# Endoscopic en-face optical coherence tomography and fluorescence imaging
using correlation-based probe tracking

Manuel J. Marques  1,*,${\dagger}$ Michael R. Hughes  1,${\dagger}$ Adrián F.
Uceda  1 Grigory Gelikonov  2 Adrian Bradu  1 and Adrian Podoleanu1  1Applied
Optics Group, School of Physical Sciences, Division of Natural Sciences,
University of Kent, Canterbury CT2 7NH, United Kingdom
2Institute of Applied Physics RAS, Nizhny Novgorod, Russia.
${\dagger}$Both authors contributed equally to this publication.
<EMAIL_ADDRESS>
###### Abstract

Forward-viewing endoscopic optical coherence tomography (OCT) provides 3D
imaging in vivo, and can be combined with widefield fluorescence imaging by
use of a double-clad fiber. However, it is technically challenging to build a
high-performance miniaturized 2D scanning system with a large field-of-view.
In this paper we demonstrate how a 1D scanning probe, which produces cross-
sectional OCT images (B-scans) and 1D fluorescence T-scans, can be transformed
into a 2D scanning probe by manual scanning along the second axis. OCT volumes
are assembled from the B-scans using speckle decorrelation measurements to
estimate the out-of-plane motion along the manual scan direction. Motion
within the plane of the B-scans is corrected using image registration by
normalized cross correlation. En-face OCT slices and fluorescence images,
corrected for probe motion in 3D, can be displayed in real-time during the
scan. For a B-scan frame rate of 250 Hz, and an OCT lateral resolution of
approximately $20\text{\,}\mathrm{\SIUnitSymbolMicro m}$, the approach can
handle out-of-plane motion at speeds of up to 4 mm/s.

††journal: boe

##  1 Introduction

Endoscopic optical coherence tomography (OCT) allows high-resolution imaging
of tissue to a depth of 1-2 mm beneath the surface. It was first demonstrated
as early as a few years after the first report of OCT for the eye [1], and has
since been commercialized for use in coronary arteries, complementing
intravascular ultrasound for interventional guidance [2]. There are other
potential applications in the area of imaging the epithelium of internal
organs, particularly for early diagnosis of cancerous lesions or for lateral
margin identification during endoscopic surgery.

Endoscopic OCT probes can be broadly divided into ‘side-viewing’ and ‘forward-
viewing’ designs. Side-viewing probes involve deflecting the beam exiting a
fiber by 90 degrees, using a prism or mirror or by angle-cleaving the fiber.
Scanning can be achieved by rotating the fiber using a motor at the proximal
end, outside of the patient [1, 3]. Alternatively, the deflecting element may
be fixed onto a micro-motor at the distal end of the probe, in which case the
fiber and lens assembly itself does not need to rotate [4, 5, 6]. 3D imaging
is achieved by moving the probe axially, obtaining ‘tunnel-like’ images. Side-
viewing probes can have very small diameters; needle probes with diameters as
small as a few hundred microns having been demonstrated [7].

Side-viewing probes are ideal for imaging tubular-shaped structures such as
vasculature or parts of the gastro-intestinal tract. They are less well-suited
to general endoscopic use where it may be desirable to have a forward-looking
image, such as at branches of the airways, in organs such as the bladder or
the stomach, or for image-guided surgery. For this reason, forward-viewing
probes have also been developed, although not successfully commercialized.
There are two common approaches to miniaturization of forward-viewing scanning
probes. The first, and most common, is to scan the fiber itself laterally,
usually behind a lens [8]. It is also possible to integrate a miniature fiber-
based lens into the fiber itself [9]. The second is to incorporate a miniature
beam-scanning element using a micro-electromechanical (MEMS) mirror surface
[10], although the need to fold the beam makes this less suitable for narrow-
diameter probes. In either case, the scanning can either be in one direction
only, producing 2D cross-sectional OCT images, or in two directions, allowing
2D en-face images and volumes to be assembled.

The design of forward-viewing OCT endoscopic probes is challenging in several
respects. The probe would normally be introduced to the body via the working
channel of an endoscope, limiting both the maximum possible probe diameter (to
around 3 mm) and the length of the rigid tip. It is technically difficult to
build a fast 2D scanning element at low cost and in a sufficiently small
package. The field-of-view tends to be small since, unlike side-viewing probes
which can rotate a full $360^{\circ}$, forward-viewing probes only scan over a
small arc. For example, while recent state-of-the-art forward-scanning compact
probe heads can acquire several volumes per second, typical image sizes are
0.8 mm [9], 0.9 mm [11], 1.3 mm [12], 1.5 mm [13], 2 mm [14] and 3.5 mm [15].
To maintain an acceptable depth-of-field for endoscopic use, lateral
resolution is typically 10-30 µm, resulting in a relatively small number of
lateral resolution elements in the image.

In this paper we attempt to address some of these difficulties by introducing
a new concept whereby scanning along only one axis is performed using a distal
scanning mechanism, and the raster scanning (or scanning along the orthogonal
axis) is completed by a manual bending mechanism of an endoscope. This relaxes
the requirements on the distal scanning mechanism, and means that OCT volumes
can be much larger along the manual-scan (slow-scan) direction than those that
could be produced by any feasible miniaturized distal scanning mechanism
within the probe itself. This new approach comes with its own challenges,
particularly the difficulty of assembling artifact-free volumes during manual
scanning. In our previous work using a 1D scanning endoscope and a robotic
actuator to perform the slow-scan [16], volumes were assembled simply by
assuming constant speed in the slow-scan direction. When using a manually-
scanned endoscope, a constant scanning speed or direction cannot be assumed,
and so the instantaneous velocity of the probe must be determined.

