The motions of biomolecules and biological cells are of great importance in maintaining the biological function of living systems [1]. For example, diffusive and vibrational movements of intracellular molecules such as proteins or nucleic acids are central to the biological processes of cells such as cell growth [1, 2], and red blood cells (RBCs) moving through the blood vessels deliver oxygen and nourishment to the surrounding tissues, which is crucial for tissue viability [3]. Furthermore, the beating of multi-ciliated cells (cilia) in airways is responsible for mucociliary clearance to keep the respiratory tract clean [4]. These cellular and intra cellular motions can be indicative biomarkers for evaluating cell metabolism and further our health states, therefore necessitating the development of optical microscopy techniques to measure biological dynamics.

There have been conventional optical microscopic methods [5] to measure spatial and temporal intracellular motions such as total internal reflection fluorescence microscopy (TIRFM) [6] and dark-field optical microscopy (DFM) [7]. TIRFM has been used to observe the motion of single molecules in cells; an evanescent wave under total internal reflection (TIR) can penetrate to around 100 nm into the bottom of the cell to excite the targeted molecules for fluorescence emission detection [6]. DFM, a non-fluorescence method, can detect only scattered signals from a sample by using a dark field condenser blocking the unscattered light, and has been used to monitor the interactions between molecules in cells with no dye [7]. These methods are suitable for identifying and tracking the diffusion of molecules within single cells in culture.

The dynamics within tissues in vivo have mainly been investigated by using low coherence optical interferometry techniques such as optical coherence tomography (OCT) [8]. OCT is a well-evaluated three-dimensional (3D) imaging tool that detects light signals backscattered from tissue layers in depth, thereby providing microstructural information within the tissue. The recent progress in OCT technology has enabled it to detect subtle spatiotemporal dynamics in the tissue involving blood circulation [9] or subcellular fluctuation [10]. More recently, full-field OCT (FFOCT), a variant of ultra-high resolution OCT, is an emerging tool that has been demonstrated to visualize the intracellular dynamics in tissue with sub-micrometer spatial resolution and millisecond temporal resolution [11_–13]. This functional FFOCT, called “dynamic FFOCT (D-FFOCT)” generates a colored image with an endogenous contrast linked to organelle motility at millisecond scales, and has provided a new way of quantitatively assessing cellular physiology in a wide range of tissues and organs in normal or pathological conditions [11_–13].

Despite promising results, for D-FFOCT measurements, surrounding noise can affect the generation of a dynamic contrast image. Because to current D-FFOCT employs a standard FFOCT setup based on a Linnik interferometer [14] with respective reference and sample arms, the dual-arm geometry in the system is highly sensitive to the surrounding noise; ambient vibrations or impacts can mechanically displace the arms of the system, and these displacements can cause changes in the path length of the light traveling to each arm, resulting in fluctuation in the optical path-length difference (OPD) between both arms. Accordingly, interference signals generated from the sample can be unintentionally varied under noise. This signal modulation can be especially critical for D-FFOCT imaging because it may be large enough to bury the small signal changes by the motions of organelles, which could hamper the reconstruction of the dynamic contrast image. Therefore, great care and much effort to mitigate the effect of noise should be taken for every measurement.

In this paper, we propose a method for stable D-FFOCT imaging by adapting common-path interferometry (CPI) in the FFOCT system. Unlike Linnik interferometry, CPI is designed such that the reference and sample arms share a single beam path [15, 16] to ensure that they experience equal mechanical displacement, making the OPD by external noise nominally zero. Therefore, this interferometric scheme is insensitive to the surrounding noise [15, 16], which is beneficial for detecting dynamic signals from a sample. The CPI and nearly CPI such as Mirau interferometry have been intensively used in OCT and phase microscopy to realize noise-insensitive imaging [15_–20]. However, the previous work has mainly been aimed at imaging cell surfaces [17, 18] or static tissue structures [19, 20]. The aim of this study is to investigate the utility of CPI to D-FFOCT for stabilized imaging of the dynamics within biological samples in noisy environments. A comparison study of the noise effect on D-FFOCT imaging was conducted with the conventional and proposed FFOCT setups subject to different surrounding noises, and the results demonstrated the superior insensitivity of CPI to external noise. Subsequently, the dynamics of several biological samples in a noisy environment were imaged using the proposed method.

