Real-time, label-free, intraoperative visualization of peripheral nerves and micro-vasculatures using multimodal optical imaging techniques

Abstract

Accurate, real-time identification and display of critical anatomic structures, such as the nerve and vasculature structures, are critical for reducing complications and improving surgical outcomes. Human vision is frequently limited in clearly distinguishing and contrasting these structures. We present a novel imaging system, which enables noninvasive visualization of critical anatomic structures during surgical dissection. Peripheral nerves are visualized by a snapshot polarimetry that calculates the anisotropic optical properties. Vascular structures, both venous and arterial, are identified and monitored in real-time using a near-infrared laser-speckle-contrast imaging. We evaluate the system by performing in vivo animal studies with qualitative comparison by contrast-agent-aided fluorescence imaging.

Full text

Real-time, label-free, intraoperative
visualization of peripheral nerves and micro-
vasculatures using multimodal optical
imaging techniques
JAEPYEONG CHA,1,5,6 ALINE BROCH,1,5 SCOTT MUDGE,1 KIHOON KIM,1,2
JUNG-MAN NAMGOONG,1,3 EUGENE OH,1,4 AND PETER KIM1,7
1Sheikh Zyaed Institute for Pediatric Surgical Innovation, Children’s National Health System, 111
Michigan Avenue NW, Washington, DC 20010, USA
2Department of Surgery, Inje University Haeundae Paik Hospital, 875 Haeun-daero, Haeundae-gu,
Busan 612-896, South Korea
3Department of Surgery, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 138-736, South
Korea
4Department of Biomedical Engineering, The Johns Hopkins University, 3400 N. Charles Street,
Baltimore, MD 21218, USA
5These authors contributed equally to this work
6jcha2@Childrensnational.org
7PKim@Childrensnational.org
Abstract: Accurate, real-time identification and display of critical anatomic structures, such
as the nerve and vasculature structures, are critical for reducing complications and improving
surgical outcomes. Human vision is frequently limited in clearly distinguishing and
contrasting these structures. We present a novel imaging system, which enables noninvasive
visualization of critical anatomic structures during surgical dissection. Peripheral nerves are
visualized by a snapshot polarimetry that calculates the anisotropic optical properties.
Vascular structures, both venous and arterial, are identified and monitored in real-time using a
near-infrared laser-speckle-contrast imaging. We evaluate the system by performing in vivo
animal studies with qualitative comparison by contrast-agent-aided fluorescence imaging.
© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
OCIS codes: (170.0170) Medical optics and biotechnology; (170.3890) Medical optics instrumentation.
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1. Introduction
Accurate identification, precise dissection, and careful preservation of critical structures, such
as nerves and blood vessels, are key to successful surgical outcomes [1]. Unintended and/or
unrecognized injuries to critical structures result in debilitating short- and long- term
morbidity, avoidable mortality, and considerable socioeconomic and healthcare burdens [2–
4]. These tissue injuries occur most frequently intraoperatively, and are mainly attributable to
human factors such as technical and/or cognitive competence in executing surgical tasks in
real-time. Although surgeons can look up detailed information during surgery for complex
structures and visual aids are often available, identification of relevant and critical anatomic
structures and pathologies can be further limited by significant variations in individual
surgeon’s training, experience, and clinical volume [5–7].
Nerve injury is a significant complication associated with many surgical procedures,
including prostatectomy, thyroidectomy, hernia repair, and breast cancer surgery, and can
affect up to 16.4% of patients [1–4, 8–12]. For example, in the case of gynecologic surgery,
nerve injuries are a common complication, occurring in 1.1–1.9% of cases [13]. Although the
majority of neuropathies resolve with conservative management and physiotherapy [14],
however severe injury cases such as axonotmesis can sustain long-term morbidity requiring
prolonged rehabilitation or additional reparative surgery [15]. Other traumatic neuropathies
and surgical nerve reconstructive techniques can also be found in [16–18]. These
complications, leading to loss of function, sensation, muscular atrophy, and/or chronic
neuropathy, considerably impair patient quality of life [2, 19–22]. Intraoperative nerve
injuries are often caused by surgeon’s limited ability to identify and distinguish nerve fibers
from the surrounding tissues. Nerves often appear as a network of iridescence rather than
distinct anatomical structures, or distorted or displaced by adjacent pathology such as
malignancy. Surgeons usually recognize damage to nerves of interests post facto (unintended
injury) [12]. Intraoperative placements of neural probes are time-consuming and disrupt
workflow, and lack anatomic and physiologic accuracy [23]. Current nerve-sparing
techniques rely primarily upon anatomic landmark identification by surgeons, which is highly
dependent on each surgeon's memory based on experience and training, or intraoperative
electrical stimulation devices with unknown precision [24–27]. Thus, the development of new
imaging tools, that quickly identify and clearly display peripheral nerves in real-time during
surgery, is of paramount importance.