We propose here a solution involving a speckle correlation-based algorithm for
determining motion in the direction out-of-plane of the B-scans, and
demonstrate the feasibility of the approach in simulated imaging experiments.
The scanning probe incorporates a double-clad fiber for simultaneous
fluorescence imaging, and we show that we can also assemble 2D fluorescence
images from a combination of mechanical and manual scanning, using the data
from the OCT channel for registration. While dual fluorescence/OCT imaging has
been demonstrated repeatedly using side-viewing probes [17, 18, 19, 20] there
have been comparatively fewer forward-viewing dual-mode probes [21]. Using
manual scanning on one axis, we are able to obtain fluorescence images several
millimeters in length.

The problem of assembling volumes from 2D B-scans has previously been studied
in the context of manually-scanned 2D ultrasound probes, and algorithms based
on speckle correlation have been suggested [22]. Conversely, most reports on
the assembly of OCT images with some elements of manual scanning focus on
building B-scans from A-scans. This is in contrast to the situation where we
have one axis of mechanical scanning, producing high-quality B-scans, and we
wish to assemble volumes and en-face images via manual scanning in the second,
slow-scan, direction. Nevertheless, there are some clear similarities between
the two tasks.

For assembly of B-scans from A-scans, in 2009 Ahmad et al. [23] demonstrated a
simple algorithm in which the Pearson cross-correlation coefficient is
calculated between a reference A-scan and each subsequent A-scan. Once the
correlation drops below an experimentally determined threshold, the current
A-scan is added to the B-scan and the process is repeated (with the current
A-scan becoming the new reference). To reduce the impact of structural
features, the A-scan had a moving average filter of several resolution
elements in size applied, and this filtered A-scan was subtracted from the
unfiltered A-scan. Liu et al. extended this work in 2012 [24] by incorporating
a theoretical model of speckle to determine the probe displacement
corresponding to a specific correlation value without experimental
calibration. In 2015, Wang et al. [25] showed that improved performance could
be achieved by taking multiple correlations between pairs of A-scans with
different time separations.

Other methods of tracking freehand-scanned probes have been suggested. For
example, Ren et al. [26] demonstrated 3D probe localisation to better than 20
µm using a tracking camera. It is also possible to combine image-based
tracking with other methods of probe localization. For example, for an OCT
needle probe, Iftimia et al. [27] combined an optical encoder for positioning
with a simple algorithm to reject correlated A-scans (i.e. A-scans covering
the same area of tissue) based on comparison of total intensity along the
A-scan. In 2012, Yeo et al. [28] proposed a combination of a magnetic tracking
system with a correlation-based algorithm, achieving a relative positioning
accuracy of 18 µm. However, methods requiring additional equipment or high-
quality camera images of the probe during scanning are generally undesirable
if the aim is to enable simple integration with existing clinical workflows.

While these A-scan assembly techniques were developed mainly for manually-
scanned OCT probes, similar techniques have been applied to proximally-driven
side-viewing endoscopes in order to correct non-uniform rotational distortions
(NURDs). Of particular relevance to this work, Uribe-Patarroyo et al. [29]
noted difficulties with patient motion, occasional under-sampling and areas of
tissue with low signal which inevitably mean that image-based tracking cannot
provide a perfect correction. Aboeui et al. [30] adopted a more complex
algorithm involving dynamic time warping. Other approaches to correct NURDs in
side-viewing endoscopes include tracking reflections from the endoscopic
sheath [31] or fiducial markers [32]; these methods are not applicable to a
forward-viewing probe.

The task of assembling volumes from B-scans, where the slow-scan direction is
manually scanned, is also related to the task of assembling volumes in side-
viewing endoscopic OCT, where a motorized pull-back of the probe acts as the
second axis of scanning. Variations in the speed of the pullback cause
distortions in the resulting volume, although we expect distortions from a
manually-scanned endoscope to be much larger. Lee et al. [33] proposed an
approach in which two B-scans are acquired at two axial positions of the
probe. As the probe moves axially, at some point the rear B-scan images the
same point on the tissue as the front B-scan, and the time difference between
these two events allows for the axial speed of the probe to be determined. A
similar method was applied to a forward looking galvanometer-based [34] bench-
top system but would be difficult to implement in a compact forward-viewing
probe. More recently, Nguyen et al. [35] proposed a method to correct both for
NURDs and longitudinal speed variations in side-viewing OCT. The longitudinal
correction involved analyzing the statistical variation of intensity in the
en-face image within a sliding window to estimate the pullback speed.

As discussed below, the method we adopted is closely related to the cross-
correlation techniques previously used to assemble B-scans from A-scans with
manual scanning, but with the difference that mechanically-scanned B-scans are
assembled into volumes (and fluorescence T-scans are assembled into
fluorescence images). The lower frame rate of B-scans compared with the line
rate of A-scans necessitates some modifications to the approach to allow for
difficulty in ensuring over-sampling during manual scanning on the slow axis.
As demonstrated below, this allows volumes to be successfully built when the
probe is manually scanned using an endoscope at speeds of up to 4 mm/s.

