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Emerging Technologies in Biomedical Imaging Edited by Ruikang Wang

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February 2015. Volume 5 Number 1 and Surgery. HK office: 9A Gold Shine Tower, 346-348 Queen’s Road Central, Sheung Wan,

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Endorsed by Nanotechnology Cancer Asia-Pacific (NCAP) Network & Macau Radiology Association

OCT cross section

Editorial Correspondence: Katherine Ji, MD. Managing Editor, Quantitative Imaging in Medicine

Vol 5, No 1 February 2015

In de xe d

Quantitative Imaging in Medicine and Surgery

Quantitative Imaging in Medicine and Surgery

ISSN 2223-4292

Editor-In-Chief Yi-Xiáng Wáng, MD Hong Kong SAR, China Editorial Board Hairil Rashmizal Absul Razak, PhD Selangor, Malaysia Kunwar Bhatia, FRCR Hong Kong, China Jeff W. M. Bulte, PhD Baltimore, USA Julio Carballido-Gamio, PhD San Francisco, United States Queenie Chan, PhD Hong Kong, China Zhi-Yi Chen, MB, PhD Guangzhou, China Zhen Cheng, PhD Stanford, USA Hak Soo Choi, PhD Boston, USA Peter L. Choyke, MD Bethesda, USA Hsiao-Wen Chung, PhD Taipei, Taiwan Yong Eun Chung, MD, PhD Seoul, Republic of Korea Ali Cahid Civelek, MD Louisville, USA Jean-Francois Geschwind, MD Baltimore, USA Summer L. Gibbs, PhD Portland, USA Garry E. Gold, MD Stanford, USA Jingshan Gong, MD Shenzhen, China Ali Guermazi, MD, PhD Boston, USA E. Mark Haacke, PhD Detroit, United States

Associate Editors Yongdoo Choi, PhD Goyang, Korea Taigang He, PhD London, Britain Steven Hetts, MD San Francisco, United States Edward T.D. Hoey, MRCP, FRCR Birmingham, UK Jean-Marc Idee, PhD Paris, France Stefan Jaeger PhD Bethesda, USA Jim Ji, PhD Temple, USA John Yebin Jiang, MD, PhD Ann Arbor, USA Alexander Julianov, MD, PhD, FACS Stara Zagora, Bulgaria Seung Ho Kim, MD Seoul, South Korea David A Koff, MD, FRCPC Hamilton, Canada Andrei Iagaru, MD Stanford, USA Chang-Hee Lee, MD, PhD Seoul, Korea Dong Yun Lee, PhD Seoul, South Korea Chun Li, PhD Houston, USA Dong Liang, PhD Shenzhen, China Gang Liu, PhD Xia’men, China Romaric Loffroy, MD, PhD Dijon, France Kshitij Mankad, FRCR London, United Kingdom

Ruikang K. Wang, PhD Seattle, USA

Jürgen K. Willmann, MD Stanford, USA

Greta Mok, PhD Macau SAR, China Anna Moore, PhD Boston, USA Sameh K Morcos, MD Sheffield, United Kingdom Chin K Ng, PhD Louisville, USA Yicheng Ni, MD, PhD Leuven, Belgium Edwin Oei, MD Rotterdam, The Netherlands Ramasamy Paulmurugan, PhD Stanford, USA Martin Rodriguez-Porcel, MD Rochester, United States Xiaobo Qu, PhD Xia’men, China Sarabjeet Singh, MD Boston, USA Zhonghua Sun, MB, PhD Perth, Australia Kenji Suzuki, PhD Chicago, USA Ching H. Tung, PhD New York, USA Aad van der Lugt, MD, PhD Rotterdam, The Netherlands Hebert Alberto Vargas Alvarez, MD New York, USA Defeng Wang, PhD Hong Kong SAR, China Ping Wang, MD Boston, USA Gavin Winston, MD, PhD London, United Kingdom

Chenjie Xu, PhD Singapore Yuesong Yang, MD Toronto, Canada Xincheng Yao, PhD Birmingham, USA Xin Yu, ScD Cleveland, USA Jing Yuan, PhD Hong Kong SAR, China Xiaoliang Zhang, PhD San Francisco, USA Zhongheng Zhang, MMed Jinhua, China Jie Zheng, PhD St. Louis, USA Robert Zivadinov, MD, PhD New York, USA Senior Editors Elva S. Zheng Grace S. Li Eunice X. Xu Nancy Q. Zhong Science Editor Suki X. Tang Executive Copyeditor Michael C. Peng Executive Typesetting Editor Paula P. Pan Production Editor Emily M. Shi

Aims and Scope Quantitative Imaging in Medicine and Surgery (Print ISSN 2223-4292; Online ISSN 2223-4306; QIMS) publishes peer-reviewed original reports and reviews in medical imaging, including X-ray, ultrasound, computed tomography, magnetic resonance imaging and spectroscopy, nuclear medicine and related modalities, and their application in medicine and surgery. While focus is on clinical investigations, papers on medical physics, image processing, or biological studies which have apparent clinical relevance are also published. This journal encourages authors to look at the medical images from a quantitative angle. To grade or score imaging features is also an important way of quantification. Editorial Correspondence Elva S. Zheng Senior Editor Quantitative Imaging in Medicine and Surgery HK office: Room 604, 6/F Hollywood Center, 77-91 Queen’s road, Sheung Wan, Hong Kong. Tel: +852 3188 5078; Fax: +852 3188 5078. Email: [email protected]

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Table of Contents Original Article 1

A polarization-sensitive light field imager for multi-channel angular spectroscopy of light scattering in biological tissues Rongwen Lu, Qiuxiang Zhang, Yanan Zhi, Xincheng Yao

9

Ultrasound modulated optical tomography contrast enhancement with non-linear oscillation of microbubbles Haowen Ruan, Melissa L. Mather, Stephen P. Morgan

17

Image segmentation for integrated multiphoton microscopy and reflectance confocal microscopy imaging of human skin in vivo Guannan Chen, Harvey Lui, Haishan Zeng

23

Longitudinal label-free optical-resolution photoacoustic microscopy of tumor angiogenesis in vivo Riqiang Lin, Jianhua Chen, Huina Wang, Meng Yan, Wei Zheng, Liang Song

30

Nonlinear optical microscopy for immunoimaging: a custom optimized system of high-speed, largearea, multicolor imaging Hui Li, Quan Cui, Zhihong Zhang, Ling Fu, Qingming Luo

40

Imaging endocervical mucus anatomy and dynamics in macaque female reproductive track using optical coherence tomography Siyu Chen, Ji Yi, Biqin Dong, Cheng Sun, Patrick F. Kiser, Thomas J. Hope, Hao F. Zhang

46

The application of backscattered ultrasound and photoacoustic signals for assessment of bone collagen and mineral contents Bahman Lashkari, Lifeng Yang, Andreas Mandelis

57

Tripling the detection view of high-frequency linear-array-based photoacoustic computed tomography by using two planar acoustic reflectors Guo Li, Jun Xia, Kun Wang, Konstantin Maslov, Mark A. Anastasio, Lihong V. Wang

63

In vivo imaging rhodopsin distribution in the photoreceptors with nano-second pulsed scanning laser ophthalmoscopy Tan Liu, Xiaojing Liu, Rong Wen, Byron L. Lam, Shuliang Jiao

69

Real-time automated thickness measurement of the in vivo human tympanic membrane using optical coherence tomography Zita Hubler, Nathan D. Shemonski, Ryan L. Shelton, Guillermo L. Monroy, Ryan M. Nolan, Stephen A. Boppart

78

Image reconstruction of the absorption coefficients with l1-norm minimization from photoacoustic measurements Shinpei Okawa, Takeshi Hirasawa, Toshihiro Kushibiki, Miya Ishihara

86

Assessment of oxygen saturation in retinal vessels of normal subjects and diabetic patients with and without retinopathy using Flow Oximetry System Mohamed A. Ibrahim, Rachel E. Annam, Yasir J. Sepah, Long Luu, Millena G. Bittencourt, Hyun S. Jang, Paul Lemaillet, Beatriz Munoz, Donald D. Duncan, Sheila West, Quan Dong Nguyen, Jessica C. Ramella-Roman

© AME Publishing Company. All rights reserved.

Quant Imaging Med Surg Vol 5, No 1 February 2015

97

Cranial window implantation on mouse cortex to study microvascular change induced by cocaine Kicheon Park, Jiang You, Congwu Du, Yingtian Pan

108 High-resolution harmonic motion imaging (HR-HMI) for tissue biomechanical property characterization Teng Ma, Xuejun Qian, Chi Tat Chiu, Mingyue Yu, Hayong Jung, Yao-Sheng Tung, K. Kirk Shung, Qifa Zhou 118 Real-time epidural anesthesia guidance using optical coherence tomography needle probe Qinggong Tang, Chia-Pin Liang, Kyle Wu, Anthony Sandler, Yu Chen 125 Algorithms for improved 3-D reconstruction of live mammalian embryo vasculature from optical coherence tomography data Prathamesh M. Kulkarni, Nicolas Rey-Villamizar, Amine Merouane, Narendran Sudheendran, Shang Wang, Monica Garcia, Irina V. Larina, Badrinath Roysam, Kirill V. Larin 136 Quantitative evaluation of SOCS-induced optical clearing efficiency of skull Yang Zhang, Chao Zhang, Xiewei Zhong, Dan Zhu

Review Article 143 What can biophotonics tell us about the 3D microstructure of articular cartilage? Stephen J. Matcher

Original Article 159 Optical cryoimaging of mitochondrial redox state in bronchopulmonary-dysplasia injury models in mice lungs Mohammad MasoudiMotlagh, Reyhaneh Sepehr, Nader Sheibani, Christine M. Sorenson, Mahsa Ranji 163 Anterior segment optical coherence tomography evaluation of ocular graft-versus-host disease: a case study Peng Li, Yichen Sun, Sepideh Hariri, Zhehai Zhou, Yoshihiro Inamoto, Stephanie J. Lee, Tueng T. Shen, Ruikang K. Wang

Case Report 171 Pulsatile motion of trabecular meshwork in a patient with iris cyst by phase-sensitive optical coherence tomography: a case report Yi-Chen Sun, Peng Li, Murray Johnstone, Ruikang K. Wang, Tueng T. Shen

Special Report 174 English language usage pattern in China mainland doctors: AME survey-001 initial analysis results Zhongheng Zhang, Yì -Xiáng J. Wáng

Letter to the Editor 182 On the training of young doctors in China Yì-Xiáng J. Wáng


Quant Imaging Med Surg Vol 5, No 1 February 2015

Original Article

A polarization-sensitive light field imager for multi-channel angular spectroscopy of light scattering in biological tissues Rongwen Lu, Qiuxiang Zhang, Yanan Zhi, Xincheng Yao Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA Correspondence to: Xincheng Yao, PhD. 390B Volker Hall, 1670 University Blvd, Birmingham, AL 35294, USA. Email: [email protected].

Background: Angular spectroscopy of light scattering can be used for quantitative analysis of cellular and subcellular properties, and thus promises a noninvasive methodology for in vivo assessment cellular integrity to complement in vitro histological examination. Spatial information is essential for accurate identification of localized abnormalities. However, conventional angular spectroscopy systems only provide single-channel measurement, which suffers from poor spatial resolution or requires time-consuming scanning over extended area. The purpose of this study was to develop a multi-channel angular spectroscopy for light field imaging in biological tissues. Materials and methods: A microlens array (MLA) (8×8) based light field imager for 64-channel angular spectroscopy was developed. A pair of crossed polarizers was employed for polarization-sensitive recording to enable quantitative measurement at high signal specificity and sensitivity. The polarization-sensitive light field imager enables rapid measurement of multiple sampling volumes simultaneously at 18 μm spatialresolution and 3° angular-resolution. Comparative light field imaging and electrophysiological examination of freshly isolated and physiologically deteriorated lobster leg nerves have been conducted. Results: Two-dimensional (2D) polarization-sensitive scattering patterns of the fresh nerves were highly elliptical, while they gradually lost the ellipticity and became rotationally symmetric (i.e., circular) as the nerves physiologically deteriorated due to repeated electrical stimulations. Characterized parameters, i.e., the ellipticity and the scattering intensity, rendered spatially various characteristics such as different values and deteriorating rates. Conclusions: The polarization-sensitive light field imager is able to provide multi-channel angular spectroscopy of light scattering with both spatial and angular resolutions. The light scattering properties of nerves are highly dependent on the orientation of nerves and their physiological status. Further development of polarization-sensitive multi-channel angular spectroscopy may promise a methodology for rapid and reliable identification of localized abnormalities in biological tissues. Keywords: Microlens array (MLA); light field; angular spectroscopy; nerve Submitted Oct 07, 2014. Accepted for publication Oct 19, 2014. doi: 10.3978/j.issn.2223-4292.2014.11.01 View this article at: http://dx.doi.org/10.3978/j.issn.2223-4292.2014.11.01


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Quant Imaging Med Surg 2015;5(1):1-8

Lu et al. Light field imaging of biological tissues

2

Introduction Reliable detection of precancerous cells is an essential step to ensure timely treatments which can reduce the risk of malignant development of tumors. Biological cells undergo morphological changes during cancerous development. Histological biomarkers such as nuclear enlargement (1) and hyperchromasia (darker staining because of denser chromatin) (2,3) provide indicators for early diagnosis of precancerous cells. However, conventional histological examination requires biopsies which involve painful procedure and complicated sample preparation. It is known that light scattering characteristics in cells are sensitive to the size, distribution, and relative refractive index of subcellular scatters such as nuclei (4-6). Light scattering spectroscopy has been explored for vital study of subcellular organelles (7-10), cellular volume (11) and physiological condition of biological tissues (12). It promises a new methodology to complement histological examination for in vivo detection of precancerous and cancerous cells in optically accessible organs (13,14). Morphological and physiological abnormalities of cells or nuclei have been revealed by spectral and angular spectroscopy of scattered light. The spectral spectroscopy is employed to disclose wavelength (color) dependent differences; while the angular spectroscopy is used to detect angle-resolved changes in light scattering (9,15). Angular distribution of light scattering has been known to be highly dependent on the orientation of tested specimens such as deformed elongated cells and wellpacked fibers (16,17). The angular spectroscopy is typically implemented through a single-channel goniometric setup, which suffers from low spatial resolution or requires timeconsuming scanning over extended area. Alternatively, angular resolution can be achieved by using a spatial filter at the Fourier plane that only allows scattered light with a specific scattering angle to pass through (18,19). However, this scheme requires physical manipulation of the spatial filter and it only samples one angle at a time which makes it time-consuming again for two-dimensional (2D) recording. Projecting the Fourier plane to the 2D camera could realize 2D recording of the scattering (11). However, this approach lacks the spatial resolution and the whole illuminated area could contribute to the scattering, which might overwhelm the useful signals. To address this issue, we developed a light field imager which employed a microlens array (MLA) at the image plane to realize 2D recording of the angular distribution of the light scattering while maintaining


an acceptable spatial resolution. Polarization-sensitive recording capability was integrated to enable quantitative analysis of scattering signals at high sensitivity. Lobster leg nerves were employed to demonstrate polarization-sensitive light field differentiation of normal and unhealthy tissues. Experiments on the lobster leg nerves revealed 2D elliptical scattering patterns which were highly dependent on the orientation of the nerves, while as the nerves deteriorated, the elliptical scattering patterns gradually lost the ellipticity and became rotationally symmetric (i.e., circular). We anticipate that further development of the polarizationsensitive light field imager promises a method for in vivo screening of precancerous cells by rapid multi-channel angular spectroscopy at high resolution. Materials and methods Experimental setup Figure 1A shows the optical diagram of polarization-sensitive light field imager. A near infrared (NIR) superluminescent diode (SLD-351, Superlum) was employed for illumination. The center wavelength was λ=830 nm and the band width was Δλ=60 nm. A 20× objective with 0.5 numerical aperture (NA) was used. The equivalent focal length of the objective was f 0=9 mm in the air. Camera 1 was conjugate to the specimen for the position adjustment of the specimen. The MLA was also conjugate to the specimen. The back focal plane (i.e., Frourier plan) of the MLA was imaged to Camera 2. The lenslet of the MLA was rectangular. The length of each side was d=0.3 mm. The focal length of each lenslet was fm=3 mm. The red NIR light emission diode (LED) below the sample was used for light illumination to monitor the sample through Camera 1. It was turned off during light field imaging. According to the Rayleigh criteria, the angular resolution of the system was determined by the light wavelength, diameter of the lenslet of the MLA and optical magnification of the optical system: λ f1 rθ = 1.22 ≈ 3o [1] d f0

where f1 was the focal length of the lens L1. The transverse resolution of the system was: rl = d

f0 ≈ 18 µm f1

[2]

If we multiple Eq. [1] with Eq. [2], we obtain: rθ rl = 1.22λ

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Figure 1 Polarization-sensitive light field imager. (A) Optical schematic diagram of polarization-sensitive light field imager. L1, L2 and L3, lenses; LP1 and LP2, linear polarizers; OB, objective; SLD, superluminescent diode; CO, collimator; MLA, microlens array; LF, longpass filter; DM, dichroic mirror; BS1 and BS2, beam splitters. The focal lengths of lenses L1, L2, L3 and L4 were 150, 75, 75 and 200 mm, respectively. The objective was 20× with 0.5 numerical aperture (NA). The MLA had a focal length of 3 mm and a pitch of 0.3 mm. Different color rays represent different scattering angels. The Camera 1 was conjugated to the specimen and was used to monitor the specimen, while Camera 2 was placed on a plane conjugate to the focal plane of the MLA to record angle-resolved scattering signals. The red NIR LED below the sample was used for light illumination to monitor the sample through Camera 1. It was turned off during light field signal recording. (B) Illustration of the orientation of the lobster leg nerves with respect to the polarization direction of the incident light.

