al., “Near-common-path self-reference quantitative phase microscopy,” IEEE Photonics Technol. Lett., 24 (20), 1812 –1814 (2012). https://doi.org/10.1109/LPT.2012.2214768 IPTLEL 1041-1135 Google Scholar

7. 

X. Wang, M. Zhao and D. D. Nolte, “Common-path interferometric detection of protein on the BioCD,” Appl. Opt., 46 (32), 7836 –7849 (2007). https://doi.org/10.1364/AO.46.007836 APOPAI 0003-6935 Google Scholar

8. 

H. Y. Bai et al., “Common path interferometer based on the modified Michelson configuration using a reflective grating,” Opt. Lasers Eng., 75 1 –4 (2015). https://doi.org/10.1016/j.optlaseng.2015.06.001 Google Scholar

9. 

N. T. Shaked et al., “Reflective interferometric chamber for quantitative phase imaging of biological sample dynamics,” J. Biomed. Opt., 15 (3), 030503 (2010). https://doi.org/10.1117/1.3420179 JBOPFO 1083-3668 Google Scholar

10. 

J. Gluckstad and P. C. Mogensen, “Optimal phase contrast in common-path interferometry,” Appl. Opt., 40 (2), 268 –282 (2001). https://doi.org/10.1364/AO.40.000268 APOPAI 0003-6935 Google Scholar

11. 

Y. Kim et al., “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep., 4 6659 (2014). https://doi.org/10.1038/srep06659 SRCEC3 2045-2322 Google Scholar

12. 

Y. Kim et al., “Common-path diffraction optical tomography for investigation of three-dimensional structures and dynamics of biological cells,” Opt. Express, 22 (9), 10398 –10407 (2014). https://doi.org/10.1364/OE.22.010398 OPEXFF 1094-4087 Google Scholar

13. 

S. Choi et al., “Multifrequency swept common-path en-face OCT for wide-field measurement of interior surface vibrations in thick biological tissues,” Opt. Express, 23 (16), 21078 –21089 (2015). https://doi.org/10.1364/OE.23.021078 OPEXFF 1094-4087 Google Scholar

14. 

V. Srivastava, T. Anna and D. S. Mehta, “Full-field Hilbert phase microscopy using nearly common-path low coherence off-axis interferometry for quantitative imaging of biological cells,” J. Opt., 14 (12), 125707 (2012). https://doi.org/10.1088/2040-8978/14/12/125707 Google Scholar

15. 

P. T. Samsheerali, B. Das and J. Joseph, “Quantitative phase contrast imaging using common-path in-line digital holography,” Opt. Commun., 285 (6), 1062 –1065 (2012). https://doi.org/10.1016/j.optcom.2011.11.092 OPCOB8 0030-4018 Google Scholar

16. 

G. Popescu et al., “Diffraction phase microscopy for quantifying cell structure and dynamics,” Opt. Lett., 31 (6), 775 –777 (2006). https://doi.org/10.1364/OL.31.000775 OPLEDP 0146-9592 Google Scholar

17. 

B. Bhaduri et al., “Diffraction phase microscopy: principles and applications in materials and life sciences,” Adv. Opt. Photonics, 6 57 –119 (2014). https://doi.org/10.1364/AOP.6.000057 AOPAC7 1943-8206 Google Scholar

18. 

J. A. Picazo-Bueno, M. Trusiak and V. Micó, “Single-shot slightly off-axis digital holographic microscopy with add-on module based on beam splitter cube,” Opt. Express, 27 5655 –5669 (2019). https://doi.org/10.1364/OE.27.005655 OPEXFF 1094-4087 Google Scholar

19. 

M. Kumar et al., “Common-path multimodal 3D fluorescence and phase imaging system,” J. Biomed. Opt., 25 (3), 032010 (2020). https://doi.org/10.1117/1.JBO.25.3.032010 JBOPFO 1083-3668 Google Scholar

20. 

M. Finkeldey et al., “Depth-filtering in common-path digital holographic microscopy,” Opt. Express, 25 (16), 19398 –19407 (2017). https://doi.org/10.1364/OE.25.019398 OPEXFF 1094-4087 Google Scholar

21. 

K. Jeong, J. J. Turek and D. D. Nolte, “Fourier-domain digital holographic optical coherence imaging of living tissue,” Appl. Opt., 46 (22), 4999 –5008 (2007). https://doi.org/10.1364/AO.46.004999 APOPAI 0003-6935 Google Scholar

22. 

M. Kenichiro et al., “Modified slanted-edge method and multidirectional modulation transfer function estimation,” Opt. Express, 22 6040 –6046 (2014). https://doi.org/10.1364/OE.22.006040 OPEXFF 1094-4087 Google Scholar

23. 

Z. Li et al., “Doppler fluctuation spectroscopy of intracellular dynamics in living tissue,” J. Opt. Soc. Am. A, 36 (4), 665 –677 (2019). https://doi.org/10.1364/JOSAA.36.000665 JOAOD6 0740-3232 Google Scholar

24. 

Z. Li et al., “Tissue dynamics spectroscopic imaging: functional imaging of heterogeneous cancer tissue,” J. Biomed. Opt., 25 (9), 096006 (2020). https://doi.org/10.1117/1.JBO.25.9.096006 JBOPFO 1083-3668 Google Scholar

25. 

H. M. Leung et al., “Imaging intracellular motion with dynamic micro-optical coherence tomography,” Biomed. Opt. Express, 11 (5), 2768 –2778 (2020). https://doi.org/10.1364/BOE.390782 BOEICL 2156-7085 Google Scholar

26. 

N. J. J. Arezza, M. Razani and M. C. Kolios, “Dynamic light scattering optical coherence tomography to probe motion of subcellular scatterers,” J. Biomed. Opt., 24 (2), 025002 (2019). https://doi.org/10.1117/1.JBO.24.2.025002 JBOPFO 1083-3668 Google Scholar

27. 

L. Yang et al., “Characterizing optical coherence tomography speckle fluctuation spectra of mammary organoids during suppression of intracellular motility,” Quant. Imaging Med. Surg., 10 (1), 76 –85 (2020). https://doi.org/10.21037/qims.2019.08.15 Google Scholar

28. 

M. Muenter et al., “Dynamic contrast in scanning microscopic OCT,” Opt. Lett., 45 (17), 4766 –4769 (2020). https://doi.org/10.1364/OL.396134 OPLEDP 0146-9592 Google Scholar
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Kwan Jeong, Maria Josef Lopera, John J. Turek, and David D. Nolte "Common-path interferometer for digital holographic Doppler spectroscopy of living biological tissues," Journal of Biomedical Optics 26(3), 030501 (29 March 2021). https://doi.org/10.1117/1.JBO.26.3.030501
Received: 15 January 2021; Accepted: 11 March 2021; Published: 29 March 2021
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