Vascular distribution imaging of dorsal skin window chamber in mouse with spectral domain optical coherence tomography

Abstract

Doppler optical coherence tomography or optical Doppler tomography (ODT) has been demonstrated to spatially localize flow velocity mapping as well as to obtain images of microstructure of samples simultaneously. In recent decades, spectral domain Doppler optical coherence tomography (OCT) has been applied to observe three-dimensional (3D) vascular distribution. In this study, we developed a spectral domain optical coherence tomography system (SD-OCT) using super luminescent diode (SLD) as light source. The center wavelength of SLD is 835 nm with a 45-nm bandwidth. Theoretically, the transverse resolution, axial resolution and penetration depth of this SD-OCT system are 6.13 µm, 6.84 µm and 3.62 mm, respectively. By imaging mouse model with dorsal skin window chamber, we obtained a series of real-time OCT images and reconstructed 3D images of the specific area inside the dorsal skin window chamber by Amira. As a result, we can obtain the clear and complex distribution images of blood vessels of mouse model.

Keywords

optical coherence tomography (OCT) / mouse / dorsal skin window chamber / vascular distribution

1 Introduction

Optical coherence tomography (OCT) is a non-invasive and non-destructive optical imaging technology, first proposed by Huang et al. in 1991 [ 1]. Compared with X-Ray, ultrasound and magnetic resonance imaging, OCT is called “optical biopsy” [ 2], because of its specific advantages such as high resolution, high sensitivity and real-time imaging. In 1991, Huang et al. obtained the microstructure of human retina and the structure of coronary artery wall successfully by building a time-domain optical coherence tomography (TD-OCT) system [ 1]. However, in this method, the length of the reference arm is rapidly scanned over a distance that corresponds to the imaging-depth range of the sample, leading to limited imaging speed. Fourier-domain OCT (FD-OCT), which has high signal-to-noise ratio (SNR) and high sensitivity, was later developed [ 3- 5].The depth information of the sample is not obtained by mechanical scanning of the reference arm but by Fourier transform based on the detected spectral signal. According to the difference of probing scheme, FD-OCT can be divided into two types, spectral domain OCT [ 6] and swept source OCT [ 7, 8]. So far, research groups have made great efforts to improve OCT by developing various functional expansions of traditional OCT, such as Doppler OCT [ 9], polarization-sensitive OCT [ 10] and molecular contrast OCT [ 11].These developments have made OCT technology widely applied in biomedicine [ 12- 18], particularly in ophthalmology [ 19]. Doppler OCT can obtain high-resolution images of tissue structure and hemodynamics simultaneously, combining Doppler effect with OCT technology. Doppler OCT has been applied to the depth-resolved cross-sectional flow imaging of tissues in vivo [ 20- 24]. Furthermore, blood flow imaging of retina vessels was also achieved by Doppler OCT [ 25, 26]. These results were based on TD-OCT system, in which mechanical scanning of the reference arm exists. Compared with TD-OCT, spectral domain optical coherence tomography system (SD-OCT) has higher sensitivity [ 3, 4] and the imaging speed is 150 times faster than TD-OCT [ 27].Therefore, researchers have focused on using FD-OCT system to observe blood flow imaging of retina vessels [ 28- 30]. Wang's group and Yatagai’s group have done a lot of research on three-dimensional (3D) visualization of retinal vessels. They developed high-speed SD-OCT system to gain 3D images of the blood vessels in mouse brain, i.e., optical coherence angiography (OCA) [ 31, 32]. Vakoc et al. applied optical frequency domain imaging (OFDI) technology to detect the microenvironment of various tumor models at varying sites in mice, gaining 3D depth-projected vascular distribution of brain bearing tumor and cross-sectional images of back implanted in the dorsal skin fold chamber model with an MCaIV tumor [ 33].However, high-resolution, 3D vascular distribution of the mouse with dorsal skin window chamber has not been obtained by SD-OCT system till now. Here, the mentioned chamber implanted into the dorsal skin in mice [ 34] was derived from Sandison [ 35] in order to investigate the microcirculation. Because of repeated analysis of the microcirculation over a period of time, this animal model has been applied to assess not only the angiogenesis in a variety of processes including endometriosis and tumor growth, but also the early phases of preclinical development of Bevacizumab [ 36, 37].

In this study, we developed an SD-OCT system for imaging mouse model with dorsal skin window chamber, obtained a series of real-time OCT images and reconstructed 3D images of the specific area inside the dorsal skin window chamber mouse.

2 Experimental setup

OCT system is a low coherent system based on Michelson interferometer. The schematic diagram of the system used in this study is shown in Fig. 1.

Fig.1 Schematic diagram of spectral-domain OCT system. CMOS: complementary metal oxide semiconductor; PC: personal computer

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The low-coherence light emitted from the broadband light source enters the Michelson interferometer through optoisolator. The function of optoisolator is to protect the light source from reflected light. The beam is divided into two parts by a 90/10 coupler via optical fiber and 90% of the beam is guided to the sample arm and focused on the sample after collimating mirror and galvanometer. The light in the reference arm is focused on the mirror after collimating mirror and lens. The high speed interferometer receives interference signal produced by the reflected light from the sample arm and the reference arm. Finally, the data is processed by computer using home-built software in Labview.

