Abstract

For many clinical applications, such as dermatology, optical coherence tomography (OCT) suffers from limited penetration depth due primarily to the highly scattering nature of biological tissues. Here, we present a novel implementation of dual-axis optical coherence tomography (DA-OCT) that offers improved depth penetration in skin imaging at 1.3 µm compared to conventional OCT. Several unique aspects of DA-OCT are examined here, including the requirements for scattering properties to realize the improvement and the limited depth of focus (DOF) inherent to the technique. To overcome this limitation, our approach uses a tunable lens to coordinate focal plane selection with image acquisition to create an enhanced DOF for DA-OCT. This improvement in penetration depth is quantified experimentally against conventional on-axis OCT using tissue phantoms and mouse skin. The results presented here suggest the potential use of DA-OCT in situations where a high degree of scattering limits depth penetration in OCT imaging.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Since its first demonstration in 1991, OCT has seen widespread application in the field of ophthalmology due to its micron-scale resolution and depth sectioning capability [1]. Shortly after, the utilization of 1.3 µm light sources led to a marked increase in imaging depth, slightly past 1 mm in biological tissue [2] due to a reduction in scattering at longer wavelengths [3]. Unfortunately, most demonstrations of greater penetration depth have been restricted to tissues with high optical transparency, and penetration using the 1.3 µm wavelength band in highly scattering tissue remained in the 0.5-1.2 mm range [411].

To address this problem, coherence imaging using a dual-axis architecture was introduced in the early 2010s [12]. Conventional OCT imaging relies on detecting ballistic photons, or singly backscattered photons from a specific target layer, to provide near–diffraction-limited resolution. However, the ballistic signal attenuates exponentially, making deep tissue imaging extremely difficult. Furthermore, the single-backscatter model provides an incomplete description of coherent beam propagation in tissue [13], and a significant portion of photons passing through the imaging layers only experience small-angle forward scattering. While these low-order scattering events only introduce minor changes to the propagation trajectories of photons, thus preserving the essential structural tissue information they carry [14], conventional OCT is not capable of collecting these photons. Novel imaging geometries which can collect multiply forward scattered photons have the ability to extend the depth penetration of an interferometric imaging system [12,15,16].

Building off these principles, dual-axis optical coherence tomography (DA-OCT) [17] utilizes a distinct off-axis scanning approach to preferentially detect multiple forward scattered photons to image deep subsurface morphology. Monte Carlo simulations investigating the origin of improved depth performance for DA-OCT have predicted significant signal-to-background ratio (SBR) improvements at most depths [18]. Further, a detailed diffraction theory analysis [19] suggests that the DA-OCT system offers similar scatter-free axial and lateral resolutions as a conventional co-axial OCT system. Importantly, this analysis notes that due to the dual-axis architecture, the depth of field (DOF) of the DA-OCT system is compromised, resulting in a 9-fold decrease in DOF compared to conventional OCT.

Here, we present a new DA-OCT system that combines the inherently reduced scattering at the 1.3 µm wavelength band with the depth enhancement of a dual-axis geometry to improve signal contrast with increasing penetration depth significantly over previous iterations [17,20]. The approach utilizes a custom spectrometer design based on our low-cost architecture [21,22] and incorporates a micro-electromechanical systems (MEMS) mirror for fast beam scanning. With graphics processing unit (GPU) assisted processing, a frame rate of ∼20 frames per second (fps) was achieved, and volumetric imaging was performed in seconds. A previously reported speckle reduction technique using the Dual-Window speckle reduction method [23] (DWSR) was utilized to provide real-time speckle reduction [24].

This study experimentally validates the effects of using a dual-axis scanning geometry to image deeper into turbid media. Conventional OCT scanning was multiplexed to the distal end of the DA-OCT scanning arm to enable a direct comparison of DA-OCT imaging performance against traditional OCT. Imaging phantoms including a complex tissue model, various optical scattering phantoms, and biological samples were imaged to illustrate and analyze the penetration capability of the 1.3 µm DA-OCT system. We show that the effect of reduced DOF can be mitigated by using a tunable lens for dynamic focusing, and by coherently summing images from different scans in a fashion similar to Gabor domain optical coherence microscopy [25] to create an enhanced DOF for DA-OCT, hereinafter referred to DA-DOF+.

2. Methods

2.1 Instrument design

Figure 1 shows the instrument schematic of the 1.3 µm DA-OCT system with DOF+ capabilities. Light from a supercontinuum laser (NKT Photonics, Birkerød, Denmark) was filtered to a bandwidth ranging from 1240 to 1390 nm to create the broadband input for a spectral-domain OCT (SD-OCT) system. A 90:10 fiber coupler was used to split the light into the sample and reference arms for a Mach-Zehnder interferometer. Approximately 15 mW of power illuminated the sample, adjusted with a neutral density filter wheel. A ∼4 mm beam was focused onto the sample by a 100-mm focal length lens and a tunable lens (TL) (f2 = 100-200 mm, Optotune, Dietikon, Switzerland), producing a FWHM lateral spot size measured to be 25.0 µm. The TL allows quick axial scanning of the optical focus to generate an enhanced DOF and compensate for the reduced axial FOV from the dual-axis configuration. Beam scanning was performed using a MEMS mirror (Mirrorcle Technologies, Richmond, CA). The input beam to the sample featured a radial beam divergence (α) of 1.15$^\circ $ (0.02 rad). The illumination was offset from the optical axis of the lenses by $\theta = 3^\circ {\; }({0.05\; \textrm{rad}} )$, which provided the optimal detection for photons with probing depths of 1-2 mm in tissue [18]. Light returned from the sample arm was collected and recombined with the reference arm signal by a second 90:10 fiber coupler. Correct optical path length matching of the reference arm ensures that our system only selects backscattered light accepted by the DA-OCT collection aperture at the desired offset angle. Any signal that retraces the optical path length of our illumination arm does not coherently interfere with the reference arm signal and is thus rejected. Output from the interferometer was directed to a custom spectrometer with a center wavelength of 1307 nm and 98 nm of bandwidth (details below). The lateral FOV of the dual-axis system was determined by the spot size of the illumination beam and the focal length and clear aperture of the focusing lens. The DA-OCT system uses a separate collection aperture with a 4 mm diameter offset by ∼10.5 mm (α=1.15° and θ=3°) on a 100 mm lens with ∼1-inch clear aperture. The optimal lateral scanning range on the sample plane was approximately 2.2 by 1.2 mm in the transverse dimensions. A detailed description of methods used to profile the lateral spatial resolution and field of view (FOV) of DA-OCT is presented in [19].

 figure: Fig. 1.

Fig. 1. A schematic plot of the 1.3µm DA-OCT system. A flip mirror in the sample arm selects between DA-OCT and conventional OCT (On-Axis OCT) scanning modes. (FC - Fiber Coupler; PC - Polarization Control; TL – Tunable Lens).

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A secondary traditional OCT scanning configuration (On-Axis OCT) was implemented for a comparative analysis of DA-OCT’s imaging performance. A separate Michelson-style interferometer was constructed utilizing the same light source and spectrometer as the DA-OCT system. An independent reference arm matched the optical path length of the On-Axis OCT sample arm. The On-Axis OCT’s scanning optics multiplex to the distal end of the DA-OCT scanning arm. A 50 mm focal length lens collimates light output from a 50:50 fiber coupler generating a 14 mm diameter beam as input to the scanning optics. This geometry creates an NA of 0.07 incident to the sample plane, closely matching the $\theta $-angle of the DA-OCT system, which was 3$^\circ $. A flip mirror selects between scanning modes. Entirely switching between modes requires manual replacement of optical fiber connectors at the source and spectrometer junctions and was achieved in a few seconds.