##  2 Materials and Methods

Figure 1: (a) Schematic diagram of the endoscopic SS-OCT/fluorescence system
used in this study. DC1-2: fused fiber directional coupler; CIRC: optical
fiber circulator; WDM: wavelength division multiplexer/combiner; DCFC: double-
clad fiber directional coupler; LF: emission filter; FL: achromatic lens; APD:
avalanche photo-detector; ODL: optical delay line; BPD: balanced photo-
detector; ENF: Olympus ENF endoscope proximal controls. (b) Photograph of the
Olympus ENF-P4 endoscope distal end (i), showing the OCT/fluorescence probe
(ii) mounted to it using a 3-D printed bracket (iii). (c) Detail from (a),
showing a close-up of the combined probe end with the Olympus endoscope.

A schematic diagram of the OCT/fluorescence endoscopic system used in this
study is presented in Fig. 1. The system, loosely based on a configuration
reported by Scolaro et al [36], combines a swept-source OCT sub-system
operating at a central wavelength $\lambda_{0}=$1310\text{\,}\mathrm{nm}$$
with a fluorescence imaging sub-system, with a
$\lambda_{e}=$488\text{\,}\mathrm{nm}$$ solid-state laser providing the
excitation. The two sub-systems share the same endoscopic probe (detailed in
the next section), combined by means of a double-clad fiber coupler (DCFC,
Thorlabs, model DC1300LEFA).

Briefly, in the OCT sub-system, the output from a MEMS swept-source (Axsun
Technologies, central wavelength $\lambda_{0}=$1310\text{\,}\mathrm{nm}$$,
tuning range $\Delta\lambda=$100\text{\,}\mathrm{nm}$$, sweep rate
$100\text{\,}\mathrm{kHz}$) is sent to a fused fiber coupler DC1, with a
splitting ratio of 90/10. 10% of the optical power is routed to a custom-built
optical delay line (ODL) forming the reference arm of the interferometer, and
is afterwards reunited with the power returning from the object arm at DC2,
which has a 50/50 split ratio to ensure balanced detection at the photo-
detector BPD (Thorlabs, model PDB480C-AC).

In the object arm of the OCT interferometer, the optical power is routed to a
fiber-based wavelength division multiplexer (WDM, Font Canada, custom
$488/$1310\text{\,}\mathrm{nm}$$ fiber optic combiner) via a circulator, CIRC
(AFW Technologies, model CIR-3-13-B-1-2-VR01), where it is combined with the
output power from the $488\text{\,}\mathrm{nm}$ solid-state laser providing
the fluorescence excitation (JDSU, FCD488FC-020, $488\text{\,}\mathrm{nm}$
wavelength, $\approx$20\text{\,}\mathrm{mW}$$ CW output power). Both are
directed to the double-clad fiber coupler, DCFC (Thorlabs, model DC1300LEFA),
where the combined OCT/fluorescence excitation power is routed to the double-
clad fiber output, connected to the OCT/fluorescence probe.

The first cladding mode of the double-clad fiber collects any fluorescence
signal from the sample, whereas the back-scattered OCT signal is routed
through the core (fundamental) mode. In this way, the DCFC is capable of
separating the returned OCT signal from the fluorescence signal. The latter is
sent to an avalanche photo-diode amplifier module (APD, Hamamatsu model
C5460-01, frequency response from DC to $100\text{\,}\mathrm{kHz}$) via an
emission filter LF (Thorlabs, model FBH520-40) and an achromatic
$f=$200\text{\,}\mathrm{mm}$$ lens FL (to ensure an optimum spot size on the
active area of the APD), whereas the former is sent to the OCT interferometer
via the WDM and CIRC.

###  2.1 OCT/Fluorescence Probe

The endoscopic probe is a forward-viewing, 1-D scanning probe, with an outer
diameter of approximately $3\text{\,}\mathrm{mm}$ and a working distance of
approximately $1\text{\,}\mathrm{mm}$. Its working principle has been
described elsewhere [37, 38, 16]; briefly, the probe operates on the voice
coil principle, employing a cantilevered optical fiber which oscillates upon
application of an alternating electric current. The fiber tip is imaged onto
the sample by means of a gradient-index (GRIN) lens (Edmund Optics, #64-545,
$0.46\text{\,}\mathrm{mm}$ working distance, not shown), creating a linear
scan of up to $2\text{\,}\mathrm{mm}$ in length, depending on the amplitude of
the drive signal. Whereas the similar probe employed in our previous work [16]
used a single-mode, SMF-28e fiber, in this case, due to the need to detect the
fluorescence signal, we instead used a 1250-1600 nm double-clad fiber (DCF13,
Thorlabs, Newton, NJ, USA, core diameter $9\text{\,}\mathrm{\SIUnitSymbolMicro
m}$, first cladding diameter $105\text{\,}\mathrm{\SIUnitSymbolMicro m}$,
second cladding diameter $125\text{\,}\mathrm{\SIUnitSymbolMicro m}$).