The axial resolution and transverse resolution of the spectral domain OCT is independent of each other. The axial resolution δ_z is decided by coherence length l_c, namely center wavelength and 3 dB bandwidth of the broadband source [ 38, 39]. In the SD-OCT system, we used a super luminescent diode (SLD) (Superlum) with a center wavelength of 835 nm. The full width half maximum (FWHM) of the SLD is 45 nm and the output power is 12 mW. According to these parameters, the axial resolution can be calculated as follows:

(1) $${\delta }_z=l_c={\displaystyle \frac{\mathrm{2}\ln \mathrm{2}}{\pi }}\cdot {\displaystyle \frac{{\lambda }_{\mathrm{0}}^{\mathrm{2}}}{\mathrm{\Delta }\lambda }}={\displaystyle \frac{\mathrm{2}\ln \mathrm{2}}{\pi }}\cdot {\displaystyle \frac{\mathrm{0.835}\times \mathrm{0.835}}{\mathrm{45}\times {\mathrm{10}}^{-\mathrm{3}}}}\mu m\approx \mathrm{6.84}\mu m,$$

where ${\lambda }_{\mathrm{0}}$ and $\mathrm{\Delta }\lambda$ are the center wavelength and bandwidth of the SLD, respectively. The output spectrum curve of SLD is shown in Fig. 2.

Fig.2 Output spectrum curve of the optical source

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The transverse resolution of the SD-OCT system depends on the objective lens that is to focus the light onto the sample according to Rayleigh criterion. The sample arm consists of a collimating mirror (f_c = 20 mm, OZ optics), scanning galvanometer (6215H, Cambridge Technology) and lens (f_obj = 30 mm, DHC). The numerical aperture (NA) of the optical fiber is 0.13. The transverse resolution can be calculated as

(2) $$\mathrm{\Delta }x={\displaystyle \frac{\mathrm{4}{\lambda }_{\mathrm{0}}}{\pi }}\cdot {\displaystyle \frac{f_{\mathrm{obj}}}{d}}={\displaystyle \frac{\mathrm{4}{\lambda }_{\mathrm{0}}}{\pi }}\cdot {\displaystyle \frac{f_{\mathrm{obj}}}{\mathrm{2}\mathrm{NA}\cdot f_c}}={\displaystyle \frac{4\times 0.835}{\pi }}\cdot {\displaystyle \frac{\mathrm{30}}{\mathrm{2}\times \mathrm{0.13}\times \mathrm{20}}\mu m}\approx \mathrm{6.13}\mu m,$$

where d is the diameter of the light spot on the lens of the sample arm.

High speed spectrometer plays an important role in SD-OCT system. The quality of the images depends on the scanning speed of the spectrometer. The high speed spectrometer consists of achromatic collimating lens (f_c = 60 mm, OZ optics), diffraction grating on the condition of Littrow with a center wavelength of 830 nm and line per mm of 1200 (Wasatch Photonics), near infrared achromatic lens (f = 150 mm, TECHSPEC®NearIRdoublets, Edmundoptics) and a 12-bitline-scan CMOS camera with 4096 pixels (Basler sprint, SPL4096-140K). The pixel size and line rate of the CMOS are 10 µm × 10 µm and 140 KHz, respectively. According to the grating equation:

(3) $$d(\sin {\theta }_{\mathrm{0}}+\sin \theta )=m\lambda ,$$

diffraction angle at center wavelength can be calculated by Eq. (3). In general, m is considered as 1.

$$\mathrm{2}d\sin {\theta }_{\mathrm{0}}=m{\lambda }_{\mathrm{0}}.$$

(4) $$\sin {\theta }_{\mathrm{0}}={\displaystyle \frac{\mathrm{1}\times \mathrm{830}\times {\mathrm{10}}^{-\mathrm{6}}}{\mathrm{2}\times (\mathrm{1}/\mathrm{1200})}}=\mathrm{0.498},{\theta }_{\mathrm{0}}\approx \mathrm{29.87}°,$$

where λ₀ and d are center wavelength and line number of the grating, respectively. Spectral resolution of the spectrometer can be obtained based on above parameters. At first, spectral resolution of the grating follows:

(5) $$\delta {\lambda }_g={\displaystyle \frac{{\lambda }_{\mathrm{0}}}{mN}}={\displaystyle \frac{{\lambda }_{\mathrm{0}}}{m(D/\cos {\theta }_{\mathrm{0}}d)}}={\displaystyle \frac{{\lambda }_{\mathrm{0}}\cos {\theta }_{\mathrm{0}}d}{mD}}={\displaystyle \frac{\mathrm{835}\times (\mathrm{1}/\mathrm{1200})\times \cos \mathrm{29.87}}{\mathrm{1}\times \mathrm{2}\times \mathrm{0.13}\times \mathrm{60}}}\mathrm{nm}\approx \mathrm{0.039}\mathrm{nm}.$$