2.2 Custom 3-D printed spectrometer

Figure 2(a) shows a 3D schematic layout of the custom spectrometer design. The spectrometer utilized a loop configuration [21] and was designed using OpticStudio (Zemax). A parabolic mirror collimates input light from the single-mode fiber input before being dispersed by a 1200 lp/mm grating (LightSmyth, Eugene, OR) fixed to a custom grating mount printed using Invar 36 (Carpenter Technology Corporation, Philadelphia, PA). Invar 36 was utilized to limit the thermal expansion of the fused silica substrate of the grating and avoid excessive wavefront distortion. A set of custom-designed lenses, including two achromatic doublets and a negative meniscus lens, were used to focus light onto a 12-bit line scan camera. As a result, the theoretical spot size determined via ray tracing at the detector plane is smaller than the corresponding Airy disk throughout the 98 nm spectrometer bandwidth (Fig. 2(b)), indicating a fully diffraction-limited spectrometer across all wavelengths. The camera sensor has 2048 pixels with a pixel size of 210 × 10 µm and ran at a maximum line rate of 76 kHz (Sensors Unlimited, Princeton, NJ). The pixel-limited spectral resolution was calculated to be 48.8 pm, which corresponds to a theoretical imaging range (zmax) of 8.9 mm in air. The spectrometer body was designed in SolidWorks (Dassault Systèmes, Vélizy-Villacoublay, France) and 3D printed on a MakerBot 2X printer (MakerBot, Brooklyn, NY) using ABS filament in a small form factor of 150 × 120 × 60 mm (L x W x H). Peak system sensitivity was measured to be 107dB, and the 6dB roll-off was found to be ∼2 mm (Fig. 1(c)); see section 2.4 for a description of sensitivity measurements.

 figure: Fig. 2.

Fig. 2. (a) Schematic render of a custom 3D-printed spectrometer assembly featuring a tall-pixel InGaAs array (FM – Fold Mirror; PM – Parabolic Mirror; SM Fiber – Single Mode Fiber). (b) Diffraction-limited spot profile at spectrometer detector plane simulated across 98 nm bandwidth of DA-OCT system. (c) SNR and roll-off of the DA-OCT system.

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2.3 Image acquisition and processing

Raw interferograms were processed by custom software written in LabView (National Instruments, Austin, TX) and operated through a graphical user interface (GUI). A data acquisition card (DAQ) (National Instruments, Austin, TX) synchronized MEMS scanning with the acquisition of an InGaAs array producing a fixed frame rate of 20 fps. Fast Fourier transforms (FFTs), wavelength interpolation, image rendering, and real-time DWSR were parallelized in a graphics processor (Nvidia GeForce GTX 780) to improve processing speed. Additionally, the DAQ controlled the TL, generating variable focal zones beneath the sample surface per FOV acquisition. The curvature of the polymer lens was adjusted by applying current, with a response time (10%-90% step) < 2.5 msec. An acceptable response time needed for the tunable lens to reach the desired position was chosen at 2msec. This slightly increases the total acquisition time depending on the number of desired positions of the lens, however, this does not extend the integration period for each frame. Focal plane calibration was performed by maximizing signal intensity at the bottom of the scattering layer at a known depth using a calibrated hydrogel step phantom. These data were then interpolated to calibrate TL current values to center the focal zone at a desired depth within the sample, assuming a refractive index ∼ 1.4. DA-DOF+ imaging was performed by scanning the optical focus of the DA-OCT system to a set of chosen focal zones. Focal regions were chosen to maximize signal-to-background values according to our prior modeling [18]. Processed B-scans were then averaged to create a fused image with an enhanced DOF.

2.4 System characterization

The system roll-off was measured by translating a mirror reflector along the axial direction on the reference arm with the scanner position fixed at the origin. System sensitivity was measured by placing a mirror on the sample plane and calculating the ratio of the amplitude of the peak signal to the standard deviation of the noise. We characterized the degradation of resolution in various tissue samples by measuring the resolution after propagation through materials with different transport mean free paths (tMFPs). tMFP estimates the amount of scattering experienced by photons while accounting for the different scattering anisotropies (g) in tissue. The measured axial resolution of the DA-OCT system when no scattering is present was 9.4 µm, measured by calculating the FWHM of the signal from a mirror reflector. We characterized the axial resolution degradation in turbid media by imaging the same mirror reflector behind three different samples. The first sample was a tissue-mimicking phantom discussed extensively in an earlier publication [20]. The scattering coefficient and anisotropy of the phantom were designed to be ${\mu _s}$ = 9.1 mm-1 and g = 0.78 to match that of the human dermis [26]. The tMFP of this step phantom was approximately 500 µm. The second sample was chicken breast tissue. The fresh chicken breast was sliced into a wedge shape, and axial resolutions were measured under different tissue thicknesses. The MFP of chicken breast is 43 µm with g = 0.92 [16], which translates to a tMFP of approximately 540 µm. Lastly, pig ear skin was used as the third sample using a similar preparation as the chicken breast. Pigskin has a reduced scattering coefficient of approximately 2.6 mm-1 [27], resulting in a tMFP of approximately 385 µm. The lateral resolution of the DA-OCT system after scattering was measured using the same three tissue samples. Optical pathlengths were converted to physical pathlengths using a tissue refractive index estimation of n = 1.4. For lateral resolution measurements, we placed a mirror reflector under the samples and observed the 90%-10% knife-edge response. In a previous study, the lateral response of the DA-OCT system was theoretically calculated by analyzing its point spread function (PSF) using diffraction theory and defined the theoretical lateral resolution of the DA-OCT system as

$${\delta _x} \approx 0.37\frac{{{\mathrm{\lambda }_0}}}{{\sin (\alpha )}} = 0.37\frac{{{\mathrm{\lambda }_0}}}{{\textrm{NA}}},{\; }$$
corresponding to the the FWHM of intensity PSF along the lateral dimension [19]. From Eq. (1) the predicted 90%-10% knife-edge response was then calculated by
$${\delta _x} = \sqrt {2ln(2 )} {W_0} = 0.78 \cdot \sqrt {2ln(2 )} \cdot {\delta _{x,{\; \; }ke}} \approx 0.92{\; }{\delta _{x,{\; \; }ke}}. $$

2.5 Production of optical scattering phantoms

Gelatin from porcine skin (#G2500; gel strength 300, Type A), Intralipid (#I141-100ML; 20%, emulsion), and sodium azide (#S2002) were purchased from Sigma–Aldrich, St. Louis, Missouri. Concentrations of Intralipid (1% by weight), gelatin (10%), and water (89%) were chosen to obtain a reduced scattering coefficient ($\mu^{\prime}_s$) of ∼10 cm−1 (i.e., $\; \textrm{tMFP}$ ${\approx} 1/\mu ^{\prime}_s$ of ∼ 1 mm @ 532 nm). Ultrapure, deionized, and degassed water were used for all solutions. Protocols for the construction of tissue-mimicking hydrogel phantoms are described by Lai et al. 2014 [28].