###  2.2 OCT Image Reconstruction

In order to reconstruct OCT B-scans, the raw channeled spectra are processed
using the Complex Master-Slave (CMS) method [39]. CMS replaces the Fourier
transform usually employed in conventional OCT signal processing, presenting
advantages such as tolerance to dispersion in the interferometer [40], long
depth imaging due to the requirement for a swept-source k-clock being dropped
[41], and improved reconstruction speed when only a small number of depth
points are needed [42]. This last point is of potential relevance to this
work, as the image reconstruction could be made to operate over a subset of
the axial range, allowing en-face images to be assembled in real-time even for
very high B-scan rates. However, this selective depth reconstruction was not
necessary for the frame rates reported here. Further processing was also
required to correct the distortion in the lateral direction due to the
sinusoidal probe scanning drive signal. This was corrected by linear
interpolation, assuming a sinusoidal scan, and was performed in real-time.

###  2.3 Endoscope Integration

While the OCT probe is small enough to fit through a standard endoscope
working channel, a suitable endoscope was not available when conducting this
study. Instead, the OCT probe was fixed externally to an Olympus MODEL ENF-P4
endoscope, using a 3-D printed bracket as shown in Fig. 1(b)(iii). The Olympus
endoscope has an outer diameter of $3.6\text{\,}\mathrm{mm}$ and a bending
range of up to 130 degrees, which allows the OCT/fluorescence probe to be
scanned in the out-of-plane direction with respect to the B-scans. A similar
approach of external fixing was previously employed for combined endoscopic
and OCT imaging of the larynx [38]. In practice the OCT/fluorescence probe
would normally be inserted through the working channel of a compatible
endoscope, but otherwise the procedure would be similar.

When fixing the probe to the endoscope it was necessary to ensure that the
direction of endoscope bending was approximately perpendicular to the OCT
probe scanning direction. This was achieved by viewing the laser scanning line
on an infra-red viewing card, rotating the OCT probe until the line was
correctly oriented, and then fixing the probe in place.

###  2.4 Assembly of volumes and en-face images - offline algorithms

To be able to assemble volumes or en-face images from the raw B-scans while
the probe is being manually scanned, it is necessary to estimate the
instantaneous 3D velocity vector of the probe. Given that the probe is to be
operated endoscopically, the exact path cannot be controlled or predicted
(although the general direction of its movement can). Motion within the plane
of the B-scans (axial or lateral) is straightforward to detect via image
registration, while out of plane-motion is identified by looking for
decorrelations in image speckle which cannot be explained by in-plane motion.
Note that is out-of-plane motion which is required by the very process of
scanning - and would be deliberately introduced by the operator - in order to
acquire data to assemble volumes and en-face images. Both lateral and axial
in-plane motion is undesirable but still likely to occur in practice.

A prototype of the algorithm for initial offline use was implemented in MATLAB
(Mathworks). All code and a selection of example data is available on Figshare
[43]. A summary of the image processing algorithm is given below:

  1. 1.

Beginning with the first B-scan ($i=1$), the $i$th B-scan is selected as a
reference image.

  2. 2.

The average tissue surface height for the reference B-scan is identified.
Several rapid methods for achieving this were investigated and are compared
with manual segmentation in Supplementary Fig. S1. The method employed was
binarization by thresholding of the image, followed by morphological opening
and closing operations with a disk-shaped structuring element. The top surface
position for each A-scan is then taken to be the location of the first non-
zero pixel along the A-scan. The median of these positions across each A-scan
is taken to be the average surface height for this B-scan.

  3. 3.

For the $(i+1)$th (current) B-scan image, a region of interest (ROI) of width
$\Delta x_{ROI}$ and height $\Delta y_{ROI}$ starting from a distance $d$
above the median surface height is extracted. The 2D normalized cross-
correlation (NCC) is computed between this ROI and the reference image.

  4. 4.

The location of the NCC peak is identified, and this is taken as the lateral
and axial shift between the two images. The current image is translated to
correct for this shift.

  5. 5.

To determine the out-of-plane motion, ROIs from the reference image and the
current image are high-pass filtered by subtraction of the same image with a
2D mean filter applied, similar to the method proposed by Ahmad et al.[23].
This removes most structural content in the image, leaving behind the speckle
pattern. A Gaussian filter is then applied to reduce noise. The ROI from the
reference image is then thresholded to identify specularities and a binary
mask created by applying a morphological open operation with a disc-shaped
structuring element. A correlation is then performed between the pixels of the
filtered ROI from the current and reference images which are not masked. This
gives the degree of speckle decorrelation due to out-of-plane motion.

  6. 6.

If the correlation is not below a predefined threshold, $c_{t}$, indicating
sufficient decorrelation for this to be identified as an independent B-scan,
Steps 2-5 are repeated for subsequent B-scan images, (i \+ 1), (i \+ 2) …,
using the same ith reference image until the decorrelation value drops below
the threshold.

  7. 7.

If the cross-correlation peak value is below the threshold, this image is
selected as the next B-scan to be used in the volume/en-face assembly. The
image is laterally and axially shifted to correct for the shifts detected in
Step 4 prior to insertion into the volume.