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Figure 2 Polarization-sensitive light field imaging of lobster leg nerves. (A) Transmitted image acquired by Camera 1. The lobster leg nerves were placed along the y’ axis. (B) 2D angular scattering distribution map of fresh live lobster leg nerves right after dissection. This image contained in total 64 (8 by 8) scattering subunits. Each subunit was a scattering map of an 18 μm by 18 μm retinal area which was determined by the size of the lenslet and the magnification of the system. (C) 2D angular scattering distribution map of deteriorated lobster leg nerves after a series of electrical stimulations. The intensity of (C) was multiplied by a factor of 5 for a better view. The yellow and green squares in (B) indicated a center area (designated as Area 1) and an area relatively closer to the edge of the nerves (designated as Area 2), respectively, for quantitative comparison in Figures 3 and 4. 2D, two-dimensional.

Eq. [3] indicates a tradeoff between spatial and angular resolutions for light field imaging. A pair of crossed linear polarizers LP1 and LP2 was added to provide polarization-sensitive measurement. As shown in Figure 1B and Figure 2A, the lobster leg nerve bundle was placed at 45° with respect to the polarization direction of the incident light. The incident direction of the illumination light was along the z axis and perpendicular to long axis of the nerve fibers. The polarization plane (the gray plane in Figure 1B) of the incident light was parallel to the y-z plane.


Calculation of ellipticity 2D angular polarization-sensitive scattering patterns of the fresh nerves were elliptical (Figure 2B), while they became circular as the nerves physiologically deteriorated over time, after repeated electrical stimulations (Figure 2C). At intermediate states (Figure 3A), scattering patterns were less elliptical (Figure 3B-D). The ellipticity of an ellipse is defined as: e=

a 2 − b2 a2

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[4]



4

where a and b are major radius and minor radius of an ellipse, respectively. Intensity profiles of the scattering signals along the major axis (the x’ axis in Figure 3C,E) and the minor axis (the y’ axis in Figure 3C,E) were Gaussian distributed. The major radius a and the minor radius b of the ellipse were estimated by the full width at the half maximum of the Gaussian curve in the x’ axis and the y’ axis, respectively. If the ellipticity is 0, the scattering pattern is rotationally symmetric (i.e., circular), while the scattering pattern is a single line if the ellipticity is 1. It is an ellipse if the ellipticity is between 0 and 1. Data fitting To better understand the relationship between quantitative parameters (e.g., ellipticity and mean scattering intensity) and the peak-to-peak magnitude of the electrophysiological responses of the nerves M, curve fitting with least squares was applied. The fitting function employed in Figure 4A was:

e= c1 + c2 exp(− M c3 )

[5]

where c 1, c 2 and c 3 were coefficients estimated by data fitting. In contrast, the fitting function used in Figure 4B was a logistic function:

I=

a1a2 exp(a3 M ) a1 + a2 [exp(a3 M ) − 1]

[6]

where a 1, a 2 and a 3 were coefficients estimated by data fitting, and I was the mean scattering intensity. Specimen preparation We used lobster leg nerves for experimental validation of the polarization-sensitive light field imaging system. The lobster leg nerves provide a simple anisotropic specimen for technical validation of 2D recording of the angular distribution. The 2D distribution profile underwent changes as the nerves deteriorated. We used the electrophysiological response of the nerves as the physiological biomarker of functional integrity of the nerves. All animal handling procedures were approved by the Institutional Animal Care and Use Committee of the University of Alabama at Birmingham. Details of the sample preparation have been reported in our previous publications (20). Briefly, the Furusawa pulling out method was applied to extract the nerves (21). The recording chamber was filled with the Ringer solution of lobster leg nerves. At the center of the chamber, a square cover glass was placed on the top of the


recording chamber to reduce the effect of water fluctuation on the optical recording. A pair of silver electrodes was used to stimulate the nerves. Another pair of silver electrodes was used to record stimulus-evoked electrophysiological response of the nerves. Each stimulus pulse lasted for 0.1 ms. The current of the stimulus was 10 mA. Repeated stimulations were applied to accelerate physiological degeneration of the nerves. Scattering patterns of the nerves were recorded after every 20 stimulations. Results Figure 2 shows comparison of 2D angular polarizationsensitive scattering distribution between fresh and unhealthy lobster leg nerves. Figure 2A shows an image acquired by Camera 1 using the NIR LED illumination below the specimen. Figure 2A illustrates the orientation of the nerves. As shown in Figure 1B, the long axis of the nerves was parallel to the x-y plane and 45° with respect to the x axis (i.e., along the y’ axis in Figure 2A). Figure 2B shows elliptical scattering distribution patterns of the fresh nerves. The long axis of the ellipse was perpendicular to the orientation of the nerves. In contrast, as shown in Figure 2C, the deteriorated nerves showed more rotationally symmetric scattering patterns which were independent from the orientation of the nerves. Moreover, the scattering intensity of the deteriorated nerves was much weaker than that of the fresh nerves (intensity of the image Figure 2C was multiplied by a factor of 5 for a better view). We selected two areas, Area 1 (specified by yellow) and Area 2 (specified by green squares in Figure 2B) to better illustrate how the scattering distribution patterns were altered by the deterioration of the nerves. Figure 3 shows that the ellipticity of the polarizationsensitive scattering patterns degraded when the nerves were physiologically deteriorated due to repeated electrical stimulations. Scattering patterns of two representative areas, Area 1 (Figure 3B) and Area 2 (Figure 3D), were illustrated. Figure 3A shows electrophysiological responses of the nerves with various magnitudes sequentially recorded at 20 th, 160 th, 220 th and 480 th stimulation, respectively. We used peak-to-peak magnitude of electrophysiological responses as a biomarker to represent the viability level of the nerves. Peak-to-peak magnitudes of four curves in Figure 3A were around 10, 4, 1.3 and 0.03 mV, respectively. Figure 3B,D shares a similar trend overall. First, the elliptical scattering patterns gradually became rotationally symmetric as the nerves got more degenerated. Second, the polarization-sensitive scattering intensity was

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Figure 3 Polarization-sensitive scattering adaptation as a function of the viability of the lobster leg nerves. (A) Electrophysiological responses of the lobster leg nerves. Peak-to-peak magnitudes of four plots in (A) were around 10, 4, 1.3 and 0.03 mV, respectively. The peakto-peak magnitude denoted the viability of the nerves. (B) Scattering distribution maps at a center area as the nerves deteriorated. This area was specified by the yellow square in Figure 2B and designated by Area 1. The four images in (B) were scattering maps corresponding to four viability levels (specified by magnitudes of electrophysiological responses: 10, 4, 1.3 and 0.03 mV, respectively). (C) Scattering profiles along and perpendicular to the orientation of the nerves. The sampled data were from region of interest 1 (ROI 1), a scattering subunit at the top-left corner of Area 1. The y’ axis was parallel to the orientation of the nerves, while the x’ axis was perpendicular to it. Red and blue dots were raw intensity data along the x’ axis and the y’ axis, respectively. Red and blue curves were fitted data using Gaussian functions. (D) Scattering distribution maps at an area relatively closer to the edge of the nerves as the nerves deteriorated. This area was specified by the green square in Figure 2B and designated by Area 2. (E) Scattering profiles along and perpendicular to the orientation of the nerves. The sampled data were from ROI 2, the scattering subunit at the top-left corner of Area 2. The y’ axis was parallel to the orientation of the nerves, while the x’ axis was perpendicular to it. Red and blue dots were raw intensity data along the x’ axis and the y’ axis, respectively. Red and blue curves were fitted data using Gaussian functions. The brightness of the second, third and fourth images in both (B) and (D) was adjusted (multiplied by a factor of 1.5, 3 and 5, respectively) for a better visualization of the angular patterns.


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Figure 4 Quantitative comparison of scattering adaptations between two areas. (A) Ellipticities of ROI 1 and ROI 2 as a function of the peak-to-peak magnitude of the electrophysiological responses of the nerves. The curve fitting function was defined in Eq. [5]. (B) Mean scattering intensity of ROI 1 and ROI 2 as a function of the peak-to-peak magnitude of the electrophysiological responses of the nerves. The curve fitting function was defined in Eq. [6]. (C) Ellipticities of Area 1 and Area 2 as a function of the peak-to-peak magnitude of the electrophysiological responses of the nerves. (D) Mean scattering intensities of Area 1 and Area 2 as a function of the peak-to-peak magnitude of the electrophysiological responses of the nerves. Shadows in (C) and (D) indicate the standard deviations. ROI, region of interest

decreased as the nerves lost the viability. The brightness of the second, third and fourth images in both Figure 3B,D was adjusted (multiplied by a factor of 1.5, 3 and 5, respectively) for a better view. Therefore, the intensity of images in both Figure 3B,D was decreased in sequence, although the presented images render similar brightness. As shown in Figure 3C,E, scattering intensity profiles along the x’ axis and the y’ axis at the region of interest 1 (ROI 1) and ROI 2 confirmed these two observations: the loss of the ellipticity and the decrease of scattering intensity due to the deterioration of the nerves. Two quantitative parameters, the ellipticity as defined in Eq. [4] and the scattering intensity, were plotted as a function of the peak-to-peak magnitude of electrophysiological responses of the nerves. Figure 4A shows the values of the ellipticity of ROI 1 (specified by the white arrow in Figure 3B) and ROI 2 (specified by the white arrow in Figure 3D) as the nerves got deteriorated, while Figure 4B shows the mean of the scattering intensity of ROI 1 and ROI 2. To reliably compare the ellipticity and the mean of the scattering intensity between Area 1 and Area 2, we investigated all scattering subunits of Area 1 and Area 2 and


plotted the data in Figure 4C,D. Shadow in Figure 4C,D indicates the standard deviation. In Figure 4C, the overall trend of the ellipticity at Area 1 and Area 2 was in common: the value of the ellipticity was high when the nerves were fresh and was decreased due to physiological deterioration of the nerves. However, distinct spatial variations existed. First, at any given point of the electrophysiological magnitude, Area 1 always had a smaller ellipticity value than Area 2. Second, the changing rate of Area 1 was bigger than that of Area 2, since the length of the green bar was longer than that of the black bar in Figure 4C. Figure 4D shows that both Area 1 and Area 2 had a decreased scattering intensity when the magnitudes of the electrophysiological responses were small because of the degeneration of the nerves. The length of the green bar was shorter than that of the black bar in Figure 4D, which implied that the scattering intensity of Area 1 decreased faster than that of Area 2 as the electrophysiological magnitude got smaller. Discussion and conclusions In summary, we developed a polarization-sensitive light field

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imager to conduct multi-channel [64] angular spectroscopy of light scattering with both spatial and angular resolutions. Light filed imaging of freshly isolated lobster leg nerves revealed elliptical scattering patterns (Figure 2B), which reflects highly orientation dependent light distributions. Values of the ellipticity and the scattering intensity were also location dependent. Area 1 always had a smaller ellipticity (Figure 4C) and a bigger scattering intensity (Figure 4D) comparing to Area 2. The spatial variations might attribute to localized difference of nerve fiber sizes and numbers (22). Comparative light field imaging and electrophysiological examination of freshly isolated and physiologically deteriorated lobster nerves was conducted to demonstrate the potential of light field identification of tissue dysfunctions. First, as the nerves got deteriorated, the elliptical scattering patterns became more and more rotationally symmetric (Figures 3, 4A and C). Second, the mean scattering intensity was attenuated as the deterioration of the nerves got advanced. Moreover, this relationship was spatially various (Figure 4C,D), which demonstrated the advantage of the multi-channel imaging for reliable longitudinal (i.e., chronological) studies. Reflected polarization light signals might be produced by scattering (23) and birefringence (24). The disease or deterioration of the nerves might cause structural and morphological changes, e.g., disruption of well-organized cell membranes, alternation of refractive index, and cell swelling or shrinking, which could in turn lead to the change of optical properties of the nerves. The axial resolution of our current light field system is limited by the NA of the objective. To enhance the axial resolution, the optical coherence tomography technique can be integrated in future systems to gate the signal from individual depths (25,26). We anticipate that further development of polarization-sensitive light field imager promises a reliable strategy to screen precancerous cells in vivo at optically accessible organs to complement in vitro histological examination. The multi-channel angular spectroscopy is also providing an important tool to investigate biophysical mechanisms of transient polarization light changes in excitable nerve tissues (20,22). Acknowledgements This research is supported in part by NIH R21 EB012264, NIH R01 EY023522, NIH R01 EY024628, and NSF CBET-1055889. Disclosure: The authors declare no conflict of interest.


7

References 1. Yanofsky VR, Mercer SE, Phelps RG. Histopathological variants of cutaneous squamous cell carcinoma: a review. J Skin Cancer 2011;2011:210813. 2. Bloom HJ, Richardson WW. Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years. Br J Cancer 1957;11:359-77. 3. Jeong JO, Han JW, Kim JM, Cho HJ, Park C, Lee N, Kim DW, Yoon YS. Malignant tumor formation after transplantation of short-term cultured bone marrow mesenchymal stem cells in experimental myocardial infarction and diabetic neuropathy. Circ Res 2011;108:1340-7. 4. Kreysing M, Boyde L, Guck J, Chalut KJ. Physical insight into light scattering by photoreceptor cell nuclei. Opt Lett 2010;35:2639-41. 5. Sloot PM, Figdor CG. Elastic light scattering from nucleated blood cells: rapid numerical analysis. Appl Opt 1986;25:3559. 6. Mullaney PF, Dean PN. The small angle light scattering of biological cells. Theoretical considerations. Biophys J 1970;10:764-72. 7. Mourant JR, Canpolat M, Brocker C, Esponda-Ramos O, Johnson TM, Matanock A, Stetter K, Freyer JP. Light scattering from cells: the contribution of the nucleus and the effects of proliferative status. J Biomed Opt 2000;5:131-7. 8. Perelman LT, Backman V, Wallace M, Zonios G, Manoharan R, Nusrat A, Shields S, Seiler M, Lima C, Hamano T, Itzkan I, Van Dam J, Crawford JM, Feld MS. Observation of Periodic Fine Structure in Reflectance from Biological Tissue: A New Technique for Measuring Nuclear Size Distribution. Phys Rev Lett 1998;80:627-30. 9. Mourant JR, Freyer JP, Hielscher AH, Eick AA, Shen D, Johnson TM. Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics. Appl Opt 1998;37:3586-93. 10. Wilson JD, Bigelow CE, Calkins DJ, Foster TH. Light scattering from intact cells reports oxidative-stress-induced mitochondrial swelling. Biophys J 2005;88:2929-38. 11. Kinnunen M, Kauppila A, Karmenyan A, Myllylä R. Effect of the size and shape of a red blood cell on elastic light scattering properties at the single-cell level. Biomed Opt Express 2011;2:1803-14. 12. Hofmann KP, Uhl R, Hoffmann W, Kreutz W. Measurements on fast light-induced light-scattering and

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-absorption changes in outer segments of vertebrate light sensitive rod cells. Biophys Struct Mech 1976;2:61-77. Mourant JR, Bigio IJ, Boyer J, Conn RL, Johnson T, Shimada T. Spectroscopic diagnosis of bladder cancer with elastic light scattering. Lasers Surg Med 1995;17:350-7. Mourant JR, Bocklage TJ, Powers TM, Greene HM, Dorin MH, Waxman AG, Zsemlye MM, Smith HO. Detection of cervical intraepithelial neoplasias and cancers in cervical tissue by in vivo light scattering. J Low Genit Tract Dis 2009;13:216-23. Drezek R, Dunn A, Richards-Kortum R. Light scattering from cells: finite-difference time-domain simulations and goniometric measurements. Appl Opt 1999;38:3651-61. Sacks MS, Smith DB, Hiester ED. A small angle light scattering device for planar connective tissue microstructural analysis. Ann Biomed Eng 1997;25:678-89. Arifler D, Macaulay C, Follen M, Guillaud M. Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths. Biomed Opt Express 2014;5:485-98. Pyhtila JW, Wax A. Rapid, depth-resolved light scattering measurements using Fourier domain, angle-resolved low coherence interferometry. Opt Express 2004;12:6178-83. Gurjar RS, Backman V, Perelman LT, Georgakoudi I, Badizadegan K, Itzkan I, Dasari RR, Feld MS. Imaging

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human epithelial properties with polarized light-scattering spectroscopy. Nat Med 2001;7:1245-8. Lu RW, Zhang QX, Yao XC. Circular polarization intrinsic optical signal recording of stimulus-evoked neural activity. Opt Lett 2011;36:1866-8. Furusawa K. The depolarization of crustacean nerve by stimulation or oxygen want. J Physiol 1929;67:325-42. Schei JL, McCluskey MD, Foust AJ, Yao XC, Rector DM. Action potential propagation imaged with high temporal resolution near-infrared video microscopy and polarized light. Neuroimage 2008;40:1034-43. Yao XC, Foust A, Rector DM, Barrowes B, George JS. Cross-polarized reflected light measurement of fast optical responses associated with neural activation. Biophys J 2005;88:4170-7. Cohen LB, Keynes RD, Hille B. Light scattering and birefringence changes during nerve activity. Nature 1968;218:438-41. Lu RW, Curcio CA, Zhang Y, Zhang QX, Pittler SJ, Deretic D, Yao XC. Investigation of the hyper-reflective inner/outer segment band in optical coherence tomography of living frog retina. J Biomed Opt 2012;17:060504. Wang B, Lu R, Zhang Q, Yao X. Breaking diffraction limit of lateral resolution in optical coherence tomography. Quant Imaging Med Surg 2013;3:243-8.