3D-printed resin molds for a step phantom were created featuring cascading 250 µm steps in the axial dimension ranging from 0-6 mm deep. This design provides simultaneous visualization of identical interfaces at known depths acting as quantitative landmarks allowing for DA-OCT depth performance characterization. Step phantom molds were printed using a Form2 SLA-type printer with Tough V5 resin (#F2TOTL05, Form Labs, Somerville, MA) with printing XY resolution of 150 µm and 25 µm in Z. Scattering phantom solutions were heated above the gelatin’s melting point of 35°C and poured into the step phantom. A glass microscope slide coated by an aerosolized silicone-based release agent (Pol-Ease 2300; Polytek, Easton, PA) was pressed against the mold to prevent surface deformation while curing at 4°C and removed before imaging.

Although Intralipid closely matches the scattering coefficient of skin, it does not match the desired phase scattering profile of skin for longer wavelengths, with an average particle diameter less than ∼450 nm and anisotropy (g) near 0.35 at λ = 1.3 µm [29]. To better match the scattering phase function of biological tissue, we introduced a suspension of 25 µm polystyrene microspheres (#64165; Polysciences, Warrington, PA) within our previous Intralipid based hydrogel. Simulations performed using MiePlot v4.6 predicted that the inclusion of the more prominent scatterers raised the anisotropy of the solution to g ∼ 0.9 (λ ∼ 1.3 µm) and $\mu ^{\prime}_s\; \sim 16$ cm−1. A visualization of scattering phantoms in a 3d printed step mold is provided as Supplemental Fig S1.

2.6 Animal imaging protocol

In this study, three C57BL/6J wild-type mice were obtained from Jackson Laboratories and maintained through in-house breeding. All animal procedures were approved by the Institutional Animal Care and Use Committee at Duke University. Mice were anesthetized with isoflurane before sacrifice. A chemical depilatory (Nair; Church & Dwight, Ewing, NJ) was used for hair removal, and tissue was washed in 1X DPBS before imaging.

2.7 Dual-axis performance comparison against conventional OCT

The first round of comparative images captured using both DA-OCT and conventional (on-axis) OCT was performed on a complex tissue model (#TSC-10; Simulab, Seattle, WA), mimicking several layers of skin, subcutaneous fat, fascia, and pre-peritoneal fat made of synthetic material. Before imaging, we inserted an 8 mm long 28-gauge insulin needle (Easy Touch, Houston, Tx) into the phantom. The needle was stable within the tissue phantom at a fixed angle without external supports, though the leverage applied by the weight of the attached syringe caused a slight protuberance of the surface of the phantom. The focal plane of the system was positioned 500 µm beneath the surface of the phantom. Due to the narrow lateral FOV of the DA-OCT system, the sample was imaged on a single-axis translation stage featuring a 25 mm travel micrometer head (#150-811ST; Thorlabs, Newton, NJ) to include a more extensive range. 10-frame average OCT B-scans were acquired at 0.64 mm lateral spacing, carefully following the insertion profile of the needle. This process was performed in both On-Axis OCT and dual-axis configurations across the same ROI with and without an enhanced DOF. For imaging in tissue phantoms, DA-DOF+ imaging was performed by scanning the optical focus of the DA-OCT system from 500 µm to 1400 µm within the sample at ten 100 µm increments.

Composite images were stitched together during post-processing using custom MATLAB software. After insertion, the weight of the syringe resting on the tissue sample caused its surface to bulge. To accurately quantify signal strength as a function of penetration depth, manual segmentation of the needle interface and exterior surface of the sample was performed in each image to account for the influence of surface curvature on the thickness. Contrast-to-noise ratio (CNR) was chosen as the primary metric to determine image quality for each mode. CNR is similar to the metric signal-to-noise ratio (SNR) but subtracts a neighboring ROI term before taking the ratio to evaluate contrast. This is an important distinction when there is a significant bias in one mode, such as noise in the form of diffusely scattered light, which has scattered throughout the medium but is accepted by our system when its pathlength is within one coherence length of our interferometer. Contrast-to-noise ratio (CNR) calculations were performed at each point along the traced path of the needle using the equation

$$\textrm{CNR} = \frac{{|{{\mu_s} - {\mu_m}} |}}{{\sqrt {\sigma _s^2 + \sigma _m^2} }}$$
where ${\mu _s}$ and ${\mu _m}$ are the mean pixel count of the needle profile and surrounding media, respectively, and ${\sigma _s}$ and ${\sigma _m}$ are the corresponding standard deviation. From Eq. (3), we identify that a threshold of CNR < 1 for the needle profile compared to surrounding media as indistinguishable from noise. Mean and standard deviation values were measured across a 5 × 5-pixel area and 20 × 20-pixel area for the needle and media regions, respectively. The surrounding media was sampled by 20 pixels at each point shifted to the right from above the signal measurement corresponding to the needle location. To quantify CNR as a function of depth across various imaging modes, we developed a metric referred to as the half-maximum CNR imaging depth (HCID). These values represent the measured imaging depth corresponding to an imaging fall-off of half the maximum observed CNR value. Similar methods were applied to imaging and analyzing both hydrogel-based step phantoms imaged for DA-OCT and On-Axis OCT modes.

Step phantom imaging was performed in a fashion similar to the previous needle experiments. 10-frame average OCT B-scans were acquired at 0.32 mm lateral spacing perpendicular to the descending steps features. Three parallel imaging planes were imaged separated 1mm apart. Reported CNR measurements represent the mean. Scattering media measurements were recorded 20 pixels shifted above the corresponding signal measurement at the step-media interface location.

For murine needle experiments, imaging was conducted on the midline of the dorsum, roughly at the level of the base of the scapulae. For needle insertion experiments, OCT scanning was performed perpendicular to the needle insertion to ensure the needle localization within the FOV. The sample stage was translated in 1 mm intervals along the insertion direction using methods described in section 2.4 to image needle penetration for increasing depth. For imaging in tissue, enhanced DOF imaging was performed by acquiring repeated image frames with the optical focus of the tunable lens placed at 500 µm (3 frames), 750 µm (3 frames), and 1000 µm (4 frames). These images were then compiled to create 10-frame averaged B-scans with an enhanced DOF. The percentage of black points within a single DA-DOF+ frame was thresholded to improve contrast and reduce background noise. The adjusted signal histogram was fit to a Gaussian distribution and used to calibrate subsequent images. A previously developed method for histogram-matching [30] was applied to provide uniform contrast comparison across datasets. For each modality, 10 neighboring A-scans that intersect the needle were averaged and plotted as a function of depth for three specific imaging sites (s1-s3). The background was selected manually as the scattering layer above the needle and compared to the needle signal for CNR analysis. We report the mean and standard deviation across three repeat acquisitions for each needle site. Repeat trials were performed by slightly shifting the specimen after each pair of on-axis OCT and DA-OCT images were recorded. A statistical analysis of calculated CNR values across imaging modes was performed by paired t-test using custom MATLAB software.

3. Results

3.1 DA-OCT imaging performance at 1.3 µm

Due to scattering, the DA-OCT system's spatial resolution in tissue depends on the tissue type and penetration depth. The DA-OCT system's theoretical scatter-free axial resolution is determined by the coherence length of the light source [31] and was calculated to be 7.6 µm in air. Experimentally measured axial resolutions under the three samples were plotted against various tMFPs in Fig. 3(a). Optical pathlengths were converted to physical pathlengths using a tissue refractive index estimation of n = 1.4. The measurements from 3 different types of tissue were in good agreement. The axial resolution of the DA-OCT system was ∼50 µm after 5-6 tMFPs.

 figure: Fig. 3.