For the results presented below, the threshold for surface finding was set at
twice the mean value for the B-scan, and the disc structuring element had a
diameter of 30 pixels ($285\times 300$ µm). The high-pass mean filter had a
kernel size of $5\times 5$ pixels ($47.5\times 50$ µm), the Gaussian filter
had a sigma of $1.5\times 1.5$ pixels ($14.25\times 15$ µm), the mask
threshold was 5 times the mean pixel value in the ROI, and the size of the
mask structuring element was 5 x 5 pixels ($47.5\times 50$ µm). The ROI was
$180\times 100$ pixels ($1710\times 1000$ µm), laterally centered and
beginning immediately below the median detected surface height. Examples of
intermediate processing steps for the surface finding are shown in
Supplementary Fig. S2, and for the image processing prior to the correlation
calculation in Supplementary Fig. S3. The effect of the location and size of
the ROI is explored in Supplementary Fig. S4.

###  2.5 Handling under-sampling in the slow-scan direction by interpolation

The above method is expected to work when the manual scanning speed is
relatively slow, such that there is over-sampling in the slow-scan direction.
(This is similar to the situation that occurs when manually assembling A-scans
in most previous reports[23]). However, when the probe moves more quickly, and
the degree of over-sampling is reduced, the shift will be under-estimated.
This is because, when a B-scan below the correlation threshold is obtained, no
account is taken of how far below the threshold the correlation has fallen.
Higher speeds are therefore under-estimated and the parts of the volume/en-
face image acquired when the probe was moving faster will appear compressed in
the out-of-plane direction. A modification to the algorithm was therefore
developed to calculate the location of each B-scan relative to the previously
inserted B-scan based on the actual value of the correlation of the first
frame to drop below the threshold. Assuming an approximately linearly fall-off
in cross-correlation peak with out-of-plane distance (see Section 3.2 for
analysis of this approximation), the estimated distance moved, $\Delta z$, is
given by

$\Delta z=z_{dec}\bigg{(}1+\frac{c_{t}-c_{m}}{1-c_{t}}\bigg{)}$ (1)

where $z_{dec}$ is the experimentally determined out-of-plane movement
distance required for the B-scan correlation to drop below the decorrelation
threshold, $c_{t}$, and $c_{m}$ is the measured correlation peak for this
B-scan. Rather than simply inserting the B-scan as the next frame of the
volume, the volume is then assembled by linear interpolation between the
B-scans onto an evenly spaced grid in the out-of-plane direction. The
interpolation points can be chosen to be equal to the lateral pixel size,
resulting in images with square pixels, and hence the correct aspect ratio,
without need for further scaling. The benefits of this approach are
illustrated in Section 3.2.

###  2.6 Surface flattening

One of the aims of this study was to demonstrate that en-face images can be
generated in real-time by manual scanning along the slow axis. En-face images
are generally more useful if they follow the shape of the tissue surface
rather than being an oblique slice through the tissue. As described above,
axial shifts between B-scans are already corrected by the algorithm. While
this was designed primarily to correct for probe axial shifts during
acquisition, it will also tend to smooth out undulations in the tissue surface
in the slow-scan direction, since these two effects are not easily separated.
In addition to this, we also explored the possibility of correcting for a
constant probe tilt along the fast scan axis, since again, if this is not
corrected, this tilt would result in the en-face image being an oblique slice
through the tissue. This is done by correcting for the average tilt of the
tissue surface across all the B-scans of the volume. A volume was first
assembled without lateral shift corrections. The volume was then re-sliced to
obtain a stack of B-scans oriented along the slow scan direction. The median
surface heights along each of these re-sliced B-scans (i.e. for each position
in the fast scan) are then determined using the same method as described
above. A linear fit to these surface heights gives an estimate of the probe
tilt in the fast-scan direction. This measured tilt is then used to provide a
simple correction for probe tilt (by axially shifting each A-scan of each
B-scan) as part of a full reconstruction which includes both axial and lateral
shifts.

###  2.7 Assembly of volumes and en-face images - online algorithms

To demonstrate the feasibility of assembling en-face images in real-time
during endoscopic investigations, a simplified form of the algorithm was
implemented in LabVIEW (National Instruments). While the majority of the
algorithm was implemented using standard LabVIEW functions (including the
Vision Development Module), cross-correlation and image filtering prior to
speckle correlation measurement was performed using the OpenCV4 library,
accessed from LabVIEW via DLLs compiled from C++. The implementation
functioned similarly to the method described above, including tissue
flattening in the slow-scan direction and interpolation. Probe tilt correction
(i.e. flattening in the fast-scanning lateral direction) was not performed, as
this requires information from the entire volume scan and hence cannot be
implemented in a real-time en-face preview. OCT B-scans can be streamed
directly to the en-face assembly algorithm from the OCT acquisition and
processing software or from a saved datafile. Images were successfully
processed in real-time at B-scan frame rates of 250 Hz, and the en-face image
extracted at a fixed depth relative to the estimated tissue surface was
displayed with line-by-line update as the probe was scanned. A video of the
real-time display is provided in Supplementary Video 1 [44] and the LabVIEW
code is provided on Figshare [45].