Cite this article as: Lu R, Zhang Q, Zhi Y, Yao X. A polarization-sensitive light field imager for multi-channel angular spectroscopy of light scattering in biological tissues. Quant Imaging Med Surg 2015;5(1):1-8. doi: 10.3978/ j.issn.2223-4292.2014.11.01


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Original Article

Ultrasound modulated optical tomography contrast enhancement with non-linear oscillation of microbubbles Haowen Ruan1,2, Melissa L. Mather1,3, Stephen P. Morgan1 1

Electrical Systems and Optics Research Division, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK; 2Department of

Electrical Engineering, California Institute of Technology, 1200 E California Boulevard, Pasadena, California 91125, USA; 3Institute of Biophysics, Imaging and Optical Sciences, University of Nottingham, UK Correspondence to: Stephen P. Morgan. Head of Electrical Systems and Optics Research Division, Prof. of Biomedical Engineering, Faculty of Engineering, University of Nottingham, NG7 2RD, UK. Email: [email protected].

Background: Ultrasound modulated optical tomography (USMOT) is an imaging technique used to provide optical functional information inside highly scattering biological tissue. One of the challenges facing this technique is the low image contrast. Methods: A contrast enhancement imaging technique based on the non-linear oscillation of microbubbles is demonstrated to improve image contrast. The ultrasound modulated signal was detected using a laser pulse based speckle contrast detection system. Better understanding of the effects of microbubbles on the optical signals was achieved through simultaneous measurement of the ultrasound scattered by the microbubbles. Results: The length of the laser pulse was found to affect the system response of the speckle contrast method with shorter pulses suppressing the fundamental ultrasound modulated optical signal. Using this property, image contrast can be enhanced by detection of the higher harmonic ultrasound modulated optical signals due to nonlinear oscillation and destruction of the microbubbles. Experimental investigations were carried out to demonstrate a doubling in contrast by imaging a scattering phantom containing an embedded silicone tube with microbubbles flowing through it. Conclusions: The contrast enhancement in USMOT resulting from the use of ultrasound microbubbles has been demonstrated. Destruction of the microbubbles was shown to be the dominant effect leading to contrast improvement as shown by simultaneously detecting the ultrasound and speckle contrast signals. Line scans of a microbubble filled silicone tube embedded in a scattering phantom demonstrated experimentally the significant image contrast improvement that can be achieved using microbubbles and demonstrates the potential as a future clinical imaging tool. Keywords: Ultrasound modulated optical tomography (USMOT); microbubbles; optical scattering; harmonic imaging; ultrasound Submitted Oct 09, 2014. Accepted for publication Oct 20, 2014. doi: 10.3978/j.issn.2223-4292.2014.11.30 View this article at: http://dx.doi.org/10.3978/j.issn.2223-4292.2014.11.30

Introduction Imaging inside optically scattering media such as tissue is challenging using optical techniques alone. By detecting light modulated by ultrasound, ultrasound modulated optical tomography (USMOT) is able to provide optical spatial resolution comparable to ultrasound imaging and offers the potential for quantitative functional imaging in tissue. © AME Publishing Company. All rights reserved.

However, the ultrasound modulated light is often very weak compared with the background unmodulated light resulting in a low signal to noise ratio (SNR) and low contrast images. Parallel speckle detection techniques (1,2) were proposed to improve the SNR as a large optical acceptance angle can be obtained. Holography technique based on a photorefractive medium (3) and a photo detector array (4,5)

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Ruan et al. Ultrasound modulated microbubbles

10

further improved the SNR as optical gain of the ultrasound modulated signal was achieved through interference with a reference beam. Recently, a wavefront shaping technique (6-8) was used to enhance light at the focus of the ultrasound so that imaging contrast can be improved. The SNR improvement using all these techniques was achieved by improving the optical detection of the system. Another approach to improve the contrast of the USMOT image lies in optimising the ultrasound propagation such as reducing the effect of shear wave modulated light (9), using the acoustic radiation force (10) and detecting the second harmonic ultrasound modulated signal arising from nonlinear propagation (5,11-13) and bubble oscillation. Nonlinear ultrasound in general has been widely used to enhance the contrast of ultrasound imaging. To further improve the contrast in conventional ultrasound imaging, microbubbles are often applied to the imaging target to enhance the acoustic wave reflectivity (14). There are a number of different mechanisms that give rise to enhanced image contrast using microbubbles. Depending on the ultrasound power, microbubbles can be used to produce images based on detection of the fundamental frequency, the second harmonic, arising predominantly from non-linear bubble oscillation, and transient broadband ultrasound emission due to destruction of the bubbles (14). In particular, detection of the second harmonic signal arising from non-linear oscillation is useful as the nonlinear signal generation is much stronger from the microbubbles than the surrounding tissue. Pulse inversion is one of the techniques that is typically used in second harmonic detection as it enables the fundamental frequency to be suppressed (15). In relation to USMOT, microbubbles can be used to increase the magnitude of the ultrasound modulated optical signal. This has been studied both theoretically (16) and experimentally (17) but to date only the ultrasound modulation at the fundamental frequency has been considered. The observed signal enhancements arise due to the large differences in the acoustic impedance of microbubbles compared to the surrounding tissue. Here we demonstrate a speckle contrast detection technique in which the fundamental ultrasound modulated signal is suppressed while higher harmonic signals remain or are less suppressed. Distinct to the speckle contrast technique described previously (18), in this work a pulsed laser light modulated by an ultrasound tone burst is used that allows the system to achieve a high pass filter response. Results demonstrate that the suppression of the fundamental frequency by the high pass filter provides enhanced image contrast.


Methods Experiment The experimental setup is shown in Figure 1. One channel of a software-triggered dual channel function generator (Tektronix AFG3022B, function generator A) was used to produce 2.25 MHz tone bursts of 1 ms duration with a duty cycle that was altered depending on the required frequency response of the detection system. This signal was then amplified by a power amplifier (Amplifier Research 75A250A) and used to drive the transmitting ultrasound transducer (Olympus A304S, 2.25 MHz, 48 mm focal length). The second channel of function generator A produced pulsed signals of the same frequency and overall duration as the first. These pulses were used to trigger function generator B (Tektronix AFG3252) which generated 80 MHz burst signals for the acousto-optic modulator (AOM, Isomet 1205c-1) that modulated a continuous wave (CW) laser (Oxxius Slim 50 mW, 532 nm wavelength). The AOM acts as a shutter for the illumination by selecting the first order of the diffracted light using an aperture which allowed the illuminating laser pulses to be synchronised with the ultrasound signal. The laser pulses were then modulated by the ultrasound wave inside the scattering medium resulting in ultrasound modulated light which was detected by a CCD camera (Hamamatsu ORCA C474295-12ERG, 1,344×1,024 pixels, 12 bits, pixel size 6.45 µm by 6.45 µm). A second aperture was placed in front of the camera to control the speckle size so that speckle contrast was maximized [the speckle correlation area is sampled by four pixels (19)]. A silicone tube (3 mm inner diameter, 0.75 mm wall thickness) was embedded in a scattering phantom and connected to a computer controlled syringe pump to control the flow of microbubbles (Optison, GE Healthcare) through the silicone tube. The silicone tube was tilted by ~15° to the z axis so that incident light can uniformly illuminate the sample. The microbubbles had a nominal size of 3.0-4.5 µm and were used at a concentration of 0.2% v/v. During measurement the syringe pump was switched off and was switched on to refresh the bubbles between measurements. A second ultrasound transducer (Olympus V307, 5 MHz, 50 mm focal length, 0.6 mm calculated –6 dB beam diameter, 8.8 mm calculated –6 dB focal zone) was used as a receiver to detect the ultrasound scattered from the sample. The detected ultrasound signal was amplified (Stanford Research Systems, SR445A) and then recorded by an oscilloscope (Tektronix, DPO2024). Both the camera and oscilloscope were triggered by the

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11

A

B

C

Figure 1 Experimental setup. (A) The system simultaneously detects the ultrasound reflected from the sample and ultrasound modulated optical signal. The AOM acts as a shutter which converts the CW laser to a laser pulse train with the same repetition rate as the ultrasound signal. Transducer T generates a tone burst ultrasound signal which modulates the scattered light inside the scattering phantom. Transducer R receives the scattered signals from the tube which is centred at the focal zone of the transducer. The camera detects the light from the scattering phantom. (B) y-z view and (C) x-z view of the sample structure. The ultrasound focuses on the microbubble-filled silicone tube which is embedded in the scattering sample. Abbreviations: AB, ultrasound absorber; AP, aperture; MB, microbubble; SC, sample container; SS, scattering sample; ST, silicone tube; US, ultrasound; UT, ultrasound transducer; AOM, acousto-optic modulator; CW, continuous wave.

function generators. This experimental setup enables the speckle contrast and scattered ultrasound signal to be measured simultaneously. The background sample used in this experiment was a scattering phantom (x=90 mm, y=50 mm, z=15 mm) made of agarose and polystyrene microspheres resulting in a scattering coefficient of 2.3 mm –1 (g=0.932). The liquid sample that flowed through the silicone tube was prepared using the same polystyrene microspheres (1.6 µm) and water and therefore had a similar scattering coefficient as the background sample. Two ultrasound transducers focused on the silicone tube at the mid-plane (x, y) of the phantom at a common point. The computer controlled function generator triggered ultrasound (~1 ms duration) and the digital oscilloscope. After a time delay (40 µs) allowing for propagation of the US pulse to the focal region, the laser pulse duration (1 ms) and the camera (1 ms exposure time) were triggered simultaneously. A comparison between results obtained with and without microbubbles was carried out. The microbubble concentration used was 0.2% v/v.


The ultrasound pressure at the focal point was 480 kPa (measured with a 0.2 mm calibrated needle hydrophone, Precision Acoustics), resulting in a mechanical index (MI) of 0.32. The MI is lower than the ultrasound safety threshold (MI =0.4) for tissue containing a gas body (20). Speckle processing An optical speckle contrast detection technique (21) was used to analyse the ultrasound modulated optical signal in which speckle contrast is measured in two separate frames with the ultrasound on and off. The ultrasound modulated signals (AC) are averaged out over the integration time of the camera resulting in a decrease in speckle contrast whereas contrast is retained for a static speckle pattern (DC). A signal mixing approach can be used to shift the AC signals to DC and attenuate the signal from a certain frequency band. In this case, pulsed laser illumination is used rather than CW as described in (21). In order to image non-linear ultrasound modulated signals, the fundamental

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12

was supressed by using ultrasound to modulate a laser pulse train whose pulse repetition rate equals the ultrasound frequency. Let I(t) be the light intensity on each pixel of the camera if the laser intensity were kept constant I (t ) = I dc + I ac + 2 I dc I ac cos(2πft + ϕ )

[1]

where Idc and Iac are the amplitudes of the DC and AC light intensity respectively; f is the frequency of the ultrasound; φ is the random phase of the speckle. The modulation function of the laser pulse train is given in Fourier series, a + ∑ An cos(2πf 0 nt ) [2] b n =1 a 2  2πf 0 na  where An = sin   is the Fourier coefficient; and f0 b nπ 2   ∞

D(t ) =

is the duty cycle and fundamental frequency of the laser pulse train respectively. Therefore, the time-averaging intensity of the ultrasound modulated laser pulse train on the ith pixel is given by 1 Te I = ∫ I (t )D(t )dt [3] Te 0 Substituting I(t) and D(t) in Eq. [3], expanding the series and then neglecting the terms with f0Te(>>1) in the denominator, gives;  fT  I ≈ I ' dc + I ' ac +2 I ' dc I ' ac sinc e  cos(πfTe + ϕ )  2  [4] b ∞  nf 0Te − fTe  + I ' dc I ' ac ∑ An sinc  cos(nπf 0Te − πfTe + ϕ ) a n =1 2  

where I 'dc = I dc

a a and I 'ac = I ac is the mean light intensity b b

of DC and AC light respectively. The speckle contrast is given by C=

I2 − I

2

I 'dc + I 'ac

[5]

Substituting Eq. [4], into Eq. [5], the equation can be further simplified by retaining the first two terms of the Taylor series; C dc + C≈

2 1b ∞ M   f nT − fTe  2 2  fTe   +   ∑ An sinc 2  0 e  sinc  2 C dc   2  4  a  n =1   [6] 1+ M

where M = Cdc =

I 'dc

2

I 'ac is defined as the modulation depth, I 'dc

− I 'dc I 'dc

2

is the speckle contrast of the DC signal.

The terms within the square brackets in Eq. [6] describe the Fourier power spectrum of the laser pulse train D(t). This power spectrum can be denoted by S(f) and expressed


as:

S ( f ) = Dˆ ( f )

2

[7]

where Dˆ (f) is the Fourier transform of D(t). In the experiments, the speckle contrast difference between ultrasound off and on is considered as the ultrasound modulated signal which is given by Cdiff = Cdc − C

[8]

Substituting Eq. [6] and the expression for Cdc into Eq. [8]; M S( f ) C dc 1+ M

MC dc − C diff ≈

[9]

For a fully developed speckle pattern, the speckle contrast of the static optical signal is 1 (22) (Cdc =1). Therefore, Eq. [9] can be further reduced to C diff ≈

M [1 − S ( f )] 1+ M

[10]

From the above it can be seen that the frequency response of the detection system can be controlled by tuning the parameters (i.e., duty cycle) of the laser pulse train. Thus, this approach enables optimised detection of a specific frequency band of interest in the ultrasound modulated signal. Results In order to compare the spectrum of the ultrasound signals reflected from the microbubbles and the pass band of the system, the ultrasound signal reflected from microbubbles (0.2% v/v) was measured with its spectrum shown in Figure 2A. Based on Eq. [10], the system pass band can be calculated by normalizing the contrast difference over a range of frequencies. Figure 2B-D show the pass band of the optical detection system with speckle contrast detection using laser pulses of duty cycle at 100%, 50% and 34% respectively at a pulse repetition frequency of 2.25 MHz. It can be seen that the laser pulse train creates a notch filter which is an inverted frequency spectrum of the laser pulse train. The stop-bandwidth of each notch is determined by the camera exposure time and the envelope is determined by the laser pulse duration. Accordingly, the fundamental ultrasound modulated signal can be suppressed by using a laser pulse train with appropriate pulse duration. In the CW laser speckle contrast method (21) signals over the entire bandwidth contribute evenly to the decrease in speckle contrast (Figure 2B). When pulsed light is used the fundamental ultrasound modulated signal is suppressed while the harmonic ultrasound modulated signal due to non-linear oscillation of the ultrasound microbubbles is maintained or

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Normalized response

C

B

1

0 0

D

1

0 0

2 4 6 Frequency (MHz)

8

1

0 0

2 4 6 8 Frequency (MHz) Normalized response

Normalized response

A

2 4 6 Frequency (MHz)

8

1

0 0

2 4 6 8 Frequency (MHz)

Figure 2 (A) spectrum of the ultrasound signal reflected from the microbubbles; (B-D) pass band of the speckle contrast detection with laser pulse train of 100%, 50% and 34% duty cycle respectively.

is less heavily attenuated (depending on the duty of the laser pulse train). This is a beneficial response when microbubbles that oscillate non-linearly are used as contrast agents. The ultrasound signal reflected from the silicone tube containing the bubbles was recorded in the time domain (~1 ms). A fast Fourier transform (FFT) was then applied to the time domain ultrasound signal and a 100 kHz square window centred at the second harmonic (4.5 MHz) was used to extract the signal amplitude at the second harmonic by integration. The sample was exposed 30 times to 1 ms bursts of ultrasound and light with a 2 s time interval between exposures. To detect the ultrasound modulated optical signal with the fundamental frequency suppressed, the laser pulse train (34% duty cycle) was used as it produced a filter that significantly suppressed the fundamental component (Figure 2D). During this process, the liquid sample was not flowing (i.e., the syringe pump was off and only Brownian motion of the liquid in the tube was present). Figure 3A shows the second harmonic ultrasound signal over the sequence of exposures in the cases of without microbubbles and with microbubbles detected by the receiving ultrasound transducer. The results show that the second harmonic signal without microbubbles is lower but constant over the exposure sequence. With microbubbles, the strength of the detected second harmonic signal decays


13

rapidly indicating that many of the microbubbles were destroyed following the first few exposures. However, the reflected second harmonic signal became constant after ~10 exposures and was still higher than that without microbubbles. This indicates that some bubbles remained intact in the ultrasound focal zone. Figure 3B shows the optical speckle contrast difference, which is a measure of modulated optical signal, over the exposure sequence. A decay of the speckle contrast difference is observed for the microbubble case while it remained constant without bubbles. When microbubbles were used the ultrasound modulated optical signal enhancement can be clearly observed during the first exposure. As the exposure sequence continues, no significant enhancement is observed, although intact bubbles remained (Figure 3A). This is because the fundamental signal contribution dominated the speckle contrast difference even though it was suppressed by using the laser pulse train. To further investigate whether the broadband acoustic emission due to destruction of the microbubbles dominates the contrast improvement, the sample with microbubbles was first exposed to low pressure ultrasound (480 kPa) under the same conditions as in Figure 3. It was then exposed to high pressure ultrasound (1.6 MPa) 30 times, and finally to low pressure ultrasound again without replenishing the bubbles. Figure 4A,B show the results of ultrasound signal and optical speckle contrast difference respectively. The decay curves of the first group of 30 exposures demonstrate the partial destruction of microbubbles [Figure 4A,B (I)] . In the second group of exposures, high pressure ultrasound caused the remaining microbubbles to be destroyed resulting in strong higher harmonic ultrasound emission. This can also be observed in the ultrasound modulated optical signal [Figure 4B (II)]. After high pressure exposure, no enhanced higher harmonic emission is observed from both ultrasound signal and ultrasound modulated optical signal [Figure 4A,B (III)]. In order to quantify the ultrasound modulated signal enhancement with microbubbles over the laser pulse duty cycle, the optical speckle contrast difference (modulated signal intensity) was measured with and without microbubbles over the laser pulse duty cycle (Figure 5A, blue). Each data point is an average of 20 measurements of speckle contrast difference with the liquid sample being refreshed before each measurement by stepping the syringe pump. The signal enhancement with microbubbles can be observed at all measurement points. The signal enhancement ratio, which is defined as the difference between signals

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14

Without MB With MB

3 2 1 0 0

10 20 30 Sequence of exposure

10

A

×10–3 Without MB With MB

8 6 4 2 0


B

15

(I) 0.48 MPa (II) 1.6 MPa (III) 0.48 MPa

10

5

0

0

0.05

Speckle contrast difference

4

B

2nd harmonic US amplitude (a.u.)