Fig. 3. Measured (a) axial and (b) lateral resolution of a 1.3 µm DA-OCT system under various tissue samples. The transport mean free path (tMFP) is defined as the reciprocal of the reduced scattering coefficient.

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Similarly, the theoretical lateral resolution calculated based on Eq. (2) was 24.1 µm. In comparison, the measured scatter-free lateral resolution was 25.0 µm in air. Lateral resolution measured under various tissue samples ranged from ∼100 µm at two tMFPs to 300 µm near six tMFPs. Again, the measurements from three different types of tissue remained in good agreement.

3.2 Dynamic focusing using a tunable lens

Images comparing DA-OCT and On-Axis OCT visualization of a 28-gauge needle penetrating a surgical training phantom are presented in Fig. 4(a) with corresponding CNR measurements plotted as a function of penetration depth. The needle is visible near the surface on the left-hand side of the sample and extends deeper below the surface from left to right. From visual inspection alone, intensity values at the needle-phantom interface appear to be higher for DA-OCT, particularly at around a depth of 500 µm beneath the surface of the phantom, which is the same depth where the optical focus for both systems is centered. CNR measurements show no significant advantage for either scanning mode as values at half maximum CNR, corresponding to HCID values of 1.35 mm and 1.33 mm, for DA-OCT and On-Axis OCT, respectively.

 figure: Fig. 4.

Fig. 4. Composite OCT b-scans of a 28-gauge needle penetrating a surgical training phantom for both DA-OCT and On-Axis OCT. (a) 10-frame averaged composite images with a fixed focal plane with normalized CNR with respect to needle penetration. (b) Averaged OCT b-scans with ten incremental focal planes ranging from 500 µm – 1500 µm beneath the surface of the phantom with normalized CNR concerning needle penetration. Colored dashed lines indicate the half-maximum CNR imaging depth (HCID) corresponding to imaging mode. Scale bars = 1 mm.

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Figure 4(b) shows the same ROI imaged with DA-OCT and On-Axis OCT using an enhanced DOF method. Qualitative assessment of the OCT b-scans for both OCT+ and DA-DOF+ show a modest improvement in depth performance for DA-DOF+ against the other imaging modes. The corresponding HCID values were measured to be 1.53 mm and 1.18 mm, for DA-OCT and On-Axis OCT using an enhanced DOF, respectively, corresponding to a 29.7% improvement in CNR fall-off versus depth for DA-OCT over On-Axis OCT and a 15.0% improvement over On-Axis OCT without an enhanced DOF.

3.3 DA-OCT imaging of highly forward scattering media

To better interpret the results of the previous section, a calibrated step-phantom setup using an Intralipid-based hydrogel phantom ($\mu ^{\prime}_s$ ∼1 mm−1; g ∼ 0.35), was imaged in DA-OCT, On-Axis OCT, and DA-DOF+ modes. Representative images are presented with corresponding CNR measurements plotted as a function of penetration depth in Fig. 5(a)-(b). A Gaussian curve was fitted to CNR measurements to parametrize their behaviors, with related goodness of fit (R2) measured at 0.95, 0.95, and 0.91 for DA-OCT, On-Axis OCT, and DA-DOF+, respectively. The HCID threshold points in CNR were measured to be 1.75 mm, 1.76 mm, and 2.07 mm for DA-OCT, On-Axis OCT, DA-DOF+, respectively. These values indicate no improvement (in fact, a slight reduction) in the effective depth of non-DOF+ DA-OCT over On-Axis OCT and a 17.0% increase for DA-DOF+ over On-Axis OCT.

 figure: Fig. 5.

Fig. 5. Composite OCT b-scans of a calibrated phantom featuring 250 ± 25 µm steps imaged by DA-OCT, On-Axis OCT, and DA-DOF+. (a) Composite images (10-frame averaged b-scans) using an Intralipid-based hydrogel phantom and (b) corresponding mean CNR measurements (N=3) at the mold/media interface concerning imaging depth. (c) Composite images (10-frame averaged b-scans) using an Intralipid-based hydrogel phantom with increased forward scattering with (d) corresponding mean CNR measurements (N=3) at the mold/media interface with respect to imaging depth. Colored dashed lines indicate the half-maximum CNR imaging depth (HCID) corresponding to imaging mode. CNR data fit to Gaussian curves for visualization. Scale bars = 1 mm.

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To more closely simulate the scattering characteristics of living tissue, an Intralipid-based hydrogel phantom with increased forward anisotropy at 1.3 µm ($\mu^{\prime}_s{\; }\sim 1.6$ mm−1; g ∼ 0.9) was produced and imaged in the same modes. Representative images and CNR measurements are presented in Fig. 5(c)-(d). Gaussian curve fitting was again used for improved visualization, with corresponding goodness of fit (R2) of 0.98, 0.87, 0.92 for DA-OCT, On-Axis OCT, and DA-DOF+, respectively. HCID values were measured to be 1.73 mm, 1.82 mm, and 2.38 mm, for DA-OCT, On-Axis OCT, DA-DOF+, respectively. As in the low anisotropy case, no DA depth enhancement was observed in the absence of DOF+, but importantly, DA-DOF+ demonstrated a ∼31% increase over On-Axis OCT in CNR-equivalent depth using DA-DOF+.

3.4 In-vivo DA-OCT imaging

We aimed to validate the capabilities of DA-OCT for improved depth penetration by tracing a needle's CNR profile in turbid media using a mouse skin model. Figure 6(a)-(d) shows a lateral projection OCT image perpendicular to the needle insertion direction with mean intensity profiles plotted as a function of penetration depth. Images follow the needle at three sampling sites (s1-s3) each separated by 1 mm to monitor the signal strength with depth penetration below the tissue surface (Fig. 6(e)). Needle penetration depth was evaluated using ImageJ. Mean tissue-surface to needle-surface depths were found to be 1.30 ± 0.05 mm and 2.5 ± 0.07 mm at s2 and s3, respectively.

 figure: Fig. 6.

Fig. 6. OCT B-scans of a 28-gauge pen needle penetrating mouse skin imaged at a 1 mm interval along the insertion of the needle using four imaging modes: (a) On-Axis OCT, (b) DA-OCT, (c) On-Axis OCT with DOF+, and (d) DA-DOF+. Averaged A-line profiles (N=10) for each image correspond to colored ROIs shown. (e) Illustration of the needle insertion site, OCT scanning orientation (red lines), and definition of sampling sites s1-s3. (f) Contrast-to-noise (CNR) analysis for On-Axis OCT with DOF + (red) and DA-DOF+ (blue) concerning sample/needle penetration. Red and blue dashed lines represent On-Axis OCT and DA-OCT using a fixed focal zone centered at f = 0.75 mm, respectively, grey line represents CNR = 1. Error bars represent the standard deviation of three repeat measurements. Scale bars = 1 mm.