##  3 Quantitative Characterization and Validation

###  3.1 Optical Coherence Tomography Probe and System

The procedure to quantify the sensitivity of the OCT channel was the same as
in a previous study from Marques et al. [16]. An A-scan was obtained from a
brushed metal target and the signal to noise ratio (SNR) of the A-scan peak
was measured with respect to the average noise floor to be
$39.8\text{\,}\mathrm{dB}$. The reference arm was then blocked and the optical
power was measured at the BPD first from the brushed metal and then from a
mirror. The ratio of these measured powers was $35.5\text{\,}\mathrm{dB}$, and
this was used to correct the measured signal-to-noise ratio of the signal from
the metal to obtain a sensitivity of $75.3\text{\,}\mathrm{dB}$. This is much
lower than $85-$92\text{\,}\mathrm{dB}$$ in our other MS systems previously
reported in the literature. This may be due to lower efficiency in re-
injecting light back into the DCF inside the custom-made probe. The width of
the A-scan peak was measured to be $28\text{\,}\mathrm{\SIUnitSymbolMicro m}$,
which effectively dictates the axial resolution of the system.

To measure the signal level of the fluorescence channel as function of
distance from the probe, the probe was mechanically translated at constant
speed away from a piece of paper stained with a 0.05g/100ml solution of
acriflavine hydrochloride. The average signal across the fluorescence T-scan
is plotted as a function of distance from the probe tip (determined from the
position of the OCT-scan surface peak) in Fig. 2(a). Peak signal was obtained
at 1.5 $\pm$ 0.1 mm from the probe, and there is no significant selection in
depth for the fluorescence channel.

Figure 2: (a) Fluorescence signal as a function of distance from probe tip. A
moving average filter of 5 points was applied to smooth the fluorescence
sensitivity profile. (b) En-face OCT image of a positive USAF target. (c)
Fluorescence image of positive USAF target placed over fluorescently-stained
paper.

To characterize the lateral resolution of the probe, a positive United States
Air Force (USAF) resolution target was placed on top of the stained paper and
the probe was mechanically translated across the sample. An en-face slice from
the OCT channel and a fluorescence image are shown in Fig. 2(b) and (c),
respectively. In the OCT channel, element 4 of group 4 can be resolved, giving
a lateral resolution of 22.1 µm. In the fluorescence channel, element 1 of
group 5 can be resolved, giving a lateral resolution of 15.63 µm.

###  3.2 Characterization of Volume/en-face Assembly

To determine the relationship between speckle correlation and out-of-plane
motion, the probe was translated over a scattering phantom (paper resolution
target) and chicken breast tissue at 0.5 mm/s using a motorized translation
stage, perpendicular to the fast-scan direction (i.e in a direction that is
out-of-plane of the OCT B-scan). The normalized cross correlation between the
first image and a region of interest of $180\times 100$ pixels ($1710\times
1000$ µm) extracted from each subsequent image was then calculated to
determine in-plane shifts. The shift-corrected images were then processed as
described above in Section 2.4, and the correlation as a function of out-of-
plane motion distance was then calculated. This was performed 50 times, each
with a different starting point within the scan. The correlation for zero
shift was calculated by averaging the correlation between 20 pairs of images
when the probe was not in motion to account for the effect of random noise.

The mean correlation is shown as a function of out-of-plane distance for the
chicken breast tissue in Fig. 3(a). The correlation drops to 0.5 at a distance
of 10.4 µm. The curve is approximately linear between correlations of 0.7 and
0.3, which supports the use of the linear interpolation method described in
Section 2.5. For the paper target, the correlation drops to 0.5 at a distance
of 10.8 µm, suggesting that this calibration is not highly dependent on the
sample properties, and so this data is not shown.

A Gaussian fit to the data of the form

$f(x)=(f_{max}-f_{min})\exp(-x/2\sigma^{2})+f_{min}$ (2)

is also shown. $\sigma$, $f_{max}$ and $f_{min}$ are free parameters, found to
be 12.0 µm, 0.952 and 0.052 for paper and 12.5 µm, 0.891 and 0.114 for the
tissue, respectively. $f_{max}$ is the correlation between images when there
is no out-of-plane motion, $f_{min}$ is the residual correlation at large
distances, and $\sigma$ is an estimate of the speckle grain size and hence the
lateral resolution. Using the conventional definition of resolution as the
full-width half-maximum (FWHM) of the point-spread function, this corresponds
to an expected lateral resolution of approximately 21 µm in the out-of-plane
direction, compared to the experimentally determined value of 22 µm in the in-
plane direction.

Figure 3: Validation of speed estimation in the out-of-plane direction via
speckle decorrelation, using mechanical translation stage to move the OCT
probe over chicken breast tissue. (a) Correlation as a function of out-of-
plane movement distance, average of 50 starting points. Error bars are
standard deviation across 50 runs. A least-squares Gaussian fit is shown. (b)
Measured speed using simple and interpolation based methods for six different
velocities. (c) Speed measured as a function of time using interpolation
method for four different translation stage speeds. (d) Speed measured as a
function of time using interpolation method for probe motion with varying
speed. Drops to zero speed are a mechanical feature of the way in which the
stage was programmed, and are not artifacts of the method.

This curve can be used to determine an appropriate value for the correlation
threshold, $c_{t}$. Clearly the value must lie in the range between $f_{min}$
and $1$ or it will never be reached. As the threshold approaches $f_{min}$ the
separation between B-scans will become reasonably large compared to the
lateral resolution, while a threshold close to 1 will be more subject to
random noise or other variations unrelated to out-of-plane motion. To follow
the convention of sampling approximately twice per resolution element, a
threshold of $c_{t}=0.5$ was adopted for the following work. The out-of-plane
movement required to reach this decorrelation threshold is then $z_{dec}=10.4$
µm.