5


2nd harmonic US amplitude (a.u.)

A


(I) 0.48 MPa (II) 1.6 MPa (III) 0.48 MPa

0.04 0.03 0.02 0.01 0 –0.010


Figure 3 (A) Scattered 2nd harmonic ultrasound signal from the

Figure 4 (A) Ultrasound signals with three groups of ultrasound

tube that is centred at the focal zone of the transducers; (B) optical

exposures: 0.48, 1.6 and 0.48 MPa again; (B) simultaneously

speckle contrast difference between ultrasound on and off with

detected ultrasound modulated optical signals.

0.03

with MB no MB enhancement

200

150

0.02 100 0.01

0 20

50

40 60 80 100 Laser pulse duty cycle %

0

B

4

Normalized signal intensity


A

Signal intensity enhancement (%)

fundamental frequency suppressed. MB, microbubble.

3

with MB,CW With MB, PW no MB, CW no MB, PW

2

1

0 0

5

10 15 Distance (mm)

20

25

Figure 5 (A) Blue, comparison of speckle contrast difference between cases with and without microbubbles at different laser pulse duty cycles; green, contrast enhancement over laser pulse duty cycle in percentage; (B) line scan of the silicone tube with and without bubbles at CW and 34% duty cycle PW laser illumination. MB, microbubble; CW, continuous wave; PW, pulse wave.

measured with and without microbubbles divided by the signal without bubbles, is shown in (Figure 5A, green). It demonstrates that higher contrast improvement can be obtained using lower duty cycle laser pulses. This is because higher attenuation is applied to the fundamental frequency as shown in Figure 2D and therefore the effect of the nonlinear oscillation of the microbubbles is enhanced. Line scan images of the liquid filled silicone tube are shown in Figure 5B. Images with and without microbubbles using a CW laser and 34% duty cycle pulse wave (PW) laser are compared. Base lines of these images are normalized to 1 (each one-dimensional scan is divided by the mean of the first eight data points). Without microbubbles, the image of tube is hardly discernible in both CW and pulsed laser cases. With microbubbles and CW laser illumination, the image of the silicone tube can be observed while using pulsed laser illumination the contrast of the image is


significantly enhanced (~2 times). Discussion It has been demonstrated that the image contrast in USMOT can be enhanced through the use of microbubble contrast agents. Experimental results indicate that significant harmonic signal generation occurs when there are a high proportion of microbubbles present particularly in the event of microbubble destruction. In order to improve image contrast further, a pulse laser based speckle contrast detection technique was used to detect the harmonic ultrasound modulated signals. Compared with the pulse inversion technique (12), this speckle contrast detection is less efficient in separating the fundamental and second harmonic signals. However, this technique requires does not require phase stepping and therefore reduces the effects of speckle de-correlation which

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is considerable in detecting microbubbles and also offers a high etendue which is beneficial in USMOT. Depending on the duty cycle of the laser pulses the fundamental modulated optical signal can be attenuated by different amounts and this can be used to infer the effects of the higher harmonics. Although the fundamental signal is suppressed by the filter provided by the pulse sequence of the laser, the fundamental signal is still significant in this experiment. This means that oscillation due to the second harmonic detection is undetectable and transient signal detection due to destruction of the bubbles is dominant. In order to generate significant nonlinear signals the ultrasound pressure must be sufficient. In this work an ultrasound pressure of 480 kPa is used in the majority of the experiments and significant destruction of the microbubbles occurs. This was identified as being the dominant effect responsible for the generation of significant higher harmonic signals (Figure 4). Consequently, an approach similar to agent detection imaging (23) could also apply in this destruction regime. In this case, however, significant clearing of the microbubbles at the first frame is required as the contrast difference is calculated from the difference between the first frame and second frame. This appears to be the case in the measurements shown in Figures 3 and 4. The disadvantage of working at this regime is that microbubbles need to be replenished after each exposure although in practice this would occur naturally with microbubbles being transported in the blood stream. One further difficulty in using microbubbles in the experiment carried out in this work is that concentration and position of the microbubbles are dynamic parameters, due to Brownian motion, buoyancy and mechanical instability making it difficult to maintain the signal consistency during measurement. Future investigations studying the contrast enhancement that occurs in a regime where the nonlinear oscillations are consistent would enable improvement in the signal stability and repeatability. One of the possible ways this could be achieved is through the use of the pulse inversion technique (12) in which a short time interval is used between frames. In this case the fundamental signal can be significantly suppressed and the consistent second harmonic signal due to the use of lower ultrasound pressure would be dominant. Overall, the potential applications of microbubble enhanced USMOT lie in the measurement of blood oxygen saturation (16) and haemolysis monitoring (24). Conclusions The contrast enhancement in USMOT resulting from the


15

use of ultrasound microbubbles has been demonstrated. A pulsed laser based speckle contrast detection technique has been developed to attenuate the fundamental ultrasound modulated optical signals which is attributed to both target and background tissue. Destruction of the microbubbles was shown to be the dominant effect leading to contrast improvement as shown by simultaneously detecting the ultrasound and speckle contrast signals. Finally, line scans of a microbubble filled silicone tube embedded in a scattering phantom demonstrated experimentally the significant image contrast improvement that can be achieved using microbubbles and demonstrates the potential as a future clinical imaging tool. Acknowledgements This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK (BB/F004826/1 and BB/F004923/1). Disclosure: The authors declare no conflict of interest. References 1. Li J, Ku G, Wang LV. Ultrasound-modulated optical tomography of biological tissue by use of contrast of laser speckles. Appl Opt 2002;41:6030-5. 2. Lévêque S, Boccara AC, Lebec M, Saint-Jalmes H. Ultrasonic tagging of photon paths in scattering media: parallel speckle modulation processing. Opt Lett 1999;24:181-3. 3. Murray TW, Sui L, Maguluri G, Roy RA, Nieva A, Blonigen F, DiMarzio CA. Detection of ultrasoundmodulated photons in diffuse media using the photorefractive effect. Opt Lett 2004;29:2509-11. 4. Gross M, Goy P, Al-Koussa M. Shot-noise detection of ultrasound-tagged photons in ultrasound-modulated optical imaging. Opt Lett 2003;28:2482-4. 5. Ruan H, Mather ML, Morgan SP. Pulsed ultrasound modulated optical tomography with harmonic lock-in holography detection. J Opt Soc Am A Opt Image Sci Vis 2013;30:1409-16. 6. Xu X, Liu H, Wang LV. Time-reversed ultrasonically encoded optical focusing into scattering media. Nat Photonics 2011;5:154. 7. Wang YM, Judkewitz B, Dimarzio CA, Yang C. Deeptissue focal fluorescence imaging with digitally timereversed ultrasound-encoded light. Nat Commun

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2012;3:928. 8. Si K, Fiolka R, Cui M. Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation. Nat Photonics 2012;6:657-61. 9. Zemp RJ, Kim C, Wang LV. Ultrasound-modulated optical tomography with intense acoustic bursts. Appl Opt 2007;46:1615-23. 10. Li R, Elson DS, Dunsby C, Eckersley R, Tang MX. Effects of acoustic radiation force and shear waves for absorption and stiffness sensing in ultrasound modulated optical tomography. Opt Express 2011;19:7299-311. 11. Selb J, Pottier L, Boccara AC. Nonlinear effects in acousto-optic imaging. Opt Lett 2002;27:918-20. 12. Ruan H, Mather ML, Morgan SP. Pulse inversion ultrasound modulated optical tomography. Opt Lett 2012;37:1658-60. 13. Ruan H, Mather ML, Morgan SP. Pulsed ultrasound modulated optical tomography utilizing the harmonic response of lock-in detection. Appl Opt 2013;52:4755-62. 14. Frinking PJ, Bouakaz A, Kirkhorn J, Ten Cate FJ, de Jong N. Ultrasound contrast imaging: current and new potential methods. Ultrasound Med Biol 2000;26:965-75. 15. Simpson DH, Chin CT, Burns PN. Pulse inversion Doppler: a new method for detecting nonlinear echoes from microbubble contrast agents. IEEE Trans Ultrason Ferroelectr Freq Control 1999;46:372-82. 16. Honeysett JE, Stride E, Deng J, Leung TS. An algorithm for sensing venous oxygenation using ultrasound-

modulated light enhanced by microbubbles. Proc. SPIE 8223, Photons Plus Ultrasound: Imaging and Sensing 2012, 82232Z (February 9, 2012); doi:10.1117/12.907952 17. Yuan B, Liu Y, Mehl PM, Vignola J. Microbubbleenhanced ultrasound-modulated fluorescence in a turbid medium. Appl Phys Lett 2009;95:181113. 18. Li J, Wang LV. Methods for parallel-detection-based ultrasound-modulated optical tomography. Appl Opt 2002;41:2079-84. 19. Kirkpatrick SJ, Duncan DD, Wells-Gray EM. Detrimental effects of speckle-pixel size matching in laser speckle contrast imaging. Opt Lett 2008;33:2886-8. 20. Miller DL, Averkiou MA, Brayman AA, Everbach EC, Holland CK, Wible JH Jr, Wu J. Bioeffects considerations for diagnostic ultrasound contrast agents. J Ultrasound Med 2008;27:611-32; quiz 633-6. 21. Li J, Ku G, Wang LV. Ultrasound-modulated optical tomography of biological tissue by use of contrast of laser speckles. Appl Opt 2002;41:6030-5. 22. Goodman JW. Speckle phenomena in optics: theory and applications. Roberts and Company (Greenwood Village), 2006. 23. Youk JH, Kim CS, Lee JM. Contrast-enhanced agent detection imaging: value in the characterization of focal hepatic lesions. J Ultrasound Med 2003;22:897-910. 24. Chen WS, Brayman AA, Matula TJ, Crum LA, Miller MW. The pulse length-dependence of inertial cavitation dose and hemolysis. Ultrasound Med Biol 2003;29:739-48.

Cite this article as: Ruan H, Mather ML, Morgan SP. Ultrasound modulated optical tomography contrast enhancement with non-linear oscillation of microbubbles. Quant Imaging Med Surg 2015;5(1):9-16. doi: 10.3978/ j.issn.2223-4292.2014.11.30


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Original Article

Image segmentation for integrated multiphoton microscopy and reflectance confocal microscopy imaging of human skin in vivo Guannan Chen1, Harvey Lui1,2, Haishan Zeng1,2 1

Imaging Unit-Integrative Oncology Department, British Columbia Cancer Agency Research Centre, Vancouver, BC, Canada; 2Photomedicine

Institute-Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada Correspondence to: Haishan Zeng, PhD. Imaging Unit-Integrative Oncology Department, British Columbia Cancer Agency Research Centre, 675 West 10th Avenue, Vancouver, BC, Canada V5Z 1L3. Email: [email protected].

Background: Non-invasive cellular imaging of the skin in vivo can be achieved in reflectance confocal microscopy (RCM) and multiphoton microscopy (MPM) modalities to yield complementary images of the skin based on different optical properties. One of the challenges of in vivo microscopy is the delineation (i.e., segmentation) of cellular and subcellular architectural features. Methods: In this work we present a method for combining watershed and level-set models for segmentation of multimodality images obtained by an integrated MPM and RCM imaging system from human skin in vivo. Results: Firstly, a segmentation model based on watershed is introduced for obtaining the accurate structure of cell borders from the RCM image. Secondly,, a global region based energy level-set model is constructed for extracting the nucleus of each cell from the MPM image. Thirdly, a local region-based Lagrange Continuous level-set approach is used for segmenting cytoplasm from the MPM image. Conclusions: Experimental results demonstrated that cell borders from RCM image and boundaries of cytoplasm and nucleus from MPM image can be obtained by our segmentation method with better accuracy and effectiveness. We are planning to use this method to perform quantitative analysis of MPM and RCM images of in vivo human skin to study the variations of cellular parameters such as cell size, nucleus size and other mophormetric features with skin pathologies. Keywords: Image segmentation; multiphoton microscopy (MPM); reflectance confocal microscopy (RCM); watershed; level-set model Submitted Oct 15, 2014. Accepted for publication Oct 20, 2014. doi: 10.3978/j.issn.2223-4292.2014.11.02 View this article at: http://dx.doi.org/10.3978/j.issn.2223-4292.2014.11.02

Introduction The development of non-invasive diagnostic imaging techniques for examining the microstructure of skin is of great interest for improving clinical diagnosis. Two techniques that have garnered much attention in recent years for dermatology use are reflectance confocal microscopy (RCM) and multiphoton microscopy (MPM). The optical sectioning capability of RCM has allowed in vivo, high resolution morphological images of the skin (1). MPM also has inherent optical sectioning capability due to the


nonlinear excitation process that obviates the need for a pinhole to reject out-of-focus light. Different MPM excitation mechanisms are sensitive to different biochemical components of tissue (2-4). Combining both RCM and MPM imaging (hereby called RCM/MPM imaging) potentially allows greater clinical diagnostic utility as complementary information can be revealed using the two techniques (5). We have demonstrated an integrated RCM/MPM instrument capable of simultaneously imaging human skin in vivo at up to 27 fps (5). The new challenges for clinical applications of this multimodality imaging technology are the automatic

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Chen et al. Segmentation of in vivo microscopy images of human skin

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A

B

C

Figure 1 (A) RCM image; (B) MPM image and (C) false color overlay of MPM (red) and RCM (green) images from the dorsal forearm of a 41-year-old Asian male volunteer. Excitation wavelength λex=720 nm. FOV =150×150 μm. Resolution =256×256 pixels. RCM, reflectance confocal microscopy; MPM, multiphoton microscopy.

delineation (i.e., segmentation) of cellular and subcellular architectural features on the RCM and MPM images of human skin in vivo. Cell segmentation is the essential first step in biomedical image analysis for obtaining information about objects size, area, or shape and other useful properties, as well as for locating their positions. Several algorithms for cell segmentation have been published. Image threshold (6) is the simplest method for high-speed segmentation, but it produces good results only for images with high contrast between object and background (7-9). Another common technique uses the concept of morphological image processing and considers contextual information to produce stable segmentation results (8). And markercontrolled strategies are usually utilized for avoiding over-segmentation of watershed transform (9). Spectral clustering maps the segmentation problem into graph models and uses them to find meaningful objects (10). Deformable models identify the boundaries of the object of interest by gradual development of contours or surfaces guided by internal and external forces, and include snake and balloon algorithms (11-13). Additional algorithms for image segmentation applied to the biomedical field can be found in (14-16). Considering the tradeoff between algorithmic complexity and the precision of segmentation results in our application, we adopted the watershed-based approach and incorporated some principles of the levelset method to segment cells in our in vivo multimodality (RCM/MPM) human skin images. In this work, we focus on the design and development of an algorithm that automatically isolates and segments individual cells from RCM/MPM images. Detailed experimental setup and discussion of our current RCM/ MPM imaging of human skin in vivo can be found in (5) and will not be presented here. Section 2 details the proposed


algorithm for cell segmentation. Section 3 shows the experimental results. Section 4 presents the conclusions. Methods Cell structure segmentation from RCM image Cell structure segmentation is a crucial stage in the proposed cell segmentation algorithm because the subsequent stage of nucleus and cytoplasm segmentation references valid cell structure, which can be thought of as the initial condition of the optimization problem of whole cell segmentation in order to guide the algorithm in finding a feasible solution with high performance. Cell structure segmentation is performed using gradient watershed transform to obtain accurate segmented cell structure. Watershed transform, which considers contextual information in an image and identifies the regional minima, is chosen here for cell structure segmentation. In observation of RCM image (e.g., Figure 1A), lower intensity area of a cell locates in the regional minima of images, and cell boundaries with bright intensity are in the regional maxima. Each cell is correlated with homogeneous structure in the RCM image, showing a honey-comb shaped structure in whole image. Therefore, we utilize the gradient map with prior information for the boundaries of cell to help extract and analyze the almost uniform and round cells. Direct application of the watershed segmentation algorithm by flooding from the regional minimums generally leads to over-segmentation due to noise and other local irregularities of the gradient map. To have a robust gradient map, we use the Gaussian filter to calculate gradient maps in order to smooth the noise and merge adjacent regions accordingly, where the radius of Gaussian filter is adjusted according to the average size of cells. After filtering the

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RCM image, each cell in honey-comb shaped structure is highly correlated with homogeneous regions in the gradient map. Then the classical watershed is used to segment the cell boundaries on RCM image. Cell nucleus segmentation from MPM image Based on the accurate position of cell boundary from RCM image, the relative position of each cells on the MPM image can be confirmed corresponding to the RCM image without the need of aligning the two imaging modes (5), which is a significant advantage of our system with simultaneous co-registered RCM and MPM imaging. In observation of the MPM image (e.g., Figure 1B), we see a granulated background distribution in the whole picture. And there is a low dynamic range of the intensities over the MPM images. Cytoplasm is with higher intensity, cell nucleus and other areas have similar intensity and are darker than the cytoplasm. Some cell nucleuses are not surrounded in the cytoplasm in all directions, where cytoplasms were not completely closed. Based on the anatomical features seen in the MPM images in vivo, the global region-based energy level-set method is more suitable for segmenting cell nucleus then other level-set methods. A general expression of the global energy level-set function (EG) to be minimized can be formulated as