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Results of On-Axis OCT and DA-OCT with a static focal zone centered at 0.75 mm are presented in Figs. 6(a) and 6(b), respectively. For On-Axis OCT, a CNR value of 2.2 ± 0.3 was observed at the needle-tissue boundary of s2 with the variation given as the standard deviation seen across three unique acquisitions. For On-Axis OCT, the needle was not observed visually at s3, consistent with its measured CNR of 0.1 ± 0.1. For DA-OCT, CNR values of 6.5 ± 0.9 and 1.1 ± 0.2 were observed at the needle-tissue boundary for s2 and s3, respectively. These values indicate an improvement in signal strength at s2 with DA-OCT over On-Axis OCT, a 195.5% increase at a depth greater than 1 mm in highly scattering tissue. In Fig. 6(b), the effect of the limited DOF is observed for DA-OCT when the optical focus is centered 0.75 mm beneath the surface. For s1 the needle contrast (CNR) was 66.4% lower using DA-OCT versus On-Axis OCT because the needle surface is located away from the system’s focal plane.

The enhanced DOF technique was applied for On-Axis OCT and resulted in CNR values of 2.6 ± 0.4, and 0.3 ± 0.2 at the needle-tissue boundary for s2 and s3, respectively, demonstrating no significant improvement in CNR values when compared to those measured without an enhanced DOF. For DA-DOF+, CNR values of 7.0 ± 1.3 and 1.6 ± 0.4 were observed at the needle-tissue boundary for s2 and s3, respectively. These values correspond to a 169.2% improvement in signal strength for DA-DOF+ over On-Axis OCT with an enhanced DOF and 218.2% improvement over On-Axis OCT with a fixed focus. With DA-DOF+ we observed increased specular reflection from the needle; the narrower illumination and detection apertures increase the depth range over which a reflector can deflect the illumination beam into the detection aperture. The two imaging techniques show qualitatively similar performance at the tissue surface; however, the needle signal quickly falls off for On-Axis OCT beneath the surface. This is confirmed by the mean CNR and standard deviation observed for three repeated trials at each sample site presented as Fig. 6(f). A CNR of 1.6 for DA-DOF+ at s3 demonstrates that objects >2.5 mm of penetration depth into highly scattering mouse skin can be isolated. A paired t-test was performed against a null hypothesis of equal CNR performance of both imaging modes, and the p-value was 0.03, indicating a significant difference between the two techniques to image the needle signal past 1 mm of tissue.

4. Discussion

We have presented a novel 1.3 µm DA-OCT system for deep tissue imaging. The dual-axis architecture provided improved penetration capability in both technical phantoms and mouse skin compared to conventional On-Axis OCT. Imaging depths of up to 2 mm were demonstrated in skin tissue with sensitivity to highly reflective objects at even greater depths. The resolution of the DA-OCT system was characterized and found to be similar to that of a conventional co-axial OCT system when no scattering tissue was present. The dual-axis architecture has a limited impact on the resolution of the system when both the NA of the beam and offset angle (θ) are negligible. As predicted, both axial and lateral resolutions were degraded after multiple scattering events with a more significant degradation in the lateral resolution compared to that of the axial resolution after traveling through the same tMFP. Importantly, however, we showed that DA-OCT allows contrast from deep objects to be preserved when DOF enhancement is applied to correct for the DOF limitations inherent to its scanning geometries.

Our imaging results used CNR as a comparison to assess the depth profiling ability of DA-OCT. Without focal zone adjustment, we observed no significant improvement for DA-OCT over On-Axis OCT for experiments with a needle embedded in a surgical tissue phantom. Though our modeling shows that the focal zone of the DA-OCT system near 1-2 mm into the skin provides a higher signal-to-background (hence better image contrast) than a co-axial OCT system, this enhancement depends on maintained overlap between illumination and detection beam foci. When focus tracking was implemented using the tunable lens, an enhanced DOF was realized for On-Axis OCT and DA-OCT, but in this case, a 30% improvement in HCID fall-off using DA-OCT was observed.

Although imaging of the surgical phantom showed improved image penetration using DA-OCT, it is comprised synthetic materials with uncharacterized optical properties. Greater insight was achieved by using calibrated step-phantoms with an Intralipid-based hydrogel medium. Again, the imaging advantage of DA-OCT was only seen using our DA-DOF+ method, producing a 17% increase in our HCID value over DA-OCT. Considering that DA-OCT’s imaging advantage arises from its ability to preserve the detection of information-carrying photons scattered at small angles, as is common in biological tissue, samples that exhibit isotropic scattering will not benefit from this technique. Introducing 25 µm polystyrene microspheres to increase scattering anisotropy to a more biological range increased DA-DOF+ depth performance overall by 15% and produced up to a 31% increase in CNR fall-off depth for DA-DOF+ over conventional OCT. The advantages of DA-OCT are therefore only realized when anisotropy is high, as in many biological tissues. While 1.3 µm imaging inherently increases penetration due to decreased scattering, the reduction of tissue anisotropy at this wavelength also limits the additional benefit conferred by DA-OCT.

Our in vivo results demonstrate a potential imaging situation where DA-OCT is highly effective. We were able to identify the position of a needle past an insertion depth of 2.5 mm. After a 5 mm round trip through scattering tissue, a conventional on-axis OCT system struggles to distinguish a photon that has interacted with the needle surface from one that has undergone several scattering events and maintained a similar total optical path. By displacing the detection aperture relative to the illumination, DA-OCT preserves the detection of the photons that have been weakly scattered by the needle surface while reducing the contribution of photons with more pronounced scattering angles. We expect DA-OCT to be useful in similar situations, such as monitoring the placement of an implant, imaging the insertion of a stimulating electrode, or performing an anatomical scan.

5. Conclusion

To summarize, we have developed DA-OCT technology for increased depth priority in highly-scattering media using 1.3 µm wavelengths and enhanced DOF using a dynamic focusing method. This study experimentally validates our approach against conventional On-Axis OCT across various imaging samples and highlights the importance of multiple forward scattering events for DA-OCT to achieve superior imaging performance. Future work will leverage these results to provide a more significant advantage in tissues with substantial forward scattering, such as brain and breast tissues. These results indicate that using this approach will offer improved OCT imaging in deep tissue environments.

Funding

National Science Foundation (CBET-2009841, IIP-1827560).

Acknowledgements

The authors thank Venkat Thirveedi and Diana Li for their assistance with animal models.

Disclosures

A.W., Lumedica, Inc. (I, C, S); M.C., Lumedica Inc. (I); E.T.J, Lumedica Inc. (C)

Data availability

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

Supplemental document

See Supplement 1 for supporting content.

References

1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991). [CrossRef]  

2. J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995). [CrossRef]  

3. B. E. Bouma and G. Tearney, “Clinical imaging with optical coherence tomography,” Academic Radiology 9(8), 942–953 (2002). [CrossRef]  

4. C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998). [CrossRef]  

5. J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

6. J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996). [CrossRef]  

7. C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999). [CrossRef]  

8. G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

9. G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997). [CrossRef]  

10. Z. Wang, H.-C. Lee, O. O. Ahsen, B. Lee, W. Choi, B. Potsaid, J. Liu, V. Jayaraman, A. Cable, and M. F. Kraus, “Depth-encoded all-fiber swept source polarization sensitive OCT,” Biomed. Opt. Express 5(9), 2931–2949 (2014). [CrossRef]  

11. E. Li, S. Makita, Y.-J. Hong, D. Kasaragod, and Y. Yasuno, “Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography,” Biomed. Opt. Express 8(3), 1290–1305 (2017). [CrossRef]  

12. M. G. Giacomelli and A. Wax, “Imaging beyond the ballistic limit in coherence imaging using multiply scattered light,” Opt. Express 19(5), 4268–4279 (2011). [CrossRef]  