Here, N is line number in the spot through collimating mirror on the grating. D is the diameter of the spot, $D=\mathrm{2}\mathrm{NA}\cdot f_c.$

Moreover, spectral resolution decided by pixel size p of line-scan CMOS:

(6) $$\delta {\lambda }_c={\displaystyle \frac{p}{fm/(d\cos {\theta }_{\mathrm{0}})}}={\displaystyle \frac{\mathrm{10}\times {\mathrm{10}}^{\mathrm{3}}\times (\mathrm{1}/\mathrm{1200})\times \cos \mathrm{29.87}}{\mathrm{1}\times \mathrm{150}}}\mathrm{nm}\approx \mathrm{0.04818}\mathrm{nm}.$$

Therefore, the spectral resolution of the high speed spectrometer in our system is 0.04818 nm by comparing Eq. (5) with Eq. (6). According to sampling theorem, theoretically the imaging depth of this SD-OCT system [ 4, 5, 40] in air is

(7) $$Z_{\max }={\displaystyle \frac{{\lambda }_{\mathrm{0}}^{\mathrm{2}}}{\mathrm{4}\delta \lambda }}={\displaystyle \frac{\mathrm{835}\times \mathrm{835}}{\mathrm{4}\times \mathrm{0.04818}}}\mathrm{nm}\approx \mathrm{3.62}\times {\mathrm{10}}^{\mathrm{6}}\mathrm{nm}=\mathrm{3.62}\mathrm{mm}.$$

3 Results and discussion

In the experiment, mice are bought from medical laboratory animal center in Guangdong province. The requirements are female BALB/c-nu mice of more than 8 weeks old and>20 g to be fit for dorsal skin window chamber. Dorsal skin window chamber model (the diameter is 12 mm) is prepared when the mouse is in the state of anesthesia, injected with 100 μL phenobarbital solution [ 41].Complicated main vessels can be observed with naked eye. Dorsal skin window chamber model is shown as in Fig. 3. To make sure that the mouse model is in focus, we adjusted the height of the sample stage and obtained interference images simultaneously. At the same time, there is a proper angle between the sample and the horizontal plane to get suitable intensity from the reflected light.

Fig.3 Dorsal skin window chamber model

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The skin of human and higher animals is composed of cuticle, dermis and subcutaneous tissue. Dermis is located deep in the cuticle, the outermost layer of human and higher animals. There are abundant blood vessels, lymphatic vessels, nerves, sweat glands and hair follicles in the subcutaneous tissue (also called subcutaneous fat tissue). Capillaries produced by blood vessels cannot be seen with naked eyes due to their small size (6-9 µm). In view of calculated resolution, capillaries at these sizes can be obtained by this high-resolution SD-OCT system.

Figure 4 shows one set of representative OCT image and reconstructed 3D image of the skin of the mouse back. The real-time cross-sectional image of blood vessels is shown in Fig. 4(a), indicating the size of blood vessels intuitively. Two blood vessels are marked with circles, and their diameters are 150 and 38 μm respectively. The imaging depth is about 610 μm. Reconstructed 3D image of the same position by Amira is shown in Fig. 4(b). Figures 4(c) and 4(d) show vascular distribution at two different depths. Vascular distributions at different layers of the mouse skin are different obviously. The size of the area in Fig. 4(a) (marked in red circle) corresponds to the diameter of the vessels in Figs. 4(b), 4(c) and 4(d).That is to say, the larger the size of the area forms, the bigger the diameter of the blood vessels is. Each image size is 1.3 mm ×1.3 mm.

Fig.4 Vascular distribution images of dorsal skin window chamber mouse model. (a) Real-time OCT image; (b) 3D image of the vascular distribution; (c) and (d) blood vessel distributions at two different depths, which are 80 and 320 μm, respectively. Scale bar in (a) is 300 μm in longitudinal direction, while the scale bar in transverse direction is 100 μm. Scale bars in (b), (c) and (d) are 100 μm

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4 Conclusion

A SD-OCT system has been developed to image the skin of the mouse in vivo. According to obtained OCT image and 3D image, complicated distribution of blood vessels exists in the deeper layer of the mouse skin. To our knowledge, this system can be further applied to observe vascular distribution of other tissues in vivo. The changes of vascular distribution of the mouse in different experiments can be visualized for a certain period of time. This system will provide a useful method for tumor research, e.g., to observe tumor growth or drug delivery in tumor therapy.

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Acknowledgements

This work was partially supported by the National Basic Research Program of China (No. 2015CB352005), the National Natural Science Foundation of China (Grant Nos. 61378091, 11204226, 11404285 and 61475143), Shenzhen Science and Technology Development Project (Nos. ZDSY20120612094247920, JCYJ20130329114905559 and CXB201104220021A), Zhejiang Provincial Natural Science Foundation of China (No. LY14F050007).

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