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The first integral of [1] corresponds to a data attachment term and the second is a regularization term that minimizes the length of the curve, thereby, smoothing the evolving contour in the course of its evolution. The evolution equation is

∂φ = δ (φ ( x))(( I ( x) − v) 2 − ( I ( x) − u ) 2 ) + λδ (φ ( x)) ∂t Cytoplasm segmentation from MPM image

After obtained the contour of cell nucleus, cytoplasm can be segmented further. For the homothetic mean intensity of cell nucleus and cell border, and several connected status with them shown in MPM image, level-set method utilizing the localized region-based energy can be used to separate the image into two homogeneous regions, cytoplasm and non-cytoplasm, according to their mean values. A framework of localized region-based energy formulation in level-set segmentation methods is introduced by Lankton and Tannenbaum (17), which overcoming the problems associated with global segmentation functional. The localized strategies can use the nucleus border to set the localized initialization and evolve the energy formulation within each cell structure. First, we consider the general localized region-based energy formulation (EL) proposed in:

= EL

= EG

∫ F ( I ( x), φ ( x))dx + λ ∫ δ (φ ( x)) || ∇φ ( x) || dx

Ω

[1]

Ω

Where I(x) is the intensity at position x of the image, δ is the Dirac function, and F is given by

F ( I ( x), φ ( x= )) f in ( I ( x)) H φ ( x) + f out ( I ( x))(1 − H φ ( x)) [2] H is the Heaviside function, fin and fout provide energy criteria attached to the inside and outside region delimited by interface, and still given through the Chan-Vese function as

 fin ( I= ( x)) ( I ( x) − u ) 2   ( x)) ( I ( x) − v) 2   f out ( I=

[3]

u and v are two parameters of the mean image inside and outside the interface which updated at each iteration as follows:  (1 − H φ ( x)) I ( x)dx u = ∫Ω  ∫Ω (1 − Hφ ( x))dx    ∫Ω Hφ ( x) I ( x)dx v =  ∫Ω Hφ ( x)dx 


[4]

[5]

∫ δφ ( x)Ω∫ B( x, y) F ( y, Hφ ( y))dxdy + λ Ω∫ δ (φ ( x)) || ∇φ ( x) || dx L

[6] where B(x,y) is a mask function in which the local parameters that crave the evolution of the interface are estimated, and FL(y, H∅(y)) is the image criteria. The energy function F L is given by Eq. [2]. However, the localized parameters now is estimated in neighborhood B. the parameters u and v are replaced by localized value as Ω

 B( x, y )(1 − H φ ( y )) I ( y )dy u x = ∫Ω  ∫Ω B( x, y)(1 − Hφ ( y))dy    ∫Ω B( x, y) Hφ ( y) I ( y)dy vx =  ∫Ω B( x, y) Hφ ( y)dy

[7]

The original method use the radial mask around interface point x under evaluation. In this paper, we defined B to be the set of points belonging to one cell x and the distance to x is smaller than the average radius r which has been calculated by the segmentation result of RCM. The Mask function B(x,y) is defined as

1, if y = x + k × R , k ∈ [−r , r ] B ( x, y ) =  0, otherwise

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[8]



20

A

B

C

Figure 2 (A) Cell borders segmentation from the RCM image; (B) cell borders segmeted from the RCM image overlayed onto the MPM image; (C) cell borders segmeted from the RCM image overlayed onto to the false color overlay of RCM/MPM images. RCM, reflectance confocal microscopy; MPM, multiphoton microscopy.

Where R is the vector at position x, and r is the radius of the local neighborhood. In this work, we used minimization of the energy formulation with Lagrange coefficient, L, indicated in the following expression:

∂E = ∂li , j

∫Ω

∂F ( x, φ ( x)) 2 .Li , j ( x − i, y − j )dx ∂φ ( x)

[9]

with ∂F ( x, φ ( x)) = δ (φ ( x))(( I ( x) − vx ) 2 − ( I ( x) − u x ) 2 ) ∂φ ( x)

[10]

 m=h  ∏ ( x − i − m)( y − j − m)  m= − h,m≠0 , | x − i |≤ h,| y − j |≤ h[11] L2i , j ( x − i, y − j ) =  (h !) 4   0, otherwise

The level-set evolution may then be computed through a gradient descent on the Lagrange coefficients. The corresponding variation of the Lagrange coefficients is given as:

l i +1 = l i − ρ∇l E (l i )

[12]

where ρ is the iteration step and ∇ l corresponds to the gradient of the energy relative to the Lagrange coefficients given by Eq. [9]. Results and discussion In order to evaluate the proposed algorithms, several experiments were carried out on the MPM and RCM images. As shown in Figure 1, the MPM and RCM images are obtained from the dorsal forearm of a 41-yearold Asian male volunteer using our in vivo video rate, integrated RCM/MPM imaging system where excitation wavelength λex=720 nm, field of view (FOV) =150×150 μm,


resolution =256×256 pixels. The segmentation methods were coded using MATLAB 2012b and the data analysis was performed on a 2.66 GHz Core Duo laptop PC, with 3 GB RAM. Segment RCM image The first experiment was aimed at obtaining the cell borders from RCM image. The Gaussian filter is used to calculate gradient maps first where the radius parameter of Gaussian filter is adjusted according to the average size of cells, and the sigma parameter is half of the radius. Estimated from Figure 1A, the radius is set to 10 and the sigma is set to 5. And then, watershed transform is used to segment the cell borders. The only parameter connectivity of watershed transform is set to 8 by default. The results of RCM image segmentation is show in Figure 2A. It can be observed that the cell borders are very well segmented. If the radius parameter is changed to between 7 and 13, the results are similar except the exact locations of the boundary part because the image is fuzzy, so that the boundary cannot be exactly located even by human eyes. Figure 2B shows the Cell borders segmented from the RCM image overlayed onto the MPM image. Figure 2C shows the cell borders segmented from the RCM image overlayed onto the false color overlay of RCM/MPM image. The cell borders segmented from the RCM image also matched the position of cells in the MPM image with high accuracy. Segment cell nucleus from MPM image After obtained the cell borders, the regional center of each cell could be calculated. To segment cell nucleus from MPM image, the global region based energy level-set formulation can initialize the zero level-set at the center of each cell.

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This algorithm has one specific parameter that can be modified from the corresponding panel: the curvature term λ: it weights the influence of the regularization term of Eq. [5] (default value is set to 0.2). The evolution is only computed on the narrow-band of the level-set, and stops at the borders of cell nucleus. Φ is implemented as a signed distance function and is reinitialized at each iteration. Figure 3 shows the result of cell nucleus segmentation from the perspective of the entire graph of the MPM image. And the third column of Figure 4 shows some details of cell nucleus in each sub-image of individual cells. Segment cytoplasm from MPM image After obtained the nucleus of a cell, the cytoplasm

21

region could be calculated. To segment cytoplasm from MPM image, after initialized the zero level-set at the location of each cell nucleus, the local region-based Lagrange Continuous level-set modeling is carried out. This algorithm has three specific parameters that can be modified from the corresponding panel: (I) the radius term r: it allows to x the radius size of the neighborhood, and it is set to be the same as the radius parameter of Gaussian filter 10; (II) the curvature term λ: it weights the influence of the regularization term of Eq. [5] (default value is set to 0.2). This algorithm is a locally region-based method for its feature term is computed locally and segments nonhomogeneous objects. Φ is implemented as a signed distance function and is reinitialized at each iteration. The second column of Figure 4 shows some details of cell cytoplasm in subimages of individual cells, segmented using the proposed method. The fourth and fifth columns of Figure 4 show some detail of cell cytoplasm in each subimage of individual cells segmented using two other well-known level-set models (14,15). This demonstrated that our method for cytoplasm segmentation gives better results than those other well-known level-set methods. Conclusions

Figure 3 Cell nucleus segmentation from MPM image. MPM, multiphoton microscopy.

In this article, dedicated image segmentations for RCM/ MPM imaging of human skin in vivo have been designed and implemented. The methodology incorporates different image segmentation methods to obtaining different cell features from different imaging modalities that are

Figure 4 Cell nucleus and cytoplasm segmentation from MPM image. 1st column, original subimage of three individual cells. 2nd column, segmentation of cytoplasm by our method. 3rd column, segmentation of cell nucleus. 4th colum and 5th columns, Segmentation by two other well-known methods (14,15). MPM, multiphoton microscopy.


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22


co-registered with each other. Firstly, a segmentation model built by watershed is introduced for obtaining the accurate structure of cell borders from the RCM image. Secondly, a global region based energy level-set model is constructed for extracting the nucleus of each cell from the MPM image. Thirdly, the local region-based Lagrange Continuous level-set approach is used for segmenting cytoplasm from the MPM image. Experimental results demonstrated that the cell borders from RCM image and the boundaries of cytoplasm and nucleus from MPM image can be obtained by our method with better accuracy and effectiveness. The quantitative analysis of MPM and RCM images of in vivo human skin based on this methodology can be used to study the variations of cellular parameters such as cell size, nucleus size and other mophormetric features with pathologies and improve clinical diagnosis. Acknowledgements

7.

8.

9.

10.

11.

12.

This work was supported by the Canadian Institutes of Health Research (grant # MOP130548) and the Canadian Dermatology Foundation. 13.

Disclosure: The authors declare no conflict of interest. References

14.

1. Rajadhyaksha M, Grossman M, Esterowitz D, Webb RH, Anderson RR. In vivo confocal scanning laser microscopy of human skin: melanin provides strong contrast. J Invest Dermatol 1995;104:946-52. 2. Tsai TH, Jee SH, Dong CY, Lin SJ. Multiphoton microscopy in dermatological imaging. J Dermatol Sci 2009;56:1-8. 3. Zipfel WR, Williams RM, Christie R, Nikitin AY, Hyman BT, Webb WW. Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation. Proc Natl Acad Sci U S A 2003;100:7075-80. 4. König K. Clinical multiphoton tomography. J Biophotonics 2008;1:13-23. 5. Wang H, Lee AM, Frehlick Z, Lui H, McLean DI, Tang S, Zeng H. Perfectly registered multiphoton and reflectance confocal video rate imaging of in vivo human skin. J Biophotonics 2013;6:305-9. 6. Moo EK, Abusara Z, Abu Osman NA, Pingguan-Murphy B, Herzog W. Dual photon excitation microscopy and


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image threshold segmentation in live cell imaging during compression testing. J Biomech 2013;46:2024-31. Hill PR, Canagarajah CN, Bull DR. Image segmentation using a texture gradient based watershed transform. IEEE Trans Image Process 2003;12:1618-33. Zhou X, Li F, Yan J, Wong ST. A novel cell segmentation method and cell phase identification using Markov model. IEEE Trans Inf Technol Biomed 2009;13:152-7. Yan J, Zhao B, Wang L, Zelenetz A, Schwartz LH. Marker-controlled watershed for lymphoma segmentation in sequential CT images. Med Phys 2006;33:2452-60. Oliveira S, Ribeiro JFF, Seok SC. A spectral clustering algorithm for manufacturing cell formation. Computers & Industrial Engineering 2009;57:1008-14. Zhang Z, Zhang P, Mao X, Zhang S. The Application of the Snake Model in Carcinoma Cell Image Segment. Communications in Computer and Information Science 2008;15:455-62. Cai H, Xu X, Lu J, Lichtman JW, Yung SP, Wong ST. Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks. Neuroimage 2006;32:1608-20. Maksimovic R, Stankovic S, Milovanovic D. Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models—‘snakes’. Int J Med Inform 2000;58-59:29-37. Li C, Kao CY, Gore JC, Ding Z. Minimization of regionscalable fitting energy for image segmentation. IEEE Trans Image Process 2008;17:1940-9. Bernard O, Friboulet D, Thévenaz P, Unser M. Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. IEEE Trans Image Process 2009;18:1179-91. Liu T, Xu H, Jin W, Liu Z, Zhao Y, Tian W. Medical image segmentation based on a hybrid region-based active contour model. Comput Math Methods Med 2014;2014:890725. Lankton S, Tannenbaum A. Localizing region-based active contours. IEEE Trans Image Process 2008;17:2029-39.

Cite this article as: Chen G, Lui H, Zeng H. Image segmentation for integrated multiphoton microscopy and reflectance confocal microscopy imaging of human skin in vivo. Quant Imaging Med Surg 2015;5(1):17-22. doi: 10.3978/ j.issn.2223-4292.2014.11.02

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Original Article

Longitudinal label-free optical-resolution photoacoustic microscopy of tumor angiogenesis in vivo Riqiang Lin*, Jianhua Chen*, Huina Wang, Meng Yan, Wei Zheng, Liang Song Research Laboratory for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China *These authors contributed equally to this work. Correspondence to: Liang Song. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Nanshan, Shenzhen 518055, China. Email: [email protected].

Background: Optical-resolution photoacoustic microscopy (OR-PAM) is a high-resolution imaging technology capable of label-free imaging of the morphology and functions of the microvasculature in vivo. Previous studies of angiogenesis by OR-PAM were carried out primarily with transgenic mice and the mouse ear model. While important findings have been generated using this approach, the application of ORPAM to the more widely used subcutaneous dorsal tumor models remains challenging, largely due to the respiratory and cardiac motion artifacts, as well as the protruding tumor contours. Methods and materials: A noninvasive dorsal skin-fold (N-DSF) model, along with adaptive z-scanning and a corresponding experimental protocol, is developed. Mammary carcinoma cells (4T1) were administered subcutaneously to the backs of female BALB/c mice for tumor inoculation. The mice were anesthetized using a mixture of isofluorane and oxygen. Results: In vivo OR-PAM of angiogenesis with subcutaneous dorsal tumor models in mice has been demonstrated. To test the performance of this method, we have monitored the growth of 4T1 mouse mammary carcinoma in BALB/c mice over a period of 9 days. The major features of tumor angiogenesis, including the change of vascular tortuosity, the dilation of vessel diameters, and the increase of blood supply, have been clearly captured with OR-PAM. Conclusions: In combination with N-DSF model, OR-PAM has demonstrated outstanding capacity to provide label-free monitoring of angiogenesis in tumor. Thus, OR-PAM is of great potential to find broad biomedical applications in the pathophysiological studies of tumor and the treatments for anti-angiogenesis. Keywords: Tumor angiogenesis; optical resolution photoacoustic microscopy (OR-PAM); dorsal skin-fold (DSF) Submitted Oct 15, 2014. Accepted for publication Oct 21, 2014. doi: 10.3978/j.issn.2223-4292.2014.11.08 View this article at: http://dx.doi.org/10.3978/j.issn.2223-4292.2014.11.08

Introduction Angiogenesis is known to be a hallmark of malignant tumor growth, invasion, and metastasis (1,2). As early as in 1970s, a strategy that inhibits angiogenesis for anti-cancer therapy was proposed (3,4). Afterwards, in a few decades, investigations on angiogenesis have rapidly become one of the hottest topics in oncology research. Regulated by a number of growth factors secreted by tumor cells, the remodeling of existing vasculature and forming of new


microvessels result in the alteration of the morphology of the microvascular networks (5). Clinical imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have been applied to imaging tumor angiogenesis (6,7), but the lack of micrometer-level resolution limits their capacity to visualize fine feature changes in the microvasculature. In combination with a skin-fold window chamber mounted on a mouse’s back or head, intravital microscopy (IVM) based on confocal or

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Lin et al. Angiogenesis monitoring by photoacoustic microscopy