13. J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994). [CrossRef]  

14. R. K. Wang, “Signal degradation by multiple scattering in optical coherence tomography of dense tissue: a Monte Carlo study towards optical clearing of biotissues,” Phys. Med. Biol. 47(13), 2281–2299 (2002). [CrossRef]  

15. T. E. Matthews, M. G. Giacomelli, W. J. Brown, and A. Wax, “Fourier domain multispectral multiple scattering low coherence interferometry,” Appl. Opt. 52(34), 8220–8228 (2013). [CrossRef]  

16. T. E. Matthews, M. Medina, J. R. Maher, H. Levinson, W. J. Brown, and A. Wax, “Deep tissue imaging using spectroscopic analysis of multiply scattered light,” Optica 1(2), 105–111 (2014). [CrossRef]  

17. Y. Zhao, W. J. Eldridge, J. R. Maher, S. Kim, M. Crose, M. Ibrahim, H. Levinson, and A. Wax, “Dual-axis optical coherence tomography for deep tissue imaging,” Opt. Lett. 42(12), 2302–2305 (2017). [CrossRef]  

18. Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019). [CrossRef]  

19. Y. Zhao, “Deep Tissue Imaging with Dual Axis Optical Coherence Tomography” (Duke University, 2018).

20. Y. Zhao, J. R. Maher, M. M. Ibrahim, J. S. Chien, H. Levinson, and A. Wax, “Deep imaging of absorption and scattering features by multispectral multiple scattering low coherence interferometry,” Biomed. Opt. Express 7(10), 3916–3926 (2016). [CrossRef]  

21. S. Kim, M. Crose, W. J. Eldridge, B. Cox, W. J. Brown, and A. Wax, “Design and implementation of a low-cost, portable OCT system,” Biomed. Opt. Express 9(3), 1232–1243 (2018). [CrossRef]  

22. G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021). [CrossRef]  

23. F. Robles, R. N. Graf, and A. Wax, “Dual window method for processing spectroscopic optical coherence tomography signals with simultaneously high spectral and temporal resolution,” Opt. Express 17(8), 6799–6812 (2009). [CrossRef]  

24. Y. Zhao, K. K. Chu, W. J. Eldridge, E. T. Jelly, M. Crose, and A. Wax, “Real-time speckle reduction in optical coherence tomography using the dual window method,” Biomed. Opt. Express 9(2), 616–622 (2018). [CrossRef]  

25. J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.

26. L. J. Steven, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013). [CrossRef]  

27. Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001). [CrossRef]  

28. P. Lai, X. Xu, and L. V. Wang, “Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time,” J. Biomed. Opt. 19(3), 035002 (2014). [CrossRef]  

29. V. Kodach, D. Faber, J. Van Marle, T. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express 19(7), 6131–6140 (2011). [CrossRef]  

30. G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019). [CrossRef]  

31. J. A. Izatt and M. A. Choma, “Theory of optical coherence tomography,” in Optical Coherence Tomography (Springer, 2008), pp. 47–72.

References

  • View by:

  1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
    [Crossref]
  2. J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
    [Crossref]
  3. B. E. Bouma and G. Tearney, “Clinical imaging with optical coherence tomography,” Academic Radiology 9(8), 942–953 (2002).
    [Crossref]
  4. C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
    [Crossref]
  5. J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).
  6. J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
    [Crossref]
  7. C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
    [Crossref]
  8. G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).
  9. G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
    [Crossref]
  10. Z. Wang, H.-C. Lee, O. O. Ahsen, B. Lee, W. Choi, B. Potsaid, J. Liu, V. Jayaraman, A. Cable, and M. F. Kraus, “Depth-encoded all-fiber swept source polarization sensitive OCT,” Biomed. Opt. Express 5(9), 2931–2949 (2014).
    [Crossref]
  11. E. Li, S. Makita, Y.-J. Hong, D. Kasaragod, and Y. Yasuno, “Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography,” Biomed. Opt. Express 8(3), 1290–1305 (2017).
    [Crossref]
  12. M. G. Giacomelli and A. Wax, “Imaging beyond the ballistic limit in coherence imaging using multiply scattered light,” Opt. Express 19(5), 4268–4279 (2011).
    [Crossref]
  13. J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
    [Crossref]
  14. R. K. Wang, “Signal degradation by multiple scattering in optical coherence tomography of dense tissue: a Monte Carlo study towards optical clearing of biotissues,” Phys. Med. Biol. 47(13), 2281–2299 (2002).
    [Crossref]
  15. T. E. Matthews, M. G. Giacomelli, W. J. Brown, and A. Wax, “Fourier domain multispectral multiple scattering low coherence interferometry,” Appl. Opt. 52(34), 8220–8228 (2013).
    [Crossref]
  16. T. E. Matthews, M. Medina, J. R. Maher, H. Levinson, W. J. Brown, and A. Wax, “Deep tissue imaging using spectroscopic analysis of multiply scattered light,” Optica 1(2), 105–111 (2014).
    [Crossref]
  17. Y. Zhao, W. J. Eldridge, J. R. Maher, S. Kim, M. Crose, M. Ibrahim, H. Levinson, and A. Wax, “Dual-axis optical coherence tomography for deep tissue imaging,” Opt. Lett. 42(12), 2302–2305 (2017).
    [Crossref]
  18. Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019).
    [Crossref]
  19. Y. Zhao, “Deep Tissue Imaging with Dual Axis Optical Coherence Tomography” (Duke University, 2018).
  20. Y. Zhao, J. R. Maher, M. M. Ibrahim, J. S. Chien, H. Levinson, and A. Wax, “Deep imaging of absorption and scattering features by multispectral multiple scattering low coherence interferometry,” Biomed. Opt. Express 7(10), 3916–3926 (2016).
    [Crossref]
  21. S. Kim, M. Crose, W. J. Eldridge, B. Cox, W. J. Brown, and A. Wax, “Design and implementation of a low-cost, portable OCT system,” Biomed. Opt. Express 9(3), 1232–1243 (2018).
    [Crossref]
  22. G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
    [Crossref]
  23. F. Robles, R. N. Graf, and A. Wax, “Dual window method for processing spectroscopic optical coherence tomography signals with simultaneously high spectral and temporal resolution,” Opt. Express 17(8), 6799–6812 (2009).
    [Crossref]
  24. Y. Zhao, K. K. Chu, W. J. Eldridge, E. T. Jelly, M. Crose, and A. Wax, “Real-time speckle reduction in optical coherence tomography using the dual window method,” Biomed. Opt. Express 9(2), 616–622 (2018).
    [Crossref]
  25. J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.
  26. L. J. Steven, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
    [Crossref]
  27. Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
    [Crossref]
  28. P. Lai, X. Xu, and L. V. Wang, “Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time,” J. Biomed. Opt. 19(3), 035002 (2014).
    [Crossref]
  29. V. Kodach, D. Faber, J. Van Marle, T. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express 19(7), 6131–6140 (2011).
    [Crossref]
  30. G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
    [Crossref]
  31. J. A. Izatt and M. A. Choma, “Theory of optical coherence tomography,” in Optical Coherence Tomography (Springer, 2008), pp. 47–72.