The speed of the probe in the out-of-plane direction can be estimated by
determining how many B-scan frames are required for the correlation to drop
below the threshold ($n$). The measured average speed, $\bar{v}$, is then
given by

$\bar{v}=\frac{Rz_{dec}}{n}$ (3)

where $R$ is the B-scan frame rate. The number of frames, $n$, can be
calculated using either the simple method, taking the first frame where the
correlation falls below the threshold, or the interpolation method, in which a
non-integer number of frames is calculated using the method described in
Section 2.5. For both methods, Fig. 3(b) shows the average estimated speed
when the stage was set to move at several constant speeds. The simple method
under-estimates larger speeds quite significantly as expected. The maximum
possible speed which can be predicted by the simple method is that
corresponding to when a single frame ($n=1$) is sufficient to drop below the
threshold, which is approximately 2.6 mm/s with the parameters used here. In
comparison, the interpolation method continues to accurately estimate speeds
as high as 4 mm/s, even though the probe is under-sampling by a factor of 1.5
at this speed.

The instantaneous speed estimated as a function of time is shown in Fig. 3(c)
for four velocities. The standard deviation is approximately 10% of the mean
for all velocities in the range 0.25 to 4 mm/s, but there are an increased
number of spurious high and low speed points for the larger speeds. Figure
3(d) shows the results of setting the motorized stage to change speed during
the acquisition. Due to the way the stage was programmed, the speed briefly
dropped to zero between each segment, as can be seen in the estimated speed.
The processing step of masking bright pixels prior to measuring the
correlation between images was found to be critical when specular reflections
occurred from the tissue surface, otherwise the probe speed would be
significantly under-estimated at these points, as shown in Supplementary Fig.
S5.

The OCT probe was then moved freehand over a printed grid phantom with
fluorescent highlighter applied. An example of the raw en-face OCT and
fluorescence images are shown in Fig. 4(a,b). Severe distortion is present due
to lateral motion and inconsistent out-of-plane motion, as expected. Figure
4(a) also shows the effect of axial motion of the probe; this is less apparent
in Fig. 4(b) due to the lack of optical sectioning in the fluorescence channel
(see Section 3.1). Figure 4(c) shows the reconstructed en-face image
correcting only for out-of-plane and lateral motion, while Fig. 4(d) shows the
result if surface-flattening is also employed. Finally, Fig. 4(e) shows the
reconstructed fluorescence image. While some artifacts can be observed in the
reconstructed en-face views, the algorithm successfully recovers the broad
morphology of the sample.

Figure 4: Reconstruction of OCT and fluorescence images following freehand
probe scan over fluorescently-stained printed grid phantom. (a) En-face slice
extracted from raw volume. (b) Raw fluorescence image. (c) En-face slice from
motion-corrected volume without surface correction. (d) En-face slice from
motion-corrected volume with surface correction. (e) Motion-corrected
fluorescence image. (a)-(b) have the same horizontal scale as (c)-(e) but have
no vertical scale since this depends on the instantaneous probe speed.

##  4 Imaging Results Under Simulated Conditions

An example of an OCT volume and fluorescence images of porcine lung tissue,
acquired using the endoscope for the slow-scan (as described in Section 2.3),
is shown in Figure 5. To provide fluorescence signal, the tissue was stained
for approximately 2 minutes with acriflavine hydrochloride before rinsing with
water. Figure 5(a-d) show raw acquisitions, while Fig. 5(e-g) show
reconstructed images after processing. Figure 5(c,g) show B-scans along the
fast-scan direction, while Fig. 5(a,e) show B-scans extracted from the volume
along the slow-scan direction. Figure 5(b,f) show en-face images extracted
from the volume. There was considerable in-plane lateral motion during this
scan, and so the en-face and fluorescence images have been rotated by 37
degrees for convenient display. The effect of the algorithm in removing the
motion artifacts near the top of the images can clearly be seen. The surface
flattening effect can also be clearly observed by comparing the slow-axis
B-scans and by observing the more constant intensity along the en-face image
in Fig. 5(f).

Figure 5: Example reconstruction using endoscope for slow-axis scanning over
porcine lung tissue. The raw volume is represented in (a), (b) and (c), which
shows a B-scan along the slow-scan direction (a), an en-face slice (b) and a
B-scan along the fast-scan direction (i.e. a raw B-scan) (c). The raw
fluorescence data is also shown in (d). The reconstructed OCT volume is shown
in (e), (f) and (g), which again shows a slow-axis B-scan (e), an en-face view
(f) and a fast-axis B-scan (g). The reconstructed fluorescence is shown in
(h). The en-face and fluorescence images have been rotated by
$37^{\textrm{o}}$ for display purposes. The in and out-of-plane lateral and
axial shifts detected by the algorithm are shown in (i), and the measured out-
of-plane speed is shown in (j). (a) and (b) are at the same horizontal scale
as the other images, but have no vertical scale since the vertical position
corresponds to the time of acquisition of each B-scan.