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two-photon laser scanning florescence microscope allows high-resolution imaging of tumors in vivo (8-12). However, due to the strong scattering of visible light in tissue, tissue penetration is usually a trade-off of the high resolution in IVM, which has an imaging depth of ~0.5 mm or less. To improve the imaging depth, optical frequency domain imaging (OFDI) and optical microangiography (OMAG) were developed and applied to imaging microvasculature networks (13-17). These techniques utilize near-infrared light to achieve a better imaging depth of ~1 mm, with sufficient spatial resolution (~10 μm) for visualizing most microvessels. In addition, they can be conveniently used for longitudinal imaging without the need of exogenous contrast agents, which are required for florescence-based IVM. In recent decades, photoacoustic tomography (PAT) is rapidly emerging as a vital tool for many biomedical imaging applications (18). PAT is based on the detection and reconstruction of depth-resolved ultrasound waves induced by local absorption of short laser pulses in biological tissue. Due to the strong optical absorption of hemoglobin at visible and near-infrared wavelengths, PAT provides extremely high sensitivity for label-free imaging of the microvasculature in vivo (19). In addition, featured by significantly lower scattering of ultrasound in tissue compared to photons, PAT can offer a scalable imaging depth ranging from several millimeters to several centimeters, depending on the required spatial resolution (20). Previous studies using photoacoustic computed tomography (PA-CT) at low ultrasonic frequency successfully depicted the main trunks of the vascular network of subcutaneously implanted tumors on the mouse back (21-24). A Fabry-Perot polymer film ultrasound sensor was also applied for mapping the tumor vasculature with a spatial resolution of ~100 μm (25). However, to clearly visualize the finest microvessels (capillaries) in tumor angiogenesis, a spatial resolution of ~5 μm is required, which can be provided by optical-resolution photoacoustic microscopy (OR-PAM)—a technique that uses a tightly focused laser beam for photoacoustic excitation (26,27). In order to avoid the respiratory and cardiac motion artifacts from living animals, previous studies of tumor angiogenesis by OR-PAM were carried out primarily on the ears of mice (28-30). Though impressive findings were generated, the ear is not commonly considered to be an ideal site for implanting tumors due to insufficient nutrition supply for tumor growth. A more widely used site for tumor implantation is the subcutaneous region on the back. In order to obtain sufficient optical imaging depth, conventionally, a skin-fold window chamber is mounted


on the back of a mouse by surgical operation for imaging the tumor vasculature with IVM. In such a setup, the skin on the back of a mouse is stretched and fitted between two glass slides allowing in vivo optical microscopy. However, the skin-fold window chamber is invasive, and may disturb the microcirculation and microenvironment for tumor growth. In addition, the animals may become susceptible to infections, and thus make longitudinal studies more challenging. Finally, the glass slices of the chamber can attenuate the photoacoustic signals seriously due to the mismatch of acoustic impedance between the tissue and glass. Therefore, it is essential to design a new experimental protocol with auxiliary devices dedicated to OR-PAM for angiogenesis imaging. In this study, a novel noninvasive dorsal skin-fold (N-DSF) animal fixture, together with adaptive z-scanning and a corresponding experimental protocol, is developed, which, for the first time to our knowledge, has enabled in vivo. OR-PAM of subcutaneously implanted tumors in the dorsal region of mice. Methods and materials Imaging system setup A detailed description of our OR-PAM system can be found in our previous publications (31,32). Briefly, as illustrated in Figure 1, a 1.8-ns pulsed laser beam emitted at 532 nm from an Nd:YAG laser source (SPOT-532, Elforlight, UK) was focused to an optical diffraction-limited spot to irradiate the sample for excitation. Then, a 75-MHz transducer (V2022, Olympus-NDT, Japan) was used to detect the time-resolved photoacoustic signals from the sample. A-line pulse repetition rate of up to 5 kHz was used in all experiments. The motor scanning and data acquisition (DAQ) were controlled by customized computer software written in LabView (2011, National Instruments). A volumetric dataset could be obtained by two-dimensional mechanical scanning of the imaging probe. Using the photoacoustic signals from the tumor skin surface as feedback, the height of the imaging probe was adjusted adaptively during the acquisition of successive B-scans. This adaptive scanning was helpful for enabling the major tumor microvessels to be imaged within focus, although their height may vary due to the protruding tumor contour. Animal tumor model Female BALB/c mice (4-6 weeks old and weighted 18-20 g)

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AP CL

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Figure 1 Schematic of the OR-PAM system. AP, aperture; CL, convex lens; FC, fiber coupler; SMF, single mode fiber; Obj, objective; UST, ultrasonic transducer; AL, acoustic lens; SO, silicone oil; EA, electrical amplifier; DAQ, data acquisition; PC, personal computer; OR-PAM, optical-resolution photoacoustic microscopy.

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For tumor inoculation, cells (1×10 6 cells/mouse) were suspended in serum-free DMEM medium and administered subcutaneously to the backs of the mice.

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Figure 2 (A) N-DSF mouse fixture designed for our ORPAM system. The dorsal skin only needs to be slightly clamped temporarily using this fixture; (B) Traditional invasive skinfold window chamber used for intravital optical microscopy, permanently mounted on the mouse back by a surgical operation; (C) a representative depth-encoded MAP image of a normal mouse’s dorsal region acquired by the combined use of OR-PAM and N-DSF fixture; (D) a representative depth-encoded MAP

All experimental animal procedures were carried out in compliance with protocols approved by the Animal Studies Committee of the Shenzhen Institutes of Advanced Technology, the Chinese Academy of Sciences. The mice were anesthetized using a mixture of isofluorane and oxygen at a concentration of 4% isofluorane for induction and 1.5% for maintenance (flow rate: 300 mL/min). The implanted tumor was positioned at the center of the scanning region. Acoustic gel was smeared between the skin and water tank for ultrasound coupling. Typically, one imaging session for a scanning region of 6 mm × 8 mm was completed within ~40 min. The tumors were imaged every 2 or 3 days after implantation. In all in vivo experiments, the optical energy fluence was maintained around 18 mJ/cm2 on the skin surface, which conforms to the ANSI standard (ANSI Z136.3-2005).

image of a normal mouse ear acquired by OR-PAM. The color scale represents depth below the skin surface. N-DSF, noninvasive dorsal skin-fold; OR-PAM, optical-resolution photoacoustic microscopy; MAP, maximum amplitude projection.

were purchased from the Medical Experimental Animal Center of Guangdong Province (Guangzhou, China). Mouse 4T1 mammary carcinoma cells were cultured with DMEM. The culture media were supplemented with 10% fetal bovine serum and 1% antibiotics-antimycotics.


N-DSF mouse fixture The overall architecture of the N-DSF fixture is shown in Figure 2A. The dorsal skin was gently lifted up and flattened on the top surface of the fixture. The relative positions of the two symmetrical metallic parts of the fixture can be adjusted via a pair of parallel slots to slightly clamp the skin. Then, four screws were used to fasten the entire fixture onto the platform. Compared with skin-fold window chamber as shown in Figure 2B, our new fixture is noninvasive, which is quite

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Figure 3 Photoacoustic depth-encoded MAP of a subcutaneously implanted 4T1 mouse mammary carcinoma 4 days after implantation. MAP, maximum amplitude projection.

beneficial for long-term imaging of tumor angiogenesis in vivo. In Figure 2C, a depth-encoded maximum amplitude projection (MAP) image of the subcutaneous blood vessels of a normal mouse is shown. In the image, different depths were encoded by various colors from green (superficial) to red (deep). From Figure 2C,D, it can be seen that the vascular morphologies of the ear and back are quite different. In particular, in Figure 2C, two distinct layers of vessels are shown, with the upper layer representing capillary-level microvessels in the dermis, while the lower layer representing relatively large arteries and veins beneath the dermis. The results suggest that our N-DSF mouse fixture is an effective tool dedicated to OR-PAM for imaging subcutaneous vessels. Results Using OR-PAM, first, we demonstrated an example of imaging 4T1 mouse mammary tumor (day 4 post implantation), as shown in Figure 3. The tumor was located at the center of the image, surrounded by a dense vascular network of the dorsal skin; several feeding vessels beneath and within the tumor could also be visualized. The dark region in the center of the image might be attributed to the necrosis of the tumor core. Overall, it can be seen that the high lateral resolution of OR-PAM has offered a unique capacity to clearly identify the surrounding tumor vasculature without any exogenous contrast agent. Further, a series of longitudinal images of the developing 4T1 tumor angiogenesis was acquired every 2 to 3 days.


Representative images acquired on days 5, 7, and 9 are shown in Figure 4, exhibiting the progressive growth of the tumor vessels. Quantitative analyses of selected representative vessels and regions were shown in Figure 5. Figure 5A compares the cross-sectional diameters averaged over 10 evenly selected positions along the vessel indicated by the green arrow in Figure 4. The error bars in the graph represent the standard deviations of the diameters measured at different positions. It shows that the average diameter of the vessel has increased by ~70.9% over 4 days. Figure 5B lists the integrated photoacoustic signals in the areas marked by the dashed white circles in Figure 4, revealing a >1.5-fold increase of blood flow in the tumor surrounding vessels. Accordingly, to show the depth of different vessels, the depth-encoded MAPs are also shown in Figure 4, where signals from superficial vessels are in green while signals from deeper ones are in red. Figure 6 shows another series of longitudinal OR-PAM of 4T1 mouse tumor angiogenesis. Similar to the previous case, the results in Figure 6A-C are featured by gradually chaotic vasculature network, and an increased tortuosity of the vasculature surrounding the tumor. The remodeling of vasculature along tumor growth is further identified in Figure 6, as shown by the arrows pointing to several representative vessels. In Figure 6, each representative vessel is labeled by one arrow of a fixed color. Conclusions To the best of our knowledge, this work represents the first demonstration of longitudinal OR-PAM of angiogenesis in a subcutaneous dorsal tumor model in mouse. It shows that, by utilizing a newly-designed N-DSF fixture, our OR-PAM system provides an outstanding capacity in labelfree monitoring of developing angiogenic vasculature. Various morphological changes of tumor vasculature were clearly visualized at high resolution. Specifically, the dynamics of tumor vascular density, diameter, and tortuosity were quantitatively tracked and analyzed over several days. Also, it can be seen that, although the same tumor cell line (4T1) was used in two series of experiments, the tumor vascular morphology could be distinct, as a result of the animal individual differences. However, some common features, including the increase of vascular density and tortuosity, were still observed. The results suggest that, OR-PAM system is of great potential to find broad biomedical applications related to revealing the pathophysiology of tumor, and the monitoring of anti-

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Quantitative Imaging in Medicine and Surgery, Vol 5, No 1 February 2015 Day 5

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Figure 4 Longitudinal OR-PAM of developing 4T1 tumor angiogenesis (case 1) on (A) day 5; (B) day 7; and (C) day 9 post implantation; (D-F) depth-encoded MAP corresponding to (A-C). The vessel indicated by the green arrow and the region marked by the white dashed circle are extracted for further quantitative analysis shown in Figure 5. OR-PAM, optical-resolution photoacoustic microscopy; MAP, maximum

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Figure 5 (A) Diameter change of the vessel indicated by the green arrow in Figure 4. The values are calculated by averaging over 10 evenly selected locations along the vessel. The error bar represents the standard deviation of the 10 measurements. (B) Change of the photoacoustic signals integrated over the area marked by the dashed white circle in Figure 4.


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Figure 6 Longitudinal OR-PAM of developing 4T1 tumor angiogenesis (case 2) on (A) day 5; (B) day 7; and (C) day 9 post implantation. Obvious vascular morphology changes can be identified by examining each representative vessel labeled by a colored arrow. OR-PAM, optical-resolution photoacoustic microscopy.

angiogenesis treatments. Acknowledgements This work was supported in part by the National Natural Science Foundation of China grant: 61205203, 81427804, 61405234, and 61475182; the National Key Basic Research [973] Program of China: 2014CB744503, and 2015CB755500; the Shenzhen Science and Technology Innovation Committee grants: ZDSY-2013-0401165820-357, KQCX-2012-0816155844-962, CXZZ-2012-0617113635699, and JCYJ-2012-0615125857-842; Guangdong Innovation Research Team Fund for Low-cost Healthcare Technologies (GIRTF-LCHT). Disclosure: The authors declare no conflict of interests. References 1. Weis SM, Cheresh DA. Tumor angiogenesis: molecular pathways and therapeutic targets. Nat Med 2011;17:1359-70. 2. Carmeliet P, Jain RK. Molecular mechanisms and clinical applications of angiogenesis. Nature 2011;473:298-307. 3. Folkman J. Anti-angiogenesis: new concept for therapy of solid tumors. Ann Surg 1972;175:409-16. 4. Brem H, Folkman J. Inhibition of tumor angiogenesis mediated by cartilage. J Exp Med 1975;141:427-39. 5. Albini A, Tosetti F, Li VW, Noonan DM, Li WW. Cancer prevention by targeting angiogenesis. Nat Rev Clin Oncol 2012;9:498-509.


6. Emblem KE, Mouridsen K, Bjornerud A, Farrar CT, Jennings D, Borra RJ, Wen PY, Ivy P, Batchelor TT, Rosen BR, Jain RK, Sorensen AG. Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat Med 2013;19:1178-83. 7. Haubner R, Beer AJ, Wang H, Chen X. Positron emission tomography tracers for imaging angiogenesis. Eur J Nucl Med Mol Imaging 2010;37:S86-103. 8. Padera TP, Stoll BR, So PT, Jain RK. Conventional and high-speed intravital multiphoton laser scanning microscopy of microvasculature, lymphatics, and leukocyte-endothelial interactions. Mol Imaging 2002;1:9-15. 9. Jain RK, Munn LL, Fukumura D. Dissecting tumour pathophysiology using intravital microscopy. Nat Rev Cancer 2002;2:266-76. 10. Fukumura D, Duda DG, Munn LL, Jain RK. Tumor microvasculature and microenvironment: novel insights through intravital imaging in pre-clinical models. Microcirculation 2010;17:206-25. 11. Hak S, Reitan NK, Haraldseth O, de Lange Davies C. Intravital microscopy in window chambers: a unique tool to study tumor angiogenesis and delivery of nanoparticles. Angiogenesis 2010;13:113-30. 12. Koehl GE, Gaumann A, Geissler EK. Intravital microscopy of tumor angiogenesis and regression in the dorsal skin fold chamber: mechanistic insights and preclinical testing of therapeutic strategies. Clin Exp Metastasis 2009;26:329-44. 13. Vakoc BJ, Lanning RM, Tyrrell JA, Padera TP, Bartlett LA, Stylianopoulos T, Munn LL, Tearney GJ, Fukumura

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D, Jain RK, Bouma BE. Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nat Med 2009;15:1219-23. 14. Vakoc BJ, Fukumura D, Jain RK, Bouma BE. Cancer imaging by optical coherence tomography: preclinical progress and clinical potential. Nat Rev Cancer 2012;12:363-8. 15. Wang RK, An L. Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo. Opt Express 2009;17:8926-40. 16. Wang RK, An L, Francis P, Wilson DJ. Depth-resolved imaging of capillary networks in retina and choroid using ultrahigh sensitive optical microangiography. Opt Lett 2010;35:1467-9. 17. Reif R, Wang RK. Label-free imaging of blood vessel morphology with capillary resolution using optical microangiography. Quant Imaging Med Surg 2012;2:207-12. 18. Wang LV, Hu S. Photoacoustic tomography: in vivo imaging from organelles to organs. Science 2012;335:1458-62. 19. Hu S, Wang LV. Photoacoustic imaging and characterization of the microvasculature. J Biomed Opt 2010;15:011101. 20. Wang LV. Prospects of photoacoustic tomography. Med Phys 2008;35:5758-67. 21. Lao Y, Xing D, Yang S, Xiang L. Noninvasive photoacoustic imaging of the developing vasculature during early tumor growth. Phys Med Biol 2008;53:4203-12. 22. Lungu GF, Li ML, Xie X, Wang LV, Stoica G. In vivo imaging and characterization of hypoxia-induced neovascularization and tumor invasion. Int J Oncol 2007;30:45-54. 23. Ku G, Wang X, Xie X, Stoica G, Wang LV. Imaging of tumor angiogenesis in rat brains in vivo by photoacoustic tomography. Appl Opt 2005;44:770-5.

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24. Siphanto RI, Thumma KK, Kolkman RG, van Leeuwen TG, de Mul FF, van Neck JW, van Adrichem LN, Steenbergen W. Serial noninvasive photoacoustic imaging of neovascularization in tumor angiogenesis. Opt Express 2005;13:89-95. 25. Laufer J, Johnson P, Zhang E, Treeby B, Cox B, Pedley B, Beard P. In vivo preclinical photoacoustic imaging of tumor vasculature development and therapy. J Biomed Opt 2012;17:056016. 26. Maslov K, Zhang HF, Hu S, Wang LV. Optical-resolution photoacoustic microscopy for in vivo imaging of single capillaries. Opt Lett 2008;33:929-31. 27. Rao B, Li L, Maslov K, Wang L. Hybrid-scanning optical-resolution photoacoustic microscopy for in vivo vasculature imaging. Opt Lett 2010;35:1521-3. 28. Oladipupo S, Hu S, Kovalski J, Yao J, Santeford A, Sohn RE, Shohet R, Maslov K, Wang LV, Arbeit JM. VEGF is essential for hypoxia-inducible factor-mediated neovascularization but dispensable for endothelial sprouting. Proc Natl Acad Sci U S A 2011;108:13264-9. 29. Oladipupo SS, Hu S, Santeford AC, Yao J, Kovalski JR, Shohet RV, Maslov K, Wang LV, Arbeit JM. Conditional HIF-1 induction produces multistage neovascularization with stage-specific sensitivity to VEGFR inhibitors and myeloid cell independence. Blood 2011;117:4142-53. 30. Hu S, Maslov K, Wang LV. In vivo functional chronic imaging of a small animal model using optical-resolution photoacoustic microscopy. Med Phys 2009;36:2320-3. 31. Chen J, Lin R, Wang H, Meng J, Zheng H, Song L. Blind-deconvolution optical-resolution photoacoustic microscopy in vivo. Opt Express 2013;21:7316-27. 32. Yang Z, Chen J, Yao J, Lin R, Meng J, Liu C, Yang J, Li X, Wang L, Song L. Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo. Opt Express 2014;22:1500-11.

Cite this article as: Lin R, Chen J, Wang H, Yan M, Zheng W, Song L. Longitudinal label-free optical-resolution photoacoustic microscopy of tumor angiogenesis in vivo. Quant Imaging Med Surg 2015;5(1):23-29. doi: 10.3978/ j.issn.2223-4292.2014.11.08


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Original Article

Nonlinear optical microscopy for immunoimaging: a custom optimized system of high-speed, large-area, multicolor imaging Hui Li1,2, Quan Cui1,2, Zhihong Zhang1,2, Ling Fu1,2, Qingming Luo1,2 1

Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Britton Chance Center for Biomedical

Photonics, Wuhan 430074, China; 2MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Correspondence to: Ling Fu. Wuhan National Lab for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China. Email: [email protected].