Recently, there have been interesting studies to identify nerves using label-free optical
imaging techniques such as polarization sensitive optical coherence tomography [28] and
collimated polarized light imaging (CPLi) [29]. Islam et al. extracts the subsurface structural
features of ‘Bands-of-Fontana’ from rat sciatic nerve using a polarization-sensitive spectral
domain optical coherence tomography. Although PS-OCT has a limited image volume and
short working distance, the authors successfully demonstrated a 3D imaging of nerve
structural deformation under stretched conditions. In contrast, Chin et al. used a collimated
polarized light to differentiate anisotropic optical properties of nerve structures and
highlighted a real time intraoperative nerve imaging capability with wide field of view and
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longer working distance, which permits surgical interventions into the deep tissue areas.
However, this work has limited to macroscopic observations of human cadaver tissue,
dropping the opportunity to recognize unique optical signatures of peripheral nerves, ‘Bands-
of-Fontana.’, that can be an indicator for the nerve injury [30].
Misidentification of blood vessels occur in all specialties, and have potential life
threatening outcomes [31]. Anatomical variations and local pathologic conditions, such as
inflammation and tumor infiltration, limit vessel-visualization and lead to injuries. Although
repairs to damaged blood vessels are performed immediately, and redundancies in blood
supply can often compensate for sequelae of acute ischemia, complications requiring
additional treatment due to hemorrhaging, necrosis, abscesses, and strictures are not always
avoidable [31–33]. Pre-operative “road-maps” using complex medical imaging devices, such
as computed tomography (CT) and magnetic resonance imaging (MRI), can act as
intraoperative guides, however, the time latency between imaging and surgery increases the
likelihood of inaccuracies [34–37]. Therefore, accurate identification and clear, precise
display of blood flow and tissue perfusion in real-time during surgical procedures would
tremendously benefit surgeons by facilitating quick decision-making, reducing operative
times, and improving surgical outcomes.
Intraoperative identification of critical structures, such as vessels, bile ducts, ureters,
nerves, and lymph nodes, has been successfully demonstrated using fluorescence image-
guided surgery, both in preclinical and clinical studies [38, 39]. Optical contrast agents are
usually coupled with optical imaging instruments to provide real-time visualization and to
further increase the contrast of otherwise indistinguishable anatomic features. However, there
are currently only a few fluorescent dyes approved for clinical use [40, 41]. The use of current
intraoperative dyes is further complicated by the need for preparation times, costs, dose
constraints, short half-cycles, allergic reactions, injection timings, and the inability to conduct
multiple-use cases [42–45]. Dye-free, intraoperative visualization of critical hidden, sub-
surface structures in real-time obviate the above disadvantages of dye based visualization and
would be ideal most surgical procedures [46–49].
In this study, we introduce a new multimodal optical imaging device capable of
highlighting peripheral nerves and micro-vessels in real-time on the screen of surgical fields-
of-view, without any labeling dye but can also extend the capability of fluorescence imaging.
We integrate the anisotropic optical properties of peripheral nerves, captured and processed
by a snapshot polarization imager, and real-time processing and visualization of
microvascular morphology and tissue perfusion in vivo by a graphics processing unit (GPU)-
accelerated invisible laser-speckle-contrast imaging (LSCI). We present the optical layouts,
preclinical validation studies, and performance evaluation (using rodent and swine models) of
the imaging system. Finally, we discuss the benefits, limitations, and potential applications of
the current design and technology in various surgeries.
2. Materials and methods
2.1. System design
Surgical microscopes utilizing LSCI and fluorescence have been previously reported [50–52].