2.1. FFOCT Setups

We implemented a conventional FFOCT system with a Linnik interferometer [Fig. 1(a)] and the proposed CPI-based FFOCT system [Fig. 1(b)]. Details of the conventional FFOCT system are described in a previous paper [14]. In our study, briefly, a 700 mW, 625 nm centered light-emitting diode (LED) (M625L4; Thorlabs Inc., NJ, USA) with a spectral bandwidth of 17 nm at a full-width at half-maximum (FWHM) was used as a partially coherent light source. The output beam from the light source was split into two beams by using a 50/50 cube beam splitter (BS) and directed to the reference arm and sample arm. After that, the reference and sample beams were focused through a pair of 10× objective lenses (OBJ) (UMPLFLN10XW, 0.3 NA and a working distance of 3.5 mm; Olympus Inc., Tokyo, Japan) onto the surfaces of a silver-coated flat mirror and the sample surface, respectively. The beams back-reflected from the reference mirror (slide glass) and the sample are re-combined at the BS, thereby generating interference if the OPD is within the coherence volume (coherence gate). The optical intensity of the reference beam was adjusted by placing a neutral density filter (not shown) in the reference arm to enhance the interference signal contrast. Finally, the interference signals were detected by a high-speed CMOS camera [VC-2MC-M340, 70 frames per second (FPS) and 2048 × 1088 pixels; Vieworks Inc., Anyang, Korea].

Figure 1. Schematics of conventional FFOCT system (a), and the proposed CPI-based FFOCT system (b). (c) A series of interference images consecutively captured by a CMOS camera, and (d) the resulting dynamic contrast image processed by calculating the STD of pixel intensities of the interference image data. In the image contrast, the hotter areas represent more dynamic contrast and the colder vice versa. FFOCT, full-field optical coherence tomography; CPI, common-path interferometry; CMOS, complementary metal-oxide semiconductor camera; STD, standard deviation; LED, light-emitting diode; BS, beam splitter; OBJ, objective lens; PA, piezoelectric actuator.

The CPI-based FFOCT [Fig. 1(b)] was built by simply modifying the conventional FFOCT platform in Fig. 1(a). To achieve the CPI, the reference arm of the built-up FFOCT system was blocked, and a 1-mm-thick light transparent slide glass was carefully situated above the surface of the sample. The position of the glass plate was precisely adjusted by a piezoelectric actuator (PA) (PAZ005; Thorlabs, Inc.) anchoring the other side of the glass plate using a strong magnet, and the positioner was assembled on the sample stage. Because the glass plate was treated as a flat partial reflector, the sample beam can be partially reflected from the glass and the subsequent sample. Considering the low coherence length of the light source, interference is generated by reuniting the beams reflected from the glass bottom and the sample along the same path. The use of CPI in the FFOCT setup makes it possible to suppress the ambient noise-induced fluctuations in the interference signals (enabling stabilized D-FFOCT imaging). However, a 623.8 nm laser line filter (FL632.8-1, FWHM = 1 ± 0.2 nm; Thorlabs Inc.) was placed in front of the LED to increase the coherence length (l_c) defined as 2ln2/π λ₀²/∆λ, where λ₀ and Δλ denote the centroid and bandwidth of the LED spectrum, respectively. According to the formula, the coherence length of light passing through the filter can be extended from 10 μm to 172 μm, thus easing the alignment of the glass plate to generate the interference.

2.2. D-FFOCT Imaging

When interference occurs, the vibrational motions of the intracellular particles within the coherence gate can cause temporal changes in the interference signal intensity, and this change is used as a dynamic contrast to image the organelles in motion. For this, the interference images were consecutively acquired by using the CMOS camera [Fig. 1(c)], and then the dynamic contrast (D) was calculated as the standard deviation (STD) of the intensities of each pixel in the recorded image set as follows [12]:

(1)$$D\left(x,y\right)=\sqrt{\frac{1}{N}{\displaystyle \sum _{i=1}^N{(I\left(x,y,t_i\right)-<I\left(x,y\right)>)}^2}}$$

where N is the total number of image frames captured and I(x, y, t_i) represents the intensity of the arbitrary pixel I(x, y) in the i-th frame, and <I(x, y)> is the mean value of the intensities of pixel (x, y) in all of the frames. Thus, the pixel-wise calculation yields a dynamic contrast image. The image processing was applied to the interference image set obtained using the conventional FFOCT and the proposed one. Figure 1(d) shows an example of a colored dynamic-contrast image processed from 70 interference images [Fig. 1(c)]. The redder contrast (larger STDs) indicates large fluctuations in pixel intensities over time, corresponding to larger movements of organelles, whereas the bluer contrast (lower STDs) represents less or no mobility.