2021 (1)

G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
[Crossref]

2019 (2)

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019).
[Crossref]

2018 (2)

2017 (2)

2016 (1)

2014 (3)

2013 (2)

2011 (2)

2009 (1)

2002 (2)

R. K. Wang, “Signal degradation by multiple scattering in optical coherence tomography of dense tissue: a Monte Carlo study towards optical clearing of biotissues,” Phys. Med. Biol. 47(13), 2281–2299 (2002).
[Crossref]

B. E. Bouma and G. Tearney, “Clinical imaging with optical coherence tomography,” Academic Radiology 9(8), 942–953 (2002).
[Crossref]

2001 (1)

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

1999 (2)

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

1998 (1)

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

1997 (2)

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

1996 (1)

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

1995 (1)

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

1994 (1)

J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
[Crossref]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Ahsen, O. O.

Boppart, S.

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

Boppart, S. A.

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Bouma, B.

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Bouma, B. E.

B. E. Bouma and G. Tearney, “Clinical imaging with optical coherence tomography,” Academic Radiology 9(8), 942–953 (2002).
[Crossref]

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

Brezinski, M.

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

Brezinski, M. E.

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Brown, W. J.

Cable, A.

Cariveau, M.

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Chien, J. S.

Choi, W.

Choma, M. A.

J. A. Izatt and M. A. Choma, “Theory of optical coherence tomography,” in Optical Coherence Tomography (Springer, 2008), pp. 47–72.

Chu, K.

G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
[Crossref]

Chu, K. K.

Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019).
[Crossref]

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

Y. Zhao, K. K. Chu, W. J. Eldridge, E. T. Jelly, M. Crose, and A. Wax, “Real-time speckle reduction in optical coherence tomography using the dual window method,” Biomed. Opt. Express 9(2), 616–622 (2018).
[Crossref]

Cox, B.

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

S. Kim, M. Crose, W. J. Eldridge, B. Cox, W. J. Brown, and A. Wax, “Design and implementation of a low-cost, portable OCT system,” Biomed. Opt. Express 9(3), 1232–1243 (2018).
[Crossref]

Crose, M.

Du, Y.

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

Eckhaus, M.

J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
[Crossref]

Eldridge, W. J.

Faber, D.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Fujimoto, J.

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

Fujimoto, J. G.

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Giacomelli, M. G.

Goodman, A.

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

Graf, R. N.

Gregory, K.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Hee, M. R.

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Herrmann, J. M.

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

Hong, Y.-J.

Hu, X. H.

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

Huang, D.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Ibrahim, M.

Ibrahim, M. M.

Izatt, J. A.

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

J. A. Izatt and M. A. Choma, “Theory of optical coherence tomography,” in Optical Coherence Tomography (Springer, 2008), pp. 47–72.

Jayaraman, V.

Jelly, E. T.

G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
[Crossref]

Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019).
[Crossref]

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

Y. Zhao, K. K. Chu, W. J. Eldridge, E. T. Jelly, M. Crose, and A. Wax, “Real-time speckle reduction in optical coherence tomography using the dual window method,” Biomed. Opt. Express 9(2), 616–622 (2018).
[Crossref]

Jesser, C.

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

Kalkman, J.

Kalmus, G. W.

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

Kasaragod, D.

Kendall, W. Y.

G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
[Crossref]

Kim, S.

Knuttel, A.

J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
[Crossref]

Kobayashi, K.

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

Kodach, V.

Kraus, M. F.

Kulkarni, M. D.

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

Lai, P.

P. Lai, X. Xu, and L. V. Wang, “Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time,” J. Biomed. Opt. 19(3), 035002 (2014).
[Crossref]

Lee, B.

Lee, H.-C.

Lee, K.

J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.

Levinson, H.

Li, E.

Libus, J. J.

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

Lin, C. P.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Liu, J.

Lu, J. Q.

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

Ma, X.

Y. Du, X. H. Hu, M. Cariveau, X. Ma, G. W. Kalmus, and J. Q. Lu, “Optical properties of porcine skin dermis between 900 nm and 1500 nm,” Phys. Med. Biol. 46(1), 167–181 (2001).
[Crossref]

Maher, J. R.

Makita, S.

Matthews, T. E.

Medina, M.

Meemon, P.

J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.

Murali, S.

J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.

Pitris, C.

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

Potsaid, B.

Puliafito, C. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Robles, F.

Rolland, J. P.

J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.

Schmitt, J. M.

J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
[Crossref]

Schuman, J. S.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Sivak, M. V.

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

Song, G.

G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
[Crossref]

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

Southern, J.

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

Southern, J. F.

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Stamper, D. L.

J. M. Herrmann, C. Pitris, B. E. Bouma, S. Boppart, C. Jesser, D. L. Stamper, J. Fujimoto, and M. E. Brezinski, “High resolution imaging of normal and osteoarthritic cartilage with optical coherence tomography,” The J. Rheumatol. 26, 627–635 (1999).

Steven, L. J.

L. J. Steven, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
[Crossref]

Stinson, W. G.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Swanson, E. A.

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref]

Tearney, G.

B. E. Bouma and G. Tearney, “Clinical imaging with optical coherence tomography,” Academic Radiology 9(8), 942–953 (2002).
[Crossref]

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human urologic tissue using optical coherence tomography,” The J. Urol. 157(5), 1915–1919 (1997).
[Crossref]

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

Tearney, G. J.

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Thompson, K.

J. P. Rolland, P. Meemon, S. Murali, K. Thompson, and K. Lee, “Gabor domain optical coherence microscopy,” in Design and Quality for Biomedical Technologies III (International Society for Optics and Photonics, 2010), p. 75560A.

Ulrich, J. N.

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

van Leeuwen, T.

Van Marle, J.

Wang, H.-W.

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

Wang, L. V.

P. Lai, X. Xu, and L. V. Wang, “Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time,” J. Biomed. Opt. 19(3), 035002 (2014).
[Crossref]

Wang, R. K.

R. K. Wang, “Signal degradation by multiple scattering in optical coherence tomography of dense tissue: a Monte Carlo study towards optical clearing of biotissues,” Phys. Med. Biol. 47(13), 2281–2299 (2002).
[Crossref]

Wang, Z.

Wax, A.

G. Song, E. T. Jelly, K. Chu, W. Y. Kendall, and A. Wax, “A review of low-cost and portable optical coherence tomography,” Prog. Biomed. Eng. 3(3), 032002 (2021).
[Crossref]

Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019).
[Crossref]

G. Song, K. K. Chu, S. Kim, M. Crose, B. Cox, E. T. Jelly, J. N. Ulrich, and A. Wax, “First clinical application of low-cost OCT,” Trans. Vis. Sci. Tech. 8(3), 61 (2019).
[Crossref]

S. Kim, M. Crose, W. J. Eldridge, B. Cox, W. J. Brown, and A. Wax, “Design and implementation of a low-cost, portable OCT system,” Biomed. Opt. Express 9(3), 1232–1243 (2018).
[Crossref]

Y. Zhao, K. K. Chu, W. J. Eldridge, E. T. Jelly, M. Crose, and A. Wax, “Real-time speckle reduction in optical coherence tomography using the dual window method,” Biomed. Opt. Express 9(2), 616–622 (2018).
[Crossref]

Y. Zhao, W. J. Eldridge, J. R. Maher, S. Kim, M. Crose, M. Ibrahim, H. Levinson, and A. Wax, “Dual-axis optical coherence tomography for deep tissue imaging,” Opt. Lett. 42(12), 2302–2305 (2017).
[Crossref]