To further enable multimodal imaging, and its intraoperative use, we used an existing surgical
OPMI S5 microscope (Carl Zeiss, Germany) with additional camera ports (as shown in Fig.
1). The microscope includes a 250-mm focal length main objective (obj.) lens and 2
additional camera ports, located on the left and right arms, split by a virtual beam-splitter
(BS1) connected to the main body of the microscope. The optical path of the left arm was
divided into two, for CAM1 (NIR LSCI, GS3-U3-41C6NIR-C, FLIR, U.S.A.) and CAM2
(HD Color Vision, GS3-U3-41C6C-C, FLIR, U.S.A.), by a 50:50 beam-splitter coupler (BS2;
Carl Zeiss, Germany); CAM1 operates at 90 frames per second with a 2048 x 2048 pixel-
image-size and CAM2 acquires images at 30 frames per second, with a 2048 x 2048 pixel-
image-size. CAM1 also contains a laser-line-filter (830 nm cut) to pass the laser light
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wavelength only, and a near-infrared linear-polarization-filter (LP2, Thorlabs, New Jersey,
U.S.A.) to handle and minimize Fresnel surface reflections from wet tissues. The right arm
included an additional observance arm to split the beam path into two, for CAM3 (NIR
Fluorescence, GS3-U3-41C6NIR-C, FLIR, U.S.A.) and CAM4 (PolarCAM M, 4D
technologies, U.S.A.), by video couplers (Quintus Zeiss adapter, Oprtornics, U.S.A.). CAM3
operated at 30 frames per second with a 2048 x 2048 pixel-image-size, including both long-
pass (cut-off wavelength: 800 nm) and band-pass (790/30 nm) filters to pass only emission-
fluorescence signals. CAM4 was a snapshot micropolarizer camera operating at 35 frames per
second with a 1500 x 1200 pixel-image-size, with 4 micro-patterned linear-polarization-filters
on the charge coupled device (CCD) camera sensor, enabling real-time polarization
calculations and imaging of birefringence [53].
Fig. 1. Optical layout and implementation of the proposed system using a surgical microscope.
a. Photo of the system implementation. b. Optical design. BS1, BS2, BS3: virtual beam-
splitters; E1&2: eye pieces; L1: plano-concave lens; LF: laser line filter; LP&BP: long-pass
filter and band-pass filter; LP1&2: linear polarizers; OA: observation arm; Obj.: main
objective lens.
For illumination, we used a built-in microscope illuminator (Halogen bulb) with a short-
pass filter (cut-off wavelength: 800 nm) to avoid cross-talk issues, and a near-infrared laser
diode (P = 5 mW, 830 nm; Wuhan, China) equipped with a plano-concave lens (LC1054-B,
Thorlabs, New Jersey, U.S.A.) and linear polarizer (LP1, Thorlabs, New Jersey, U.S.A.) to
illuminate the surgical area. In addition, UV/NIR LED arrays (Center wavelengths: 380 nm
and 750 nm; Larson Electronics, Texas, U.S.A.) were selectively used for generating
fluorescence-image-control-data.
2.2. Image processing for real-time nerve and vasculature imaging
Polarization light can reveal diagnostic information on tissue morphology by enabling
subsurface imaging [49]. For nerve identification, we used a camera employing a
micropolarizer array, with linear polarizers oriented at 0, 45, 90, and 135 degrees [53]. The
pixelated polarization camera acquires four polarization orientations in a single video frame,
which enables instantaneous measurements of linear-Stokes-parameters [54]. The Stokes
vector is a convenient method for describing the polarization state of a light beam, as each
element can be conveniently measured. Combined with the Muller matrix, which describes
the transfer function of an optical device, one can easily compute the complete polarization
characterization of a medium; which can be further decomposed to enable interpretation of
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depolarization, de-attenuation, and retardance, including birefringence and optical rotation
[55]. In this study, we used phase retardance (ߜ) to calculate birefringence imaging and
differentiate the peripheral nerves from surrounding tissues (Eq. (1)), where the
R
M indicates
retardance matrices (full analysis is available in [48, 56]). Peripheral nervous system (PNS)
contain anisotropic properties of myelin sheaths and internal Bands-of-Fontana structures
which can be detected by depth-selective polarization imaging [30, 56].