Figure 5(i) shows the relative probe position as a function of time calculated
by the algorithm, while Fig. 5(j) shows the calculated speed in the slow-scan
(out-of-plane) direction. For most of the scan the measured probe speed in the
out-of-plane direction was approximately 1 mm/s, although there are a large
number of spikes up to around 2 mm/s which could be a result of jerky motion
leading to sudden decorrelation.

Further example of images acquired using the endoscope are shown in Fig. 6.
En-face and fluorescence images are shown from porcine trachea in Fig. 6(a),
lung in Fig. 6(b) and esophagus in Fig. 6(c). The tissue was prepared and
stained as before. All three sets of images are shown at the same scale,
except for the zoom insets which show an area of 1 mm x 1 mm.

Figure 6: Representative pairs of fluorescence (left) and OCT images (right)
from porcine ex vivo tissue generated when using an endoscope for the slow-
axis scanning. (a) Lung, (b) esophagus, and (c) trachea. The samples were
stained with arcriflavine hydrochloride for 2 minutes and then rinsed prior to
imaging. En-face slices were manually selected for display from reconstructed
volumes. For display purposes, en-face OCT slices were contrast adjusted using
the ImageJ autocontrast tool. Fluorescence images were autocontrasted to show
the full dynamic range of the image. Zoomed insets show 1x1 mm regions.

##  5 Discussion and Conclusions

We have demonstrated the feasibility of using a conventional endoscope to
provide the slow-axis scan for simultaneous 3D OCT and fluorescence endoscopic
imaging. In experiments designed to simulate clinical imaging, slow-axis scans
over ranges of up to 1 cm were performed, and en-face images successfully
reconstructed using the speckle decorrelation and registration algorithms.
While some image artifacts are often present, and the method is not robust to
certain kinds of motion, the en-face images generally appear congruent and are
much larger in the slow-scan direction than could be achieved using any
miniaturized 2D endoscopic scanning mechanism.

The reconstruction performance is primarily limited by the fast-axis scanning
rate of the endoscopic OCT probe. With a B-scan frame rate of 250 Hz and a
lateral resolution of approximately 20 µm, the (manual) slow-axis scan is
limited to a speed of approximately 2.5 mm/s before under-sampling. At speeds
greater than 5 mm/s the adjacent B-scans will be almost entirely decorrelated
and it is no longer possible to estimate the speed correctly. These scanning
speeds are rather low and may be difficult to achieve in clinical practice.
However, a relatively modest increase in the speed of the B-scan frame, for
example to 1 kHz (which is technically feasible), would increase the permitted
scanning speed to between 10 mm/s and 20 mm/s. The maximum speed is therefore
not a limitation of the approach in general. Sudden or jerky motion during a
scan will also lead to missed areas of tissue, and even with very high fast-
axis scanning speeds it is unlikely that these artifacts could be avoided
entirely.

The manual slow-scan direction must be roughly perpendicular to the fast-scan
direction. While some in-plane motion parallel to the fast-scan direction can
be tolerated, and is corrected by the registration procedure, large amounts of
in-plane motion will reduce the accuracy of the speckle decorrelation
algorithm and lead to a smaller area of tissue being imaged. It would
therefore not be possible to simply insert the OCT probe into the working
channel of an endoscope; there would need to be some way to check that the
orientation aligns with one axis of the endoscope bending motion. This could
be intrinsic, through mechanical design forcing the probe to be inserted
correctly, or by visualizing the fluorescence excitation on the endoscope
camera view and manually rotating the probe. Alternatively, it may be
desirable to permanently build the OCT probe into the endoscope, it which case
the alignment would be fixed during manufacture.

The speckle decorrelation algorithm cannot detect a change in the direction of
the out-of-plane motion. While in principle the endoscope operator could
ensure that the endoscope is translated only along a single direction during
the scan, patient motion or inadvertent endoscope motion could result in the
same area of tissue being scanned over more than once. However, it may be
possible to detect and remove these occurrences through image analysis.

These limitations are partially mitigated by allowing the operator to see the
en-face image being assembled in real-time, providing visual feedback on the
scanning speed and direction and allowing any errors to be more easily
identified as they occur. We have demonstrated that the algorithm is
computationally inexpensive and can readily be applied in real-time for a
B-scan rate of 250 Hz. To allow for higher frame rates, Complex Master-Slave
OCT could be used to reconstruct only a limited axial range around the known
surface height.

We have therefore shown that manual slow-axis scanning in combination with
correlation-based probe tracking is a promising approach for endoscopic
forward-viewing OCT and fluorescence imaging. To aid further investigation, we
have made the code for reconstructing OCT volumes and fluorescence images
available with this report. The approach may also have other applications,
such as for lower-cost hand-held probes for external body imaging or
industrial inspection and non-destructive testing. Alternatively, it may be
applied to endoscopic probes designed to be operated with mechanical or
robotic scanning systems (such as robotic surgical systems). Indeed, for
robotic systems it should be possible to use the speed estimation from the OCT
speckle decorrelation for closed-loop robot control, leading to more precise
and controlled imaging.

Funding To be included from Prism.

Disclosures AP and AB are co-inventors of patents US10760893 and US9383187 in
the name of the University of Kent.

Data Availability Data and Matlab code underlying the results presented in
this paper are available on Figshare [43]. The real-time LabVIEW code is also
available on Figshare [45].

Supplemental document See Supplement 1 for supporting content.

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