Y. Zhao, J. R. Maher, M. M. Ibrahim, J. S. Chien, H. Levinson, and A. Wax, “Deep imaging of absorption and scattering features by multispectral multiple scattering low coherence interferometry,” Biomed. Opt. Express 7(10), 3916–3926 (2016).
[Crossref]

T. E. Matthews, M. Medina, J. R. Maher, H. Levinson, W. J. Brown, and A. Wax, “Deep tissue imaging using spectroscopic analysis of multiply scattered light,” Optica 1(2), 105–111 (2014).
[Crossref]

T. E. Matthews, M. G. Giacomelli, W. J. Brown, and A. Wax, “Fourier domain multispectral multiple scattering low coherence interferometry,” Appl. Opt. 52(34), 8220–8228 (2013).
[Crossref]

M. G. Giacomelli and A. Wax, “Imaging beyond the ballistic limit in coherence imaging using multiply scattered light,” Opt. Express 19(5), 4268–4279 (2011).
[Crossref]

F. Robles, R. N. Graf, and A. Wax, “Dual window method for processing spectroscopic optical coherence tomography signals with simultaneously high spectral and temporal resolution,” Opt. Express 17(8), 6799–6812 (2009).
[Crossref]

Xu, X.

P. Lai, X. Xu, and L. V. Wang, “Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time,” J. Biomed. Opt. 19(3), 035002 (2014).
[Crossref]

Yadlowsky, M.

J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
[Crossref]

Yasuno, Y.

Zhao, Y.

Academic Radiology (1)

B. E. Bouma and G. Tearney, “Clinical imaging with optical coherence tomography,” Academic Radiology 9(8), 942–953 (2002).
[Crossref]

Am. J. Gastroenterol. (1)

G. Tearney, M. Brezinski, J. Southern, B. Bouma, S. Boppart, and J. Fujimoto, “Optical biopsy in human gastrointestinal tissue using optical coherence tomography,” Am. J. Gastroenterol. 92(10), 1800–1804 (1997).

Am. J. Respir. Crit. Care Med. (1)

C. Pitris, M. E. Brezinski, B. E. Bouma, G. J. Tearney, J. F. Southern, and J. G. Fujimoto, “High resolution imaging of the upper respiratory tract with optical coherence tomography: a feasibility study,” Am. J. Respir. Crit. Care Med. 157(5), 1640–1644 (1998).
[Crossref]

Appl. Opt. (1)

Biomed. Opt. Express (5)

IEEE J. Sel. Top. Quantum Electron. (1)

J. A. Izatt, M. D. Kulkarni, H.-W. Wang, K. Kobayashi, and M. V. Sivak, “Optical coherence tomography and microscopy in gastrointestinal tissues,” IEEE J. Sel. Top. Quantum Electron. 2(4), 1017–1028 (1996).
[Crossref]

J. Biomed. Opt. (1)

P. Lai, X. Xu, and L. V. Wang, “Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time,” J. Biomed. Opt. 19(3), 035002 (2014).
[Crossref]

J. Biophotonics (1)

Y. Zhao, K. K. Chu, E. T. Jelly, and A. Wax, “Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study,” J. Biophotonics 12(6), e201800383 (2019).
[Crossref]

Nat. Med. (1)

J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, S. A. Boppart, B. Bouma, M. R. Hee, J. F. Southern, and E. A. Swanson, “Optical biopsy and imaging using optical coherence tomography,” Nat. Med. 1(9), 970–972 (1995).
[Crossref]

Obstet. Gynecol. (1)

C. Pitris, A. Goodman, S. A. Boppart, J. J. Libus, J. G. Fujimoto, and M. E. Brezinski, “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999).
[Crossref]

Opt. Express (3)

Opt. Lett. (1)

Optica (1)

Phys. Med. Biol. (4)

J. M. Schmitt, A. Knuttel, M. Yadlowsky, and M. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
[Crossref]

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[Crossref]

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Supplementary Material (1)

NameDescription
Supplement 1       Step mold optical phantom

Data availability

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

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Figures (6)

Fig. 1.
Fig. 1. A schematic plot of the 1.3µm DA-OCT system. A flip mirror in the sample arm selects between DA-OCT and conventional OCT (On-Axis OCT) scanning modes. (FC - Fiber Coupler; PC - Polarization Control; TL – Tunable Lens).
Fig. 2.
Fig. 2. (a) Schematic render of a custom 3D-printed spectrometer assembly featuring a tall-pixel InGaAs array (FM – Fold Mirror; PM – Parabolic Mirror; SM Fiber – Single Mode Fiber). (b) Diffraction-limited spot profile at spectrometer detector plane simulated across 98 nm bandwidth of DA-OCT system. (c) SNR and roll-off of the DA-OCT system.
Fig. 3.
Fig. 3. Measured (a) axial and (b) lateral resolution of a 1.3 µm DA-OCT system under various tissue samples. The transport mean free path (tMFP) is defined as the reciprocal of the reduced scattering coefficient.
Fig. 4.
Fig. 4. Composite OCT b-scans of a 28-gauge needle penetrating a surgical training phantom for both DA-OCT and On-Axis OCT. (a) 10-frame averaged composite images with a fixed focal plane with normalized CNR with respect to needle penetration. (b) Averaged OCT b-scans with ten incremental focal planes ranging from 500 µm – 1500 µm beneath the surface of the phantom with normalized CNR concerning needle penetration. Colored dashed lines indicate the half-maximum CNR imaging depth (HCID) corresponding to imaging mode. Scale bars = 1 mm.
Fig. 5.
Fig. 5. Composite OCT b-scans of a calibrated phantom featuring 250 ± 25 µm steps imaged by DA-OCT, On-Axis OCT, and DA-DOF+. (a) Composite images (10-frame averaged b-scans) using an Intralipid-based hydrogel phantom and (b) corresponding mean CNR measurements (N=3) at the mold/media interface concerning imaging depth. (c) Composite images (10-frame averaged b-scans) using an Intralipid-based hydrogel phantom with increased forward scattering with (d) corresponding mean CNR measurements (N=3) at the mold/media interface with respect to imaging depth. Colored dashed lines indicate the half-maximum CNR imaging depth (HCID) corresponding to imaging mode. CNR data fit to Gaussian curves for visualization. Scale bars = 1 mm.
Fig. 6.
Fig. 6. OCT B-scans of a 28-gauge pen needle penetrating mouse skin imaged at a 1 mm interval along the insertion of the needle using four imaging modes: (a) On-Axis OCT, (b) DA-OCT, (c) On-Axis OCT with DOF+, and (d) DA-DOF+. Averaged A-line profiles (N=10) for each image correspond to colored ROIs shown. (e) Illustration of the needle insertion site, OCT scanning orientation (red lines), and definition of sampling sites s1-s3. (f) Contrast-to-noise (CNR) analysis for On-Axis OCT with DOF + (red) and DA-DOF+ (blue) concerning sample/needle penetration. Red and blue dashed lines represent On-Axis OCT and DA-OCT using a fixed focal zone centered at f = 0.75 mm, respectively, grey line represents CNR = 1. Error bars represent the standard deviation of three repeat measurements. Scale bars = 1 mm.

Equations (3)

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δ x 0.37 λ 0 sin ( α ) = 0.37 λ 0 NA ,
δ x = 2 l n ( 2 ) W 0 = 0.78 2 l n ( 2 ) δ x , k e 0.92 δ x , k e .
CNR = | μ s μ m | σ s 2 + σ m 2