()()
22
1
COS ( 22 33 23 32 1)
δ
−
=++−−
RR RR
MM MM (1)
where MRij indicates the element of Muller matrix, i and j are each column and row of the
matrix. For real-time vasculature imaging, a general-purpose graphics processing unit
(GPGPU –Titan X, provided by NVIDIA) was used to accelerate the processing speed
through parallelization. In most previous research works, LSCI technique has been performed
off-line due to the heavy computation required for high-resolution image processing and the
serial processing of central processing unit (CPU) has insufficient power to accomplish this in
real-time. However, for clinical applications, real-time processing and visualization are
critical. Thanks to advancement of new technology, GPU now allows a parallel processing
and memory latency can be mitigated by employing multiple processing core units. In our
implementation, the raw laser-speckle data acquired from CAM1 was transferred, as a
normalized array-bound texture with 32-bit floating point values, into the shared GPGPU
memory. A spatial contrast value was then computed for each pixel of the array, using a
sliding window of variable size (either 5 x 5 or 7 x 7 pixels). The following results were
performed on our PC equipped with Intel® Core i7-8700K Processor, 16G main memory and
one GeForce Titan X graphics card. In 2048 x 2048 pixels size, 16 bit-depths image
processing, with the spatial window size of 5 x 5, the GPU processing speed (11.13-ms per
image) was approximately 67.2 times faster than CPU (748-ms per image). To better
visualize the newly computed array of spatial contrast values, heat-mapping was applied
based on the normalized spatial contrast value. This heat-map was stored as a 32-bit packed
integer, representing red, green, blue, and alpha channels (RGBA). Following
synchronization of all parallel processing cores, the final RGBA heat-map was copied back to
the host machine, and stored as memory in a dynamic buffer (Fig. 2). This dynamic buffer
provides sufficient capacity to store up to the 30 most-recently computed heat-maps. To
mitigate noise and extract greater signal fidelity in the time domain, temporal blending of
these buffered frames was applied, using a fixed and equal weight, to the alpha channel of
each stored frame. The final blended image was composited and displayed to the user via an
OpenGL-based frontend user interface.
Fig. 2. The overall processing framework of image analysis based on a graphics processing
unit. CPU: central Processing Unit; CUDA: parallel computing platform; GPU: graphics
processing unit; NIR: near-infrared.
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2.3. Software development for graphical user-interface
Graphical user-interface (GUI) bindings and controls for the camera, data recording, and
processing parameters were custom developed in native POSIX C + + , using OpenGL as the
primary backend for rendering and compositing both the user interface and processed image
data. The frontend software (‘STARcontrol’) was targeted for 64-bit Linux/UNIX systems.
STARcontrol also served as a hub for inter-process communication (IPC) between the many
different software modules required for the entire processing stack to run (e.g., taking data
from the hardware layer – cameras, applying preprocessing, passing data to LSCI module,
etc.). This also provides the user with the ability to adjust any operational parameter
dynamically and easily, as all were bound to specific and easy-to-navigate elements in the
GUI (Fig. 3).
The OpenGL backend also provided the ability to easily apply morphological
transformations to the imaging data in real-time (e.g., rotation, translation, zoom, etc.),
enabling rapid alignment of different image planes, captured from different sources and
sensors. In addition to morphological changes, STARcontrol also provided the user with the
ability to change global alpha, blending attributes, and color overlays in each of the imaging
planes. These features collectively provided the user with the capacity to tailor the
visualization of the scene to their specific needs, applying different visual weights to various
imaging modes to highlight specific features (e.g., subsurface vasculature extracted via LSCI
over surface features).
Fig. 3. Graphical user interface (imaging mode options: color, LSCI, polarization, and
fluorescence; image field of view; camera selection and control; multimodal image alignment
using the openGL function). LSCI: laser-speckle-contrast-imaging
2.4. Animal procedures and fluorescence imaging
2.4.1. Nerve, vein, and artery imaging in ex vivo porcine tissue
We acquired various sized porcine nerves, tendons, veins, and arteries and dissected the tissue
region of interests using a sharp scalpel; we then conducted imaging of the regions using our
system. Fresh samples were obtained from a local abattoir and dissected into tissue segments
of interest. The sample was moistened with physiological saline and preserved at 4°C for up
to 30 h from the time of slaughter until imaging. Before imaging, different segments of the
porcine tissues were dissected using forceps and dissectors. During the measurements, the
remaining samples were preserved in saline in sealed sterile containers for hydration
maintenance, for up to 30 min.