Background: The nonlinear optical microscopy has become the current state-of-the-art for intravital imaging. Due to its advantages of high resolution, superior tissue penetration, lower photodamage and photobleaching, as well as intrinsic z-sectioning ability, this technology has been widely applied in immunoimaging for a decade. However, in terms of monitoring immune events in native physiological environment, the conventional nonlinear optical microscope system has to be optimized for live animal imaging. Generally speaking, three crucial capabilities are desired, including high-speed, large-area and multicolor imaging. Among numerous high-speed scanning mechanisms used in nonlinear optical imaging, polygon scanning is not only linearly but also dispersion-freely with high stability and tunable rotation speed, which can overcome disadvantages of multifocal scanning, resonant scanner and acousto-optical deflector (AOD). However, low frame rate, lacking large-area or multicolor imaging ability make current polygonbased nonlinear optical microscopes unable to meet the requirements of immune event monitoring. Methods: We built up a polygon-based nonlinear optical microscope system which was custom optimized for immunoimaging with high-speed, large-are and multicolor imaging abilities. Results: Firstly, we validated the imaging performance of the system by standard methods. Then, to demonstrate the ability to monitor immune events, migration of immunocytes observed by the system based on typical immunological models such as lymph node, footpad and dorsal skinfold chamber are shown. Finally, we take an outlook for the possible advance of related technologies such as sample stabilization and optical clearing for more stable and deeper intravital immunoimaging. Conclusions: This study will be helpful for optimizing nonlinear optical microscope to obtain more comprehensive and accurate information of immune events. Keywords: Nonlinear optical microscopy; immunoimaging; high-speed; large-area; multicolor Submitted Oct 15, 2014. Accepted for publication Oct 31, 2014. doi: 10.3978/j.issn.2223-4292.2014.11.07 View this article at: http://dx.doi.org/10.3978/j.issn.2223-4292.2014.11.07


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Introduction The immune system is a network of immune molecules, cells, tissues and organs that work together to defend the organism against attacks from “foreign” invaders. The realization of immunologic function depends on cytokines delivering, cell mobilization, antibody production, etc. Each of these processes relies on cell-tissue, cell-cell, and cellmolecule interactions (1-3). Therefore, for immunological studies, intravital imaging methods which can be used to study the location, motility, contact, and interactions of individual cells in three physical dimensions over time are valuable (4). In the field of intravital imaging, the nonlinear optical microscopy based on two-photon absorption and second harmonic generation (SHG) is regarded as the current state-of-the-art (4,5). Due to its advantages of high resolution, superior tissue penetration, lower photodamage and photobleaching, and intrinsic z-sectioning ability (6-8), the technology is widely applied in immunoimaging since 2002 (1,2,9-15). However, in terms of monitoring immune events in native physiological environment, the conventional nonlinear optical microscope system has to be optimized for live animal studies. Generally speaking, three crucial capabilities are desired, including high-speed, large area and multicolor imaging. Immunocytes migrate and circulate between blood vessels, lymphatic vessels and immune organs to participate in immune events (3). T cells within the lymph node can achieve peak velocities >25 μm/min, and the default trafficking program is analogous to a random walk (9). Leukocytes circulating in blood vessels migrate at a speed of 1-10 mm/s (16). In consideration of the rapidly migration of cells within thick tissues, volume sampling should be less than 20 s to avoid blurring (2). Thus, high-speed imaging is essential to provide an accurate readout of instantaneous velocities from multidimensional [x, y, z, time (t)] data sets (10,17). High-resolution large-area imaging can avoid cell escaping out of the observation volume during long-term monitoring (10,17), and allows the visualization of cellcell, and cell-molecule interactions within proper context of surrounding tissue environment (5). Multicolor imaging allows simultaneous observation of differently labeled cell types, molecules and surrounding environment like vessels, connective tissues, etc. (10). The imaging speed of a microscope system is largely decided by the laser scanning mechanism (5). With high precision positioning, good scanning resolution, and high compactness at reasonable costs, galvanometer scanner


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driven by a linear saw-tooth control signal is one of the most commonly used scanning devices (18-21). However, the scanning speed, ranging from 0.5 to 2 frames per second (f/s), limits its application in intravital imaging systems (22). In order to acquire faster frame rate, various high-speed scanning mechanisms are incorporated into nonlinear optical microscopes. Among them, multifocal scanning imaging (23,24) and devices like resonant scanner, acoustooptical deflector (AOD) (25), and polygonal mirror (26) are commonly used. Due to limited dose of each excitation beam to the specimen, multifocal imaging has the advantages of slow photobleaching and low photodamage (27,28). Nevertheless, the interference between adjacent foci due to scattering could leads to decreased sectioning ability (27,29,30). Besides, the introduction of optical multiplexers could leads to low utilization efficiency of the laser power as well as decreased fluorescence collection efficiency. Moreover, to create multiple foci with enough energy for fluorescence excitation, higher laser power is required. In terms of resonant scanner, due to image distortion caused by the sinusoidal dependence of the position with time, data acquisition is complicated by introducing image correction or nonuniform pixel clock (31). Besides, the constant resonant frequency limits its flexibility in imaging speed adjustment (32). For AOD, since it can cause significant dispersion of ultra-short pulses and introduce spherical aberration, the imaging system is fairly complicated by laser pulse width and shape compensation modules (33-36). Another high-speed scanner is polygon which scans not only linearly but also dispersion-freely with high stability and tunable rotation speed, thereby, overcomes disadvantages of resonant scanner and AOD. However, existing nonlinear optical microscopes based on polygon scanning are generally not custom optimized for immunoimaging. A frame rate lower than 30 f/s, the lacking of large-area imaging ability or insufficient fluorescence detection channels (1-2 colors), make them unable to meet the requirements of immune event monitoring (5,37-41). In this study, we reported a polygonal mirror based nonlinear optical microscope system which was custom optimized for immunoimaging. The system has a frame rate ranging from 5 to 82 f/s depending on image dimension and the rotation speed of the polygonal mirror. At the same time, it has capabilities of large-area and multicolor imaging based on a precise-controlled three-dimensional (3-D) translation subsystem and a four-channel fluorescence or SHG detection module, respectively. Besides, to solve the common problem of fluorescent crosstalk between

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Li et al. Nonlinear optical microscopy for immunoimaging

32

Figure 1 Schematic of the high-speed, large-area, multicolor nonlinear optical microscope system. ND, neutral density filter; D, diaphragm; PD, photodiode detector; DM, dichroic mirror; O, objective lens; SP, short-pass filter; F, band-pass filter; PMT, photomultiplier tube; Upr. M., upright microscope.

different channels caused by the emission spectra overlap of fluorescent proteins or dyes in multicolor imaging, a spectrum detection channel is incorporated into the system to provide reference spectra used for further spectra unmixing (42). We validated the performance of the system by standard methods and demonstrated its ability of monitoring immune events by imaging typical immunological models including lymph node, footpad and dorsal skinfold chamber. The results indicate that, our highspeed, large-area, multicolor, nonlinear optical microscope system is expert in tracking immune dynamics in native physiological environment. Based on the system, more comprehensive and accurate information of immune events can be derived. Materials and methods Imaging system The schematic of the high-speed, large-area, multicolor nonlinear optical microscope system is shown in Figure 1. A Ti:Sapphire laser (Maitai BB, Spectra-Physics) is used as the excitation source (pulse duration: ~100 fs, repetition rate: 80 MHz and tunable range: 710 to 980 nm). By two different types of scanners, the expanded laser beam is rapidly raster scanned across a sample plane. A gold-coated 36-faceted polygon (DT-36-250-020, Lincoln Laser) accomplishes the high-speed horizontal scanning (x axis) and a galvanometric mirror with a bandwidth of 2 kHz (6215H, Cambridge Technology) performs the slow vertical scanning (y axis). The spinning polygon deflects the laser beam repetitively


by its serial facets, such that unidirectionally scans a line 36 times per rotation with a specific angular range. There are four selections for the rotation speed of the polygon: 10K, 20K, 40K and 54.945K r/min (rotation per minute). The two lenses between the scanners function together as a relay element to project the excitation beam deflected by the polygon onto the center of the galvanometric mirror. In order to facilitate live animal imaging, an upright microscope (BX51WI, Olympus) with a modified epiluminescence light path is incorporated into this imaging system. The scanning beam is coupled into the microscope by another group of relay lenses. After passing through a dichroic mirror (FF735-Di01-25×36, Semrock), the beam is focused on the specimen by an objective, typically XLPLN25XWMP, Olympus. The induced multiphoton fluorescence and SHG signals are collected by the same objective. This objective is optimized for nonlinear optical microscopy imaging. Furthermore, as a result of low magnification, 25×, high NA, 1.05, and long working distance, 2 mm, large field of view (FOV), high spatial resolution, flexible and deep intravital imaging can be simultaneously obtained via the objective. To perform large-volume imaging, a translation stage (H117, Prior Scientific) with resolution of 40 nm and travel range of 114 mm × 76 mm is used for xy-plane large-area scan, while an axial motor mounted on the objective focus knob (Prior Scientific) with resolution of 2 nm is used for z-axial scan. After split from the excitation laser by the dichroic mirror above and passing through a short-pass filter (FF01750/SP-25, Semrock) to remove residual scattered light,

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the emission light enters into the detection module. A beamsplitter (BS80/20, Olympus) separates the emission light into two beams by a ratio of 2:8. 20% of the light is detected by a spectrometer (ARC-SP2356, Princeton Instruments) for emission spectrum analysis, while 80% of that is separated into four beams by three dichroic mirrors (generally, we choose one of the two combinations: FF409Di01-25×36, FF510-Di01-25×36 and FF562-Di02-25×36; or FF510-Di01-25×36, FF562-Di02-25×36 and FF605Di01-25×36, Semrock) and enters different multiphoton fluorescence or SHG detection channels to accomplish multicolor imaging. Each channel includes a band-pass filter (Semrock) and a photomultiplier tube (PMT, H7422A-40, Hamamatsu) to record emission light of specific waveband. The signals collected by PMTs are amplified, and then acquired by a high-speed data acquisition system for image reconstruction, display and storage. The core of the data acquisition system is a FlexRIO FPGA module (PXIe7962R, National Instruments), cooperating with a data streaming system (HDD-8265, National Instruments). A separate He-Ne laser illuminating the polygon facet along with a photodiode (PDA-50, Thorlabs) detecting its reflection is used to encode the position of the polygon. The output signal from the photodiode detection is converted to TTL levels by a custom-built circuit board. Since each TTL pulse corresponds to a fixed position within the line scanned by one polygon facet, it is used as the horizontal synchronization signal. Based on this signal, the x, y scanning, xy-plane sample translation, z-axis objective translation and data acquisition are synchronized by customdesigned LabView (National Instruments) program. The y-axis scanning and data acquisition are both triggered by the TTL pulse and stops after a whole FOV plane is scanned. Then the acquired data is reconstructed to a frame of image, and further displayed and stored. Afterwards, the xy-plane translation stage steps to the next area or the z-axis objective motor steps to the next plane. By repeating these steps, serial images are obtained to reconstruct a tissue volume. 3-D time-lapse imaging can be further accomplished by scanning the tissue volume repeatedly. Mice Actb-EGFP C57BL/6 mice were obtained from Dr. Zhiying He (Second Military Medical University, Shanghai, China). CX3CR1-GFP C57BL/6 mice and B6.CgTg(Itagx-Venus)1Mnz/J mice were purchased from Jackson Laboratory (Bar Harbor, Maine, USA). All cell types in


33

Actb-EGFP C57BL/6 mice express EGFP. Most of the CX3CR1-GFP cells were monocytes (~90%). All ItagxVenus cells are dendritic cells (DCs). These transgenic mice were reproduced in the specific pathogen-free (SPF) animal facility of Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and Technology (Wuhan, China). Lymphocytes and neutrophils used for tail-vein injection were obtained from C57BL/6 mice purchased from Shanghai Slaccas Laboratory Animal Co., Ltd. (Hunan, China), and respectively stained with CMTMR dye and eFluor670 dye afterwards. All mice experiments were performed according to the animal experiment guidelines of the Animal Experimentation Ethics Committee of HUST. Data processing Serial images of one sample plane were combined into a large-area image section by a custom-designed MATLAB (The MathWorks) program. The cell migrating trajectories were obtained by tracking time-lapse imaging videos using Image-Pro Plus (Media Cybernetics). 3-D reconstruction of tissue volume was done by Imaris (Bitplane). Results Performance of the imaging system To evaluate if the imaging system meets the requirements of immunoimaging, we tested its performance including spatial resolution, FOV, imaging speed, as well as the ability for multicolor fluorescence and spectrum detection by standard methods. All of the imaging results shown in this paper were obtained by the 25× NA1.05 water immersion objective (XLPLN25XWMP, Olympus). By using FITC labeled fluorescent beads with a diameter of 200 nm (Invitrogen), we measured the lateral and axial resolution of this objective at the excitation wavelength of 800 nm, which are around 0.49 and 1.65 μm (theoretically 0.30 and 0.97 μm), respectively (Figure 2). Spatial resolution on this level is sufficient for subcellular immunoimaging. In addition, the FOV of the objective is measured by imaging fluorescent beads with a diameter of 10.4 μm (Spherotech), which is around 300 μm × 500 μm (theoretically 800 μm × 800 μm). The imaging area can be expanded by large-area imaging based on sample translation. The imaging speed of the system is largely decided by

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the high-speed horizontal scanner, polygon. In addition, for 3-D imaging, the response time and travel speed of the sample translation stage and axial objective motor should also be taken into consideration. Corresponding to the four selections of the polygon rotation speed, the calculated frame rates of single-FOV x, y, t imaging of the system are 15, 30, 60 and 82 f/s for image size of 396 pixel × 240 pixel, while 5, 10, 20 and 27 f/s for image size of 1,188 pixel × 720 pixel. The number of lines per image was set to be an integer multiple of the number of polygon facets, 36, to ensure that the same facet scans the same line of the reconstructed image on every successive frame, so that possible vertical scrolling effects introduced in the image by slight discrepancies between the facets can be avoided (5). The measured speeds were well consistent with the calculated values. In order to ensure that sufficient excitation photons can be acquired during each pixel time, we generally choose 30 f/s (polygon

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rotation speed of 20K r/min with image size of 240 pixel × 396 pixel) to achieve a compromise between imaging speed and quality. In this case, the speed of single-FOV x, y, z, t imaging is 6 f/s and large-area x, y, t imaging is 10 f/s. Thus, the imaging of a tissue volume of 300 μm × 500 μm × 50 μm with 2-μm z spacing can be finished within ~4.2 s, which is much lesser than the 20-s-volume-sampling requirement for immunoimaging to avoid blurring (2). In addition, imaging of an area of 2 mm × 2 mm, which is generally enough to observe the tumor microenvironment or the whole lymph node, can be finished within ~3 s. Generally speaking, the imaging system has provided a sufficiently fast imaging speed which not only allows tracking rapidly migrating immunocytes within the 3-D tissue environment, but also allows repeatedly scanning of the same area for multi-frame averaging to improve image quality. To test the ability of the imaging system for multicolor fluorescence detection, mixed fluorescent beads (diameter, 10.4 µm, Spherotech) with four colors (ultraviolet, yellow, Nile red and purple) are used for imaging. As shown in Figure 3, beads labeled with different dyes can be easily distinguished. Furthermore, the rhodamine B solution with a concentration of 1.5×10–2 g/L is used to test the spectrum detection ability of the system. The measured emission spectrum with a central wavelength of 584 nm is shown in Figure 4 (excitation wavelength, 780 nm), which is well consistent with the previous study (43). The four-channel fluorescence detection module allows simultaneously observing cell-cell or cell-molecular interactions within native physiological environment including extracellular matrix and vessels. Actually, using these four channels, at most 15 (2n–1, n is the number of detection channels)

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Figure 3 Multicolor imaging of mixed fluorescent beads (diameter, 10.4 µm) with four colors: ultraviolet, yellow, nile red and purple. Four channels separated by dichroic mirrors (Semrock), FF409-Di01-25×36, FF510-Di01-25×36 and FF562-Di02-25×36, as well as respectively mounted with filters (Semrock), 390/40 nm (A), 485/20 nm (B), 525/40 nm (C) and 600/14 nm (D) are used for fluorescence detection. (E) merged image of A-D. Scale bar, 50 µm.


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Figure 5 Large-area imaging of the popliteal lymph node of ActbEGFP mouse. Cells expressing EGFP are shown, mainly consisting of lymphocytes. The excitation wavelength is 880 nm. The average

populations can be separately distinguished based on binary determinations of the presence of a dye (9). Besides, the spectrum detection channel can provide the emission spectrum information of samples, which can be used as reference for spectra unmixing in multicolor imaging (42). Ability of the imaging system to monitor immune events To demonstrate the capability of the imaging system for immune events monitoring, we imaged typical immunological models including lymph node, footpad and dorsal skinfold chamber. Representative results are shown in Figures 5-8. Lymph nodes are secondary lymphoid organs. They strategically locate throughout the body to trap and present foreign antigens from peripheral tissues to prime the adaptive immune response, so that they are ideal for studies of immune cell interactions (44). Figure 5 shows the large-area imaging results of the popliteal lymph node explanted in phosphate buffered saline (PBS) which has been picked from Actb-EGFP mouse. The imaging area is 1 mm × 3 mm. Cells expressing EGFP are shown, mainly consisting of lymphocytes. Further, lymphocyte motility within the popliteal lymph node is shown in Figure 6. The lymph node was picked from Actb-EGFP mouse with tailvein injected lymphocytes, and then explanted in PBS for imaging. Migrating EGFP + host lymphocytes (green), injected lymphocytes labeled with CMTMR dye (red) and collagen fibers (blue) indicated by SHG signals can be simultaneously observed. Typical lymphocyte migrating trajectories are indicated by white lines (Figure 6B-I). It can be observed that, lymphocytes within lymph node


laser power on the sample surface is ~30 mW. The channel with a filter of 525/40 nm (Semrock) is used for fluorescence detection. Scale bar, 200 µm. EGFP, enhanced green fluorescent protein.

characteristically move in a consistent direction for short periods, while crawl in an amoeboid-manner over longer times, which is consistent with previous studies (11). The mouse footpad is a classical immunological model site for studies of delayed type hypersensitivity (DTH) reaction. Since it is easy to fix and access, it has become an ideal site for long-term noninvasive intravital optical imaging in immunology (45). Figure 7 shows the intravital motility of monocytes/macrophages (MMs) and neutrophils in the inflammatory foci of the DTH reaction occurring at the mouse footpad. The DTH reaction was elicited by aggregated ovalbumin. Since neutrophils and MMs play important roles in the development of the DTH reaction (45), neutrophils labeled with eFluor670 dye were tail-vein injected into the CX3CR1-GFP mouse which contains EGFP+ MMs, for simultaneously observation. The detailed description of the footpad model of DTH reaction can be found in the work of Meijie Luo, et al. (45). As shown in Figure 7B-I, MMs (green) and neutrophils (red) migrating within 3-D native physiological environment (extracellular matrix is shown as blue) at the early stage of DTH reaction (4 h post-challenge) can be observed. White lines indicate their typical migrating trajectories. The results demonstrate that MMs and neutrophils almost migrate directionally with approximately same velocity at the early stage of DTH reaction. The dorsal skinfold chamber has been commonly used for intravital microscopy in studies of tumors. It allows

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Figure 6 Time-lapse imaging of lymphocyte motility within the popliteal lymph node of Actb-EGFP transgenic mouse with tail-vein injected lymphocytes. B-I are magnified views of the area marked in A. Green: EGFP+ host lymphocytes; Red: injected lymphocytes labeled with CMTMR dye; Blue: collagen fibers indicated by second harmonic generation (SHG) signals. White lines indicate cell migrating trajectories. The excitation wavelength is 900 nm. The average laser power on the sample surface is ~20 mW. Three channels separated by dichroic mirrors (Semrock), FF510-Di01-25×36 and FF562-Di01-25×36, as well as respectively mounted with filters (Semrock), 452/45 nm (SHG), 520/28 nm (EGFP) and 575/15 nm (CMTMR) are used for detection. Scale bar, 50 µm.