2.3. Sample Preparation and Experimental Conditions

To evaluate the stability of the D-FFOCT measurements, a taped glass attached to a piezo actuator was prepared as a simple scattering bio-sample. The taped glass was axially shifted by operating the actuator to simulate the global motion of intracellular particles: the oscillation frequency was set to 3 Hz, considering the typical motion frequencies of organelles (less than 25 Hz) [21] and the sample oscillation range was limited to 1 μm to describe the subtle movement of the organelles in single cells (less than 10 μm in thickness) compressed in a tissue. After careful alignment between the sample and the glass plate to generate the interference throughout the sample surface, D-FFOCT imaging of the sample was performed with the conventional FFOCT and the CPI-based FFOCT systems in various noisy environments: ambient noise (e.g., building noise) and artificial noise (e.g., low-frequency tapping on the system table).

Also, live samples were prepared for D-FFOCT imaging. A freshwater shrimp (Paratya compressa) was gently immersed in cold water for 10 seconds to anesthetize the animal and then placed on the sample stage. To avoid motion artifacts caused by breathing or heartbeats, a peripheral uropod in the tail fan distant from the heart was selected for imaging. In addition, a fresh human hair follicle was pulled out of a healthy 26-year-old female scalp and was longitudinally imaged for 25 days to monitor dynamic changes in the hair.

3.1. Comparison of the Effect of External Noise on D-FFOCT Measurements

The proposed method is expected to be less sensitive to external noise owing to its common-path configuration. To validate the noise immunity, we compared D-FFOCT images of the sample (taped glass) exposed to surrounding noise, obtained by using the conventional double-path FFOCT and the proposed common-path FFOCT. At first, D-FFOCT measurements were made for the sample with no piezo motion, considered stationary particles or static tissue. Figures 2(a) and 2(b) show dynamic contrast images of the motionless sample with building noise obtained using the common-path FFOCT and double-path FFOCT, respectively. In Fig. 2(a), the contrast is marginal, whereas Fig. 2(b) exhibits large magnitudes in contrast that should not be seen for the stationary sample. This high contrast is contributed by time-varying interference signals [Fig. 2(c)], which fluctuated more in the double-path than the common-path one. Note that the interference signal was taken at a single pixel in the interference image data, with a maximal contrast (yellow circle) in the dynamic-contrast image. Figure 2(d) displays the distribution of contrasts in Figs. 2(a) and 2(b) as error bars (mean ± STD), in which the mean contrast was 3.37 for the double-path system, higher than that (1.96) of the common-path system. Moreover, the variation in contrast values (STD) was much wider for the double-path system. This indicates that the conventional D-FFOCT would be more subject to ambient noise compared to the D-FFOCT with CPI.

Figure 2. Comparison of dynamic contrast full-field optical coherence tomography (D-FFOCT) imaging of a scattering sample (taped glass) using the conventional method and the proposed method in noisy environments. (a) and (b) Dynamic contrast images of the sample with no motion but subject to surrounding building noise, obtained by the proposed common-path FFOCT and the double-path FFOCT, respectively. (c) Intensities taken at single pixels [marked as yellow circles in (a) and (b)] of the interference image dataset. (d) Error bar graph showing the distribution of contrasts in (a) and (b). (e) and (f) Dynamic contrast images of the same sample subject to 1 Hz tapping on the system table, obtained by the common-path FFOCT and the double-path FFOCT, respectively. (g) Intensities taken at single pixels [marked as yellow circles in (e) and (f)] of the interference image dataset. (h) Error bar graph showing the distribution of contrasts in (e) and (f). (i) and (j) Dynamic contrast images of the sample in oscillation at 3 Hz with the tapping. (k) Intensities taken at single pixels [marked as yellow circles in (i) and (j)]. (l) Spectra of the pixel intensity profiles in (k).

After applying 1 Hz tapping on the system table, however, the contrast was much higher [Fig. 2(f)] for the double-path system because of worse fluctuation in the interference signals by the tapping [see Fig. 2(g)]. In contrast, the common-path FFOCT showed no significant differences in dynamic contrast [Fig. 2(e)] and signal fluctuation [Fig. 2(g)] relative to Figs. 2(a) and 2(c). This is evident from the distributions of the dynamic contrasts [Fig. 2(h)], in which the mean of the contrasts (1.91) by the common-path FFOCT remained to the result (1.96) with no tapping. However, for the double-path system, the mean contrast significantly increased to 9.68. In particular, the maximum of contrast was over 37 for the double-path system, tenfold greater than the maximum (3.56) of contrast for the common-path system. The results show that the interference signals from common-path interferometry are more stable in noisy and vibrational environments.