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2.4.2. Rat anesthesia and exposure for sciatic and femoral nerves, arteries, and
veins
All procedures were performed in the animal research facility under institutional animal care
and use committee guidelines (IACUC) #30597. Male and female 250 - 350 g Sprague-
Dawley rats (n = 4) from Charles River Laboratories (Wilmington, Massachusetts, U.S.A.)
were used for this experiment. A 3-min inhalation of 4% Isoflurane was used for sedation and
restraint. Anesthesia was maintained using intramuscular injections of 2 mg/kg Xylaxine and
75 mg/kg Ketamine. After ensuring sterile conditions, femoral nerve and vessels were
exposed at their junction with the inguinal ligament, and imaged with our vision system. For
small bowel imaging, a mid-line laparotomy was performed and small part of the bowels
were exposed and imaged under our imaging system.
2.4.3. Pig anesthesia and exposure for liver hilum
Our institutional animal care and use committee approved our protocol (IACUC #30591). All
procedures were performed in the animal research facility. Female Yorkshire 10 kg pigs (n =
2) from Archer Farms (Darling, Maryland, U.S.A.) were used for this experiment. As the
peripheral nervous system in the body is similar between female and male, we used available
animals at the time of our experiments. Pigs were sedated by intramuscular injection of
xylazine-ketamine anesthetic. A 3-min inhalation of 4% Isoflurane was used and maintained
for anesthesia. After ensuring sterile conditions, midline laparotomies were performed. Part of
the liver was exposed and imaged with our imaging system.
2.4.4. Fluorescent dye injections in rat
For rats, as a control test and comparison study, a 24-G catheter was placed in the tail vein to
visualize nerves and vessels by intravenously injected fluorescent dyes. To visualize sciatic
and femoral nerves in rats (n = 2), an 8-mg/kg injection of bis-benzenamine (BMB) was
performed at the beginning of the procedure and fluorescence was recorded 2 hours later [8].
For vasculature imaging (n = 2), a 0.15-mg/kg injection of indocyanine green (ICG) (MP
Biomedical, LLC, Solon, Ohio, U.S.A.) was used for fluorescence angiography and
immediately imaged after injection for 5 minutes using an NIR-camera [52].
3. Experimental results
3.1. Ex vivo and in vivo imaging of peripheral nerves
To test and calibrate the polarimetric imaging setup, we first tested excised ex vivo porcine
nerves, tendons, and vessels. To demonstrate the advantage, we selected and dissected pelvic
nerves, veins, and arteries, and placed them together for comparison and contrast.
Polarimetric imaging enables real-time identification of the pelvic nerve, and highlights the
subsurface structures (as shown in Fig. 4). The pelvic nerve shows the intrinsic structure of
‘Bands-of-Fontana’ and we can use this optical signature of spiral patterns for feature
extraction, and to distinguish the nerves from other surrounding tissue types. As highlighted
in Fig. 4(b), individual fascicles inside the nerve tissue were identified by their ‘zig-zag’
spiral pattern, but we were unable to see the subsurface fascicles due to the limited sensitivity
of depth-imaging.
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Fig. 4. Polarimetric imaging for subsurface pattern extraction. (a) Color photo taken with the
color camera. (b) Representative Retaradance map from birefringence imaging; pelvic nerve
branches, with individual fascicles, are highlighted by the intrinsic zig-zag spiral patterns of
‘Bands-of-Fontana’. Please note that sub-surface fascicles within pelvic nerve are identified
and differentiated. White bars: 1 cm.
Sciatic nerves were visualized and distinguished using our polarization imaging tool using
live rats (n = 2), and the result was compared to fluorescence imaging using BMB dye (n = 2)
(Fig. 5(b)). BMB dye highlights not only peripheral nerve structures but also surrounding
tissues, such as fat. By contrast, Fig. 5(c) shows that label-free polarimetric imaging and
processing enables the extraction of hidden optical signatures from sciatic nerves,
distinguishing them from surrounding tissues. This was achieved by phase retardation
calculation and image processing kernel including feature extraction and image segmentation.