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Figure 7 3-D intravital imaging of the motility of monocytes/macrophages (MMs) and neutrophils in the inflammatory foci of the delayed type hypersensitivity reaction occurring at the footpad of CX3CR1-GFP transgenic mouse at 4 h post-challenge. B-G are magnified views of the area marked in A. Green: EGFP+ host MMs; Red: tail-vein injected neutrophils labeled with eFluor670 dye; Blue: collagen fibers indicated by second harmonic generation (SHG) signals. White lines indicate cell migrating trajectories. The excitation wavelength is 900 nm. The average laser power on the sample surface is ~20 mW. Three of the four channels separated by dichroic mirrors (Semrock), FF510Di01-25×36, FF562-Di01-25×36 and FF605-Di01-25×36, as well as respectively mounted with filters (Semrock), 452/45 nm (SHG), 525/40 nm (EGFP) and 647/57 nm (eFluor670) are used for detection. Imaging depth, 60 µm. Scale bar, 50 µm.

long-term observation of tumor growth and the changes occurring in the tumor microenvironment at different cancer stages. Figure 8 shows the in vivo migration of DCs within the mouse dorsal skinfold chamber. The extension and retraction of pseudopodia and the amoeboid-manner crawling of DCs can be easily tracked. Further quantitative analysis on the migration behaviors of these immunocytes could provide new clues to studies of tumor immune response. Thereby, new strategies for the diagnosis and


treatment of tumors might be developed. Generally speaking, the results have demonstrated that, the imaging system performs well in immune events monitoring based on typical immunological models such as lymph node, footpad and dorsal skinfold chamber. Discussion and conclusions In conclusion, we built up a nonlinear optical microscope

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Figure 8 Intravital imaging of the motility of dendritic cells within dorsal skinfold chamber of B6.Cg-Tg(Itagx-Venus)1Mnz/J transgenic mouse. The excitation wavelength is 880 nm. The average laser power on the sample surface is ~18 mW. The channel with a filter of 543/22 nm (Semrock) is used for fluorescence detection. Scale bar, 30 µm.

system for immunoimaging, which was custom optimized for high-speed, large-area, multicolor imaging. The system has a frame rate ranging from 5 to 82 f/s. The imaging of a tissue volume of 300 μm × 500 μm × 50 μm with 2-μm z spacing can be finished within ~4.2 s, while the imaging of an area enough to observe the tumor microenvironment or the whole lymph node, 2 mm × 2 mm, can be finished within ~3 s. The four fluorescence or SHG detection channels of the system can separately distinguish at most 15 populations based on binary determinations of the presence of a dye. Beside, a spectrum detection channel is incorporated to provide reference spectra for spectra unmixing in multicolor imaging. The measured parameters such as spatial resolution, FOV, imaging speed, etc. as well as the imaging results of typical immunological models including lymph node, footpad and dorsal skinfold chamber, have all demonstrated that, the imaging system meets the requirements of immunoimaging and performs well in immune events monitoring. However, the optimizing of intravital immunoimaging is not only dependent on the imaging system, but also dependent on the sample preparation. There are three critical problems affecting the image quality of intravital immunoimaging, which can be improved by proper sample stabilization and process. First, the subject motion caused by the cardiac contractions, pulsatile blood flow, and significant overall movements during the inhalation/exhalation cycle of the live animal, usually leads to mismatch of adjacent image frames, making it difficult to do further quantitative analysis (16). Second, the imaging sites of in vivo investigations are usually irregular and difficult to access, such as lymph node, tumor, etc. To reduce subject motion and facilitate optical


access, a series of custom-designed chambers, holders or flexible detection front-end, as well as image registration and correction algorithms are developed for intravital imaging of the tumor, lymph node, footpad, lung, skin and eye (5,16,44-46). Third, although near-infrared excitation light used in nonlinear optical imaging has reduced the absorption of light by living tissues, the scattering still limits the imaging depth to ~100 µm. To extend imaging depth, various optical clearing technics have been developed for deeper in vivo imaging, but limited to skin applications (47). Advanced optical clearing technologies which can be widely used for living tissues exposed by minimally invasive surgery are expected to improve the immunoimaging depth. Therefore, by combining chambers or holders customdesigned for different imaging sites, and using advanced optical clearing technics to process the tissue, more stable and deeper imaging will be obtained by our homebuilt nonlinear optical microscope system in intravital immunoimaging. Acknowledgements This work was supported by the National Major Scientific Research Program of China (Grant No. 2011CB910401), and National Natural Science Foundation of China (No. 61178077). We thank Shuhong Qi, Meijie Luo, Lili Zhou and Zheng Liu for sample preparation. We also thank the Optical Bioimaging Core Facility of WNLO-HUST for the support in data acquisition, and the Analytical and Testing Center of HUST for spectral measurements. Disclosure: The authors declare no conflict of interest.

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Cite this article as: Li H, Cui Q, Zhang Z, Fu L, Luo Q. Nonlinear optical microscopy for immunoimaging: a custom optimized system of high-speed, large-area, multicolor imaging. Quant Imaging Med Surg 2015;5(1):30-39. doi: 10.3978/ j.issn.2223-4292.2014.11.07


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Original Article

Imaging endocervical mucus anatomy and dynamics in macaque female reproductive track using optical coherence tomography Siyu Chen1, Ji Yi1, Biqin Dong1,2, Cheng Sun2, Patrick F. Kiser1, Thomas J. Hope3, Hao F. Zhang1 1

Department of Biomedical Engineering, 2Department of Mechanical Engineering, Northwestern University, Evanston IL 60208, USA; 3Department

of Cell and Molecular Biology, Northwestern University, Chicago IL 60611, USA Correspondence to: Hao F. Zhang. Biomedical Engineering Department, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA. Email: [email protected].

Background: Endocervical mucus acts as an important barrier to block human immunodeficiency virus (HIV) infection and other sexually transmitted diseases (STDs). Disruption of the mucus layer increases the risk of infection for females. An effective method to image the mucus properties can serve as a pre-screening step to risk-stratify the susceptible population. Methods: We proposed to use optical coherence tomography (OCT) to quantitatively measure the thickness of endocervical mucus. We used a home-built bench-top OCT system to monitor the dynamic change in mucus thickness of a cultivated sample. We also fabricated a prototype endoscopic OCT probe to demonstrate potential in situ applications. Results: We observed a 200% increase in the endocervical mucus thickness after cultivating in 37 ℃ phosphate buffered saline solution for 30 minutes. During mucus hydrolysis, we found that mucus layer

thickness decreased to about 60% of its original value after applying neuraminidase. Three dimensional volumetric image of intact macaque inner vaginal wall was also acquired. Conclusions: We demonstrated that OCT can quantitatively measure the endocervical mucus thickness and its dynamics in ex vivo experiments. Endoscopic OCT has the potential to resolve fine structures inside macaque female reproductive track (FRT) for in vivo applications.

Keywords: Human immunodeficiency virus (HIV); endocervical mucus; optical coherence tomography (OCT) Submitted Oct 15, 2014. Accepted for publication Oct 20, 2014. doi: 10.3978/j.issn.2223-4292.2014.11.03 View this article at: http://dx.doi.org/10.3978/j.issn.2223-4292.2014.11.03

Introduction Human immunodeficiency virus (HIV) infection and its consequent disease, acquired immunodeficiency syndrome (AIDS), affect around 40 million people worldwide (1). While heterosexual transmission accounts for about 80% of the incidents, more than half of the victims are female (2). The anatomic structure of the female reproductive tract (FRT) makes them more prone to HIV infections. Furthermore, the low social status of women, cultural and sexual norms in certain regions all pose female at higher risk (3,4). Combined with the long latent period, highly infectious during all stages, and no existing cure, all these


conditions pose it as one of the most lethal disease for woman worldwide (1). As the immune system is weakened, victims are vulnerable to other infections and need intensive medical attention, which bring huge burden on both economic and social well-being of the affected area (5). Despite the current methods using exogenous substances to prevent AIDS infection (e.g., vaginal barrier devices and antibiotics) (6-8), there are more and more investigations focus on the intrinsic AIDS defending systems. Among them, endocervical mucus serves as an important barrier. Consisting of various glycoprotein and antibodies, the normal mucus is very effective at trapping and neutralizing invading infectious microbes (9). It is reported that mucus,

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Figure 1 Dynamic monitoring of mucus thickness using OCT. (A) Schematic diagram of a table-top vis-OCT system; (B) schematic diagram and photograph of the endoscopic NIR-OCT probe; (C) endocervical tissue sample was incubated at 37 ℃ and placed under OCT objective lens; (D) magnification of square box in (C) showing scanning region indicated by dashed box; (E) picture showing the process of performing

3-dimensional endoscopic OCT scan. BS, beam splitter; DC, dispersion compensation; GM, galvanometer mirror; L, objective lens; M1,

M2, reflective mirror; F, pigtailed fiber; FC, fiber coupler; GL, grin lens; P, prism; NIR, near infrared; OCT, optical coherence tomography; vis-OCT, visible-light optical coherence tomography.

together with other defending mechanisms, can lower the incidence of HIV infection to 0.0001-0.0040 per sexual act (2,10). Disruptions caused by the presence of ulcerative sexually transmitted diseases (STDs), including herpes simplex virus-2 (HSV-2) infection, cancroid and syphilis, will greatly weaken the effect of this natural barrier (11). These complexities will affect the viscosity and thickness of the secreted mucus layer, change of both will influence how fast pathogens penetrate and reach epithelial cells and infect the host (11-13). Therefore, a widely-accessible method to evaluate the integrity of the barrier will provide an effective screening for those at higher risk of HIV infection. For example, female with lower than normal mucus thickness will be at higher risk of infection, thus should be given higher priority when considering preventive medication. One critical parameter indicating the integrity of the endocervical barrier is the mucus thickness, which is still challenging to monitor to date, partially because its gellike appearance prevents direct measurement by visual inspection. In this study we propose to use optical coherence tomography (OCT) to dynamically measure the mucus thickness in vivo. OCT is an optical imaging modality that


generates 3-dimensional mapping of scattering contrast from the sample. Our current OCT system can achieve ~12 µm lateral resolution and micrometer-scale depth resolution. The mucus contains intrinsic contrast originating from the cell debris and undissolvable substance, whose characteristic back-scattering pattern differentiates it from underlying tissue, allowing quantitative measurement of its thickness. We demonstrate here that OCT is capable of visualizing and performing endocervical mucus thickness measurements ex vivo. We also achieve real-time dynamic monitoring of mucus secreting and hydrolysis. Finally, we integrate our OCT system into a miniature sized endoscopic probe that can be easily inserted into macaque FRT and potentially allows in vivo imaging on endocervical lumen. Methods and materials Table-top OCT imaging system We used a spectral-domain OCT system working at the visible-light spectral range (vis-OCT) to image the macaque endocervical sample (Figure 1A). The detailed description

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Chen et al. Imaging mucus using optical coherence tomography

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To demonstrate that our approach has the ability to perform in vivo measurements, we constructed a prototype endoscopic OCT probe that can perform linear and circular scans. To achieve higher penetration depth for in situ measurement, near infrared (NIR) light source was used instead of the visible light. The endoscopic probe is a fiber-based, miniature sized lens-prism complex. The schematic diagram of the OCT probe was shown in Figure 1B. We used a gradientindex (GRIN) lens to obtain light focusing. A right-angle prism is attached on the GRIN lens to achieve desired sideview imaging. The lens-prism complex is mounted on a rotating shaft, which is driven by a step motor to control the circular scan. A motorized linear translation stage was used to move the probe from the proximal to distal position, allowing a 3-dimensional cylindrical scanning pattern to be performed. The photo in Figure 1B shows the dimension of a finished prototype endoscopic OCT probe. The outer diameter of the probe is roughly 4.5 mm, which can be easily inserted into the macaque FRT.

In order to imaging the dynamic secretion of endocervical mucus, we gently flushed away any remaining mucus with PBS on our sample prior to the imaging sequence and took OCT imaging immediately thereafter. The imaging area was indicated by a dashed box in Figure 1D, which covered an area of 2×2 mm2. After the initial imaging, we continue to take OCT images at the same location every 10 minutes to monitor the thickness change of the mucus layer for 40 minutes. During the entire imaging period, the sample was half submerged in PBS solution and kept at constant temperature of 37 ℃. After imaging the secretion sequence, we performed a dynamic monitoring of mucus hydrolysis. We used neuraminidase to hydrolyze the terminal α-Neu5Ac of mucus glycoprotein. We re-suspended 10,000 units of neuraminidase (50,000 units/mL, New England BioLabs) in 500 µL-50 mM sodium citrate solution. The pH of the reactive solution was controlled to be 6.0 and incubated at 37 ℃ for 5 minutes prior to use. We applied the neuraminidase reactive solution drop by drop using a disposable pipet until the entire exposed surface of the sample was covered, which took about 2.5 minutes. We immediately took an OCT image after we applied neuraminidase. The same imaging protocol as described above was used. After that, we took another OCT image 2.5 minutes later and three more every 5 minutes. We applied a few drops of neuraminidase every other minute during the imagining sequence to replenish the consumed enzyme. For ex vivo endoscopic imaging on vagina duct, we carefully fixed the intact macaque FRT on a home-made animal dissection table while keeping the vagina opening exposed. We inserted our endoscopic OCT probe into the vagina duct about 1 to 2 inches deep and began 3-dimensional cylindrical imaging (Figure 1E). During imaging acquisition, PBS solution was added to prevent dehydration.

Tissue preparation and imaging process

OCT image processing and mucus thickness measurement

Endocervical and vaginal tissue samples were harvested from sacrificed macaque. The samples were kept refrigerated in PBS solution for transportation and storage, which happened in less than 24 hours. For ex vivo dynamic monitoring of mucus thickness, we dissected and trimmed the sample into pieces of 0.5 by 0.5 inch to expose the endocervical duct. Tissue was incubated in a homemade imaging-compatible incubator with the temperature set to 37 ℃. The incubator contained 1% PBS solution to maintain the moisture and osmolality (Figure 1C).

To reconstruct the OCT image, we first removed the DC component and normalize the interference spectrum by wavelength-depended light source intensity. We then resampled the spectrum into equal-interval k-space. We applied fast Fourier transform on the re-sampled spectrum to retrieve the depth-resolved OCT structural image. For endoscopic OCT image, a coordinate transformation was performed to map the acquired matrix back to polar form for better visualization. In order to quantify the dynamic change in mucus layer

of the system can be found in our earlier publication (14). Briefly, a super continuum laser (SuperK, NKT photonics) provided illuminating light ranging from 512 to 620 nm. The light was split by a 50:50 beam splitter (BS) into the sampling arm and the reference arm. A pair of galvanometer mirrors (GM) raster scanned the sampling laser beam through a scanning objective (Thorlabs, EFL =37 mm) to cover a rectangular field of view. The back-scattered sample beam and the reference beam were recombined, and a home-built spectrometer captured the spectral interference pattern generated. When set at 25 kHz A-line rate, it took 2.6 seconds to acquire an OCT volume consisting of 65,536 A-lines, a typical value used in our experiment. Endoscopic OCT probe


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43

(a)

A

100 µm

E LP

(b)B

(c)C

Relative thickness [arb. u.]

Relative thickness [arb. u.]

** 3.0

2.0

1.0

0

10 20 30 Time [min]

40

** 1.0 0.8 0.6 0.4 0.2 0

2.5

5 10 Time [min]

15

Figure 2 Dynamic monitoring of mucus layer thickness change during secretion and enzymatic hydrolysis. (A) Single vis-OCT B-scan showing anatomical features of endocervical tissue; (B) measured maximum mucus thickness when incubated in PBS solution at 37 ℃

during a period of 40 minutes; (C) measured maximum mucus thickness after adding neuraminidase. Observed over a period of 15 minutes. **, P

MSMS

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