Next, we obtained D-FFOCT images of the same sample in vibration, caused by axial motion of the piezo actuator driven by a 3 Hz sine wave. Tapping was kept up during the measurements. Figures 2(i) and 2(j) show dynamic contrast images obtained using the common-path FFOCT and the double-path FFOCT systems, respectively. The contrast in the image is more prominent for the double-path system, but its interference signal was not periodic, but random [Fig. 2(k)]. The signal distortion may be due to the intervention of external noise in the sample motion, making it confounding to interpret the sample dynamics. In the common-path system, on the other hand, a sinusoidal interference signal appeared [Fig. 2(k)], and its frequency was found to be ~3 Hz as a main peak frequency in the fast Fourier transform (FFT) spectra of the interference signal [Fig. 2(l)], the same as the frequency of the PZT motion. This indicates that the interference signal is only affected by the sample’s motion. The second peak at 6 Hz may be caused by the nonlinear motion of the slide glass. Since the end of the slide glass was attached to the PZT, it can behave as a cantilever so that the cantilevered slide glass may be oscillated by the clamped base in a harmonic motion due to inertia and damping effect [22]. The result demonstrates that the use of common-path interferometry in FFOCT can offer noise-isolated time-varying interference signals that are caused by the sample, guaranteeing true sample dynamics in the contrast image.

3.2. D-FFOCT Imaging of Bio-Samples Using CPI-based FFOCT

After validating the noise isolation ability of the CPI-based FFOCT, dynamic imaging using the method was performed on live biological samples. In each case, 210 interference images were consecutively acquired by the CMOS camera at a frame rate of 70 Hz, and the image acquisition time was 3 s. Figure 3(a) shows a bright-field microscope image of the uropod of an anesthetized freshwater shrimp and Fig. 3(b) is a close-up view of the yellow box in Fig. 3(a). Figure 3(c) is the corresponding pseudo-colored D-FFOCT image of Fig. 3(a). The color contrast in Fig. 3(c) may represent the intracellular dynamic motion taking place in the uropod [23, 24].

Figure 3. Dynamic contrast imaging of biological samples. (a) Bright-field microscope image of a uropod in the tail fan of an anesthetized freshwater shrimp (Paratya compressa), (b) Zoomed-in image of a yellow box in (a), and (c) a corresponding color-coded dynamic contrast image. (d) Bright-field microscope image of a plucked hair follicle with intact hair bulb, (e) zoomed-in image of the yellow box in (d), and (f) a corresponding color-coded dynamic contrast image. (g) Time-lapsed dynamic contrast images of the hair follicle 0, 16, 21, 24, and 25 days after plucking.

Furthermore, we imaged a fresh hair follicle with an intact hair root harvested from a healthy young female scalp. Fig. 3(d) shows a bright-field microscope image of a fresh dark brown hair follicle near a hair bulb, and Fig. 3(e) is a close-up view of the yellow box in Fig. 3(e). Figure 3(f) is a corresponding D-FFOCT image of Fig. 3(e), showing the dynamics in color. The contrast in Fig. 3(f) is believed to have resulted from the metabolic activity of hair cells such as epithelial follicular stem cells even after plucking. In addition, time-course D-FFOCT imaging of the hair was conducted for 25 days to monitor the hair cell viability over time. Figure 3(g) shows representative dynamic contrast images of the hair follicle on days 0, 16, 21, 24, and 25 immediately after plucking the hair. It is interesting that the contrast remained for more than three weeks and then disappeared, indicating that the plucked hair follicle slowly underwent cell death over at least a few weeks. Intriguingly, we found the displacement of the contrasted area shifted by 42.8, 58, and 66.6 μm from the initial position (reference line) on day 0, probably as a result of hair growth [25, 26], which was confirmed by slight increments (~2.6 μm per day) in the hair length. The hair follicle was probably in the active growth phase (anagen) when plucked, and the stem cells residing in the hair bulge area divided to promote hair growth despite apoptosis [27].

The comparison study experimentally showed that double-path interferometry-based FFOCT, currently being used for dynamic contrast imaging, is very sensitive to surrounding noise or external vibrations, which makes it necessary to use techniques or apparatus dedicated to suppressing external perturbations for stable interference image acquisition. However, the proposed CPI-based FFOCT was 10-fold more insensitive to noise compared to the conventional one. The use of the proposed method enabled stable interference image acquisition without any special procedures in noisy environments and yielded more reliable dynamic contrast imaging. Therefore, we believe that the proposed approach will be very useful for stabilized D-FFOCT imaging of both single cells in culture and live tissues. However, the present system lacks tomographic imaging capability. Depth-resolved imaging would be possible by adding an auxiliary interferometer to the system [28, 29], which is a system modification underway.

The authors declare no conflicts of interest.

Data underlying the results presented in this paper are not publicly available at the time of publication, but can be obtained from the authors upon reasonable request.

Supported in part by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A5A1018052); Chung-Ang University Research Scholarship Grants in 2021.