Fig. 5. Imaging of sciatic nerve in rats. (a) Color image of sciatic nerve in a rat. The spiral
pattern is barely seen. (b) BMB injected fluorescence image. (c) Polarimetric kernel processed
image highlighting ‘Bands-of-Fontana’. (d) Overlay image. White bars: 5 mm.
3.2. Small bowel vasculature imaging in a rat model
To demonstrate if microvasculature can be visualized in real-time, we imaged small intestine
using rat models. We first tested bowel perfusion using the developed system and compared
with dye-based fluorescence imaging. As shown in Fig. 6 [Visualization 1], the LSCI module
enables instantaneous visualization of both arteries and veins, whilst fluorescence imaging
using ICG injection only permits temporary visualization of arteries and veins (ICG half
cycle: 3–4 seconds) [39, 44]. With regards to temporal resolution, LSCI enables seamless
visualization of blood flow, both in veins and arteries, whilst fluorescence imaging is only
available for 3–5 seconds after systemic injections. LSCI permitted the visualization of fine
details, both from mesentery and small arcades, throughout the bowel tissues; however, the
contrast of fluorescence imaging was blurry due to the diffused fluorescent light emitted from
the tissue and it was difficult to identify fine vasculatures.
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Fig. 6. Real-time perfusion imaging of the small bowel in a rat. (a) A color photo taken from
our imaging system. (b) A comparative LSCI image. (c) Overlay image onto color image. (d)
Intravenous injection of ICG and fluorescence image overlay onto color image. (e) A still
image of artery visualization with fluorescence signals. (f) A still image of vein visualization.
(see Visualization 1). All white bars: 2 cm.
3.3. Hepatic artery visualization in a pig model
We further tested our imaging capability in a large animal using the same imaging system; the
target tissue was the liver hilum. As shown in Fig. 7, we monitored hidden, subsurface
hepatic arteries located in the liver hilum (1–3 mm deep from the surface peritoneum) and
their branches extended to liver lobes in real-time from a live pig during partial liver resection
surgery. In this experiment, we used a 5-mW power, 830-nm near-infrared laser light to
create a speckle pattern and GPU accelerated ‘spatial speckle contrast processing’ with a 5 x
5-pixel sliding window, enabling an instantaneous display of blood flow (exposure time: 10
ms) in hepatic arteries. By overlaying the live feed of blood flow over real color video
streams, blood vessels could be simply displayed and hidden key-branches of hepatic arteries
identified; moreover, real-time blood flows could be monitored during surgical dissection.
Fig. 7. Subsurface blood flow monitoring using an LSCI. (a) A color photo of the liver hilum
in a live pig. (b) Real-time visualization of subsurface hepatic arteries. Hidden branches are
highlighted by arrows and circles. (see Visualization 2). White bars: 3 cm.
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3.4. Simultaneous femoral nerve, and vessel imaging in a rat model
Our multimodal imaging system also proved to be useful for simultaneously identifying and
visualizing peripheral nerves and micro-vasculatures together in vivo. Figure 8 (Visualization
3) depicts a representative image of femoral nerve, artery, and vein in a live rat. The
polarimetric imaging module enables real-time identification of femoral nerve, highlighting
the structures in green, whilst laser speckle contrast imaging visualizes the blood flow in both
veins and arteries in red. Interestingly, fat tissues were also highlighted during live-imaging.
Fig. 8. Femoral nerve, vein, and artery in a rat. (a) A standard color photo of femoral area in
live rat. (b) LSCI imaging mode highlighting femoral vasculatures colored in red. Please note
that unseen vessels were also highlighted and femoral nerve is not visible (c) Polarimetric
processed image highlighting femoral nerve. (see Visualization 3 for real time multimodal
imaging). Asterisk indicates femoral nerve, yellow arrow heads indicate femoral vein and
white arrows are vasculatures. All white bars: 1 cm.
4. Discussion
4.1. Multimodality optical imaging integration into an operating microscope
We report a combination of polarimetry and LSCI imaging during surgery with live animals.
This is achieved and demonstrated by integrating polarimetric and LSCI technologies into one
system and it enabled real-time, in situ visualization of both peripheral nerve and blood flow
across the inguinal regions, with no dissection of perivascular tissues and minimal mechanical
disruption to the vessels. Incorporating multimodality imaging to one existing operating
microscope has several advantages. Each camera shares a common optical pathway; thus,
alignment of multiple sensors is relatively easy. This imaging modality can be modular;
therefore, the field-of-view, zoom, and focus can be changed simultaneously, enabling the
same surgical field-of-view magnifications and demagnifications by the user. In our setup, a
250-mm working distance is sufficient for surgeons to perform the animal procedures, in both
small and large live animals. Our snapshot polarimetry is distinctive from the previous label-
free nerve imaging techniques of PS-OCT and CPLi as we were able to differentiate
individual fascicles in real time, which can play a crucial role for nerve sparing techniques.
Both IR-based blood vessel (superficial and deep tissue) identification and nerve imaging
(superficial) using the anisotropic optical properties are two known optical techniques which
can be easily combined. However, one of the main contribution of the current study is the
proposed system can also be used for fluorescence imaging (superficial and deep-tissue). In
the real operation room, surgery requires situational awareness of surrounding tissues and
relevant anatomy in the operative field. Our multimodal optical imaging system permits a
real-time identification of critical structures without labeling but is also fully capable of
fluorescence imaging, that can be complementary. For example, surgeon can use our label
free imaging techniques to identify nerves and vessels in the beginning of procedures and
finally confirm these anatomic structures again using a fluorescence. This alternative option is
of potential benefit for surgeons, and will enable individual imaging-module-selection
depending on the surgery type and imaging mode preferences.
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4.2. Polarimetric imaging
Our system qualitatively demonstrated that peripheral nerve identification can be possible
based on polarimetry and subsurface feature extraction of unique spiral patterns ‘Bands-of-
Fontana’. Although it is clear in our first pilot demonstration using live rat model, additional
works will be needed to validate the usefulness of this technology by setting up a survey or
testing groups to let surgeons choose which one is nerve among other tissues in their color
images, fluorescence images, and our polarimetry extracted images. Ultimately, in the near
future, machine learning could be employed to computationally segment each tissue and it
may increase the sensitivity. Our study is well aligned with the recent works by Chin et al.
[29, 57], where the authors recently demonstrated a real-time nerve identification in the
human hand. Regarding the issue of fat tissue false identification in our results, it can be
caused by the polarization imaging setup. Chin et al. used a linearly polarized imaging
scheme with two perpendicular linear polarizers to reject ‘Fresnel reflection from tissue
surface’ whilst we used a circularly polarized illumination for the birefringence calculation, in
nerve identification mode, thus failing to avoid direct reflections from fat tissue at given
wavelength of 830 nm. Any birefringent part of the tissue introduces a phase delay in their
spatial electric field and it creates an ellipticity in the polarization state. The illumination
through perpendicular polarizers disappears when the polarizers are aligned with the major or
minor axis of the birefringent part of tissues such as fat, which simply measures the
birefringence and highlights nerve tissues well, as demonstrated in Chin et al. However, to
find the true direction of alignment requires a rotation through 0 to 180 degrees to solve the
ambiguity of which rotation angle corresponds to the major and the minor axes. We
alternatively used a circularly polarized light as an input to eliminate rotation of illumination
light and calculated birefringence using a snapshot polarimetry to enable real time calculation
without any rotation angle changes. The tendon and collagen tissues have been well studied
using a circularly polarization light to measure their retardance changes under stress [58]. On
the other hand, Qi et al. also experienced the same observation in fat tissue where they
speculated this as the fat tissue also demonstrates distinguished heterogeneity in the
ratiometric depolarization images, which might be affected by different scattering properties
originated from the fluctuating superficial structures of the fat [48]. Thus, misidentification of
our current study might be associated to the wavelength dependency on incident light
(absorption coefficient of fat tissue is low at 830nm). However, our imaging scheme permits
individual fascicles can be differentiated, potentially critical for nerve sparing and/or placing
peripheral nerve stimulation devices [24, 59]. Further image-processing and tissue-
identification improvements using deep learning algorithms will be helpful.
4.3. Laser speckle contrast imaging
In our rat studies, LSCI could detect both veins and arteries in real-time by GPU accelerated
processing, whilst fluorescence imaging only permitted very short display durations in each
vessel at one time. Watson et al. demonstrated augmented microscopy using near-infrared
fluorescence images [60]; their study required exogenous injections of fluorescent dye to
visualize target structures, and temporal imaging resolutions were limited. This highlights a
significant advantage of our label-free imaging method over conventional fluorescent-agent-
aided-surgical-guidance and/or traditional surgery relying on surgeons’ visual abilities.
Another advantage is that the method is non-contact (i.e. does not require any interaction with
the tissue). Therefore, one can easily turn the technology on and off, at any time. Recently,
Feng et al. proposed an off-line method that separates the arteries and veins simultaneously in
cerebral blood flow imaging by the relative temporal minimum reflectance analysis of laser
speckle contrast images, and the same technique can be used in our application for real-time
automatic artery-vein separation by combining this method with our current setup [61]. It
should also be noted that the current system and processing method cannot provide absolute
blood-speed values, because LSCI signals are based on average velocities and red blood cell
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concentrations that require further improvement using multi exposure calculations to obtain
quantitative baseline flow measures [62].
4.4. Potential applications and future works
As we have demonstrated the usefulness of our snapshot polarimetry in nerve identification in
this study, the same optical technique can be benefited for other biomedical applications such
as skin diagnosis, collagen and tendon imaging [58, 63]. However, these anatomic structures
could further hamper the identification of nerve structures given the condition that they
exhibit clear anisotropic optical behaviors. Although we have not demonstrated the full
quantitative analysis in the current study but we instead introduced the feature extraction
strategy of intrinsic ‘zig-zag’ spiral pattern of peripheral nerves in ex vivo sample imaging to
avoid false identification thus can enhance the specificity. Our future work will focus on this
study plan for the quantitative metrics in vivo. Furthermore, the real-time nerve identification
technique will pave a new way for mapping peripheral nervous system and guiding surgical
interventions such as placement of therapeutic and modulatory probes to relieve conditions
[64]. Determining borders between viable and ischemic tissues could aid surgeons in the
identification of adequate locations for anastomoses during surgery [65]. LSCI has recently
gained interest in the assessment of intraoperative cerebral microcirculation, where local
cerebral blood flow is measured after revascularization surgery [51, 66]. LSCI has also
proven useful in the assessment of hepatic microcirculation during liver resection surgery in
mice model [50, 67]. However, to the best of our knowledge, real-time visualization of sub-
surface hepatic arteries has not been demonstrated in large animals such as porcine model,
which are similar in physiology and anatomy to those of humans. Furthermore, full-field
perfusion maps can support intravenous fluid and vasoactive drugs intraoperatively. In the
future, these maps could be applied to the liver hilum, gastric vessels, and bowel ischemia, all
of which are well studied using fluorescence angiography [39]. Lastly, these optical
techniques rely on highly adaptable light sources; thus, they can easily be extrapolated to
laparoscopes among other applications, which rely on imaging and display for minimally
invasive surgery [68].
5. Conclusion
We presented a customized operating microscope that integrates the advantages of multiple
optical imaging techniques into a single platform. The main benefits of polarimetric imaging
combined with laser-speckle-contrast imaging in one commercially available microscope
include non-invasive, real-time imaging (of critical anatomic structures), without the need for
exogenous dye injections. We validated our imaging tool through multiple in vivo studies and
demonstrated that label-free imaging performs comparable to conventional fluorescence
imaging. In future studies, this technology should be applied for minimally invasive
approaches to enhance surgical application.
Funding
Sheikh Zayed Institute for Pediatric Surgical Innovation.
Acknowledgments
Authors would like to thank to Dr. George Zalzal and ENT department for generous gift for
operating microscope. Authors also thank Dr. Martin Schnermann for his consultation.
Disclosures
The authors declare that there are no conflicts of interest related to this article.
Vol. 9, No. 3 | 1 Mar 2018 | BIOMEDICAL OPTICS EXPRESS 1110