Laser speckle projection tomography - OSA Publishing

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OPTICS LETTERS / Vol. 38, No. 15 / August 1, 2013

Laser speckle projection tomography Guanping Feng, Junbo Chen, Xuanlong Lu, Dingan Han, and Yaguang Zeng* Department of Photoelectric Technology, Foshan University, Guangdong 528000, China *Corresponding author: [email protected] Received March 21, 2013; revised June 14, 2013; accepted June 18, 2013; posted June 26, 2013 (Doc. ID 187370); published July 19, 2013 We propose a laser speckle projection tomography (LSPT) method to obtain a three-dimensional (3D) flowing image. The method combines the advantages of optical projection tomography and laser speckle imaging to reconstruct the visualization of 3D flowing structure. With LSPT, the flowing signal is extracted by laser speckle contrast method and the 3D flowing image is reconstructed by the filtered back-projection algorithm. A phantom experiment is performed to demonstrate that LSPT is able to obtain 3D flowing structure, influenced by concentration and the flow speed. © 2013 Optical Society of America OCIS codes: (110.6955) Tomographic imaging; (170.3010) Image reconstruction techniques; (170.6960) Tomography. http://dx.doi.org/10.1364/OL.38.002654

Laser speckle imaging is a full-field optical technique based on the dynamic light scattering methodology. It has been used to measure local dynamic properties in scattering media both at high temporal and spatial resolution and has attracted extensive attention in biomedical applications [1–4]. In the last decade, as an economical three-dimensional (3D) optical imaging method, optical projection tomography (OPT) has been developed [5]. This technique is based on the acquisition of a sequence of optical transmission images or fluorescence images of the sample at several orientations [6,7]. It is capable to provide the internal visualization of gene expression pattern and organ structures in tissue [8–10]. The new development of OPT is used to reconstruct 3D flowing vascular structure images, which is called flow-OPT [11]. This method combines the OPT technique and digital motion analysis, where the flow signal is extracted through motion-analysis procedures. However, the method needs to look directly at the flow of blood cells with an enlarging lens. In this Letter, we propose a laser speckle projection tomography (LSPT) method to develop the new flow-OPT technique. Different from flow-OPT, the flow signal of the LSPT image is extracted by computing the speckle contrast at every acquired position. Compared with digital motion analysis, this method is more sensitive to the motion particle and has deeper detecting depth in highly scattering tissue. A similar concept can also be found to study and simulate compressible flows [2]. When the laser hits small particles, one observes a time-dependent fluctuation in the scattering intensity. This fluctuation is due to the fact that the small molecules in solutions are undergoing Brownian motion, and so the distance between the scatters in the solution is constantly changing with time. This dynamic scattered light then undergoes either constructive or destructive interference. This process generates dynamic laser speckle. According to the laser speckle theory, the temporal speckle contrast is constructed by calculating the speckle temporal contrast of each image pixel in the time sequence, which can be written as [12]

K
x; y 

q



























































PN 1 2 f I
n − hI i g x;y x;y i1 N−1 hI x;y i

;

(1)

0146-9592/13/152654-03$15.00/0

where I x;y
n and hI x;y i are the camera counts at pixel
x; y in the nth laser speckle image and mean value over the N laser speckle images, respectively. Equation (1) can be viewed such that an absolute value of the dynamic scattering light divides the average light intensity hI x;y i. So K
x; y shows the dynamic laser speckle density. Under our experimental condition, it represents the projection of the dynamic laser speckle density along the Z axial direction of the sample, i.e., the Radon transformation of laser speckle contrast. 3D flow image can be reconstructed by the well-known filtered back-projection algorithm [13] K
x; z 

Z πZ 0

∞ −∞

P
l; θ  c
l

× δ
x cos θ  z sin θ − ldldθ;

(2)

where the asterisk denotes convolution, θ is the rotating angle, P
l; θ is the projection of laser speckle contrast K
x; y at the view angle of θ, and c
l is the spatial domain representation of an appropriate filter necessary for back-projection algorithms. The experimental setup of the LSPT system is shown in Fig. 1(a). The light source is a fiber-coupled diode laser (λ  650 nm, 85 mw). The output of the laser source becomes four light beams equally by a 1 × 4 fiber splitter. Light beams at different angles are first diffused by a diffusion screen and illuminate the flow phantom. The symmetrical lighting structure can insure the illumined field as homogeneous as possible, and only scattering light

Fig. 1. (a) Schematic of the LSPT used in our experiments. (b) The flow phantom with two polyethene tubes. © 2013 Optical Society of America

August 1, 2013 / Vol. 38, No. 15 / OPTICS LETTERS

from the phantom is collected to an 8 bit CMOS camera (A504k, Basler) with 1280 × 1024 pixels by a telecentric optical system. The exposure time of the camera was set to 5 ms and the frame rate of the camera is set to 30 fps. The whole LSPT system was fixed on a vibrationisolator optical platform. The LSPT phantom is shown in Fig. 1(b). A glass tube with the inner diameter of 7 mm is filled with a cylinder made of 2% agarose gel to simulate the background tissue. Two polyethene tubes with an inner diameter of 1 mm in Figs. 2 and 3 (0.5 mm in Fig. 4) are embedded in the agarose gel cylinder to simulate the static scattering phantom or the flow phantom. The sample is rotated through 360 deg with a step size of 1.8 deg by a stepper motor with 200 projection contrast images. Thirty frame images are acquired at each rotating position to compute the contrast value. Figures 2(a) and 2(c) show a raw laser speckle image and transmission images of the sample at one rotating position, where two tubes can be distinguished. Figure 2(b) shows that the contrast image by computing Fig. 2(a) with Eq. (1). Figure 2(d) shows the flow-OPT image based on the digital motion analysis method to process Fig. 2(c), which is used in [11]. It is shown that the laser speckle method can resolve the flowing structure but the digital motion analysis method can not resolve it. The main reason is that when the white light illumines the flow phantom, as the telocentric system has much depth of field, every pixel of the camera receives the light of hundreds of scatters. The fluctuation of transmission light intensity is very weak. However, the laser speckle is produced by the mutual interference of whole scatters, which is more sensitivity to density fluctuation. One typical reconstructed spatial distribution of OPT and LSPT within the scanned cross section is shown in Figs. 3(a) and 3(b), where tube A is made of agarose gel (2%) and intralipid solution (0.5%) to simulate the static scattering structure. Tube B is used to simulate the flowing vascular structure. A single-channel syringe pump (JZB-1800, JYM) provides 1% Intralipid solution flow velocity of 2 mm∕s in the tube B. This image agrees well with the dimensions of the tubes and the agarose gel cylinder. The edge of the glass tube, tube A and tube B can be clearly reconstructed in Fig. 3(a). The main reason is that the reflection coefficient of the glass and polyethene are higher than those of the flow fluid and agarose gel. Figure 3(b) presents the LSPT image of the phantom.

Fig. 2. (a) Raw image illuminated by laser. (b) The contrast image of (a). (c) The transmission image illuminated by white light. (d) The image based on the digital motion analysis of (c).

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Fig. 3. (a) Reconstructed cross-sectional OPT image. (b) Reconstructed cross-sectional LSPT image. Curves (c) and (d) along the horizontal dashed line indicate (a) and (b). (e) 3D OPT image. (f) 3D LSPT image.

Here, the simulating vascular had been highlighted and the stationary objects had been greatly suppressed. Figures 3(c) and 3(d) show the intensity profiles marked in Figs. 3(a) and 3(b) along the horizontal dashed lines, respectively. Figure 3(c) shows the flow signal is completely covered by the background signal. In Fig. 3(d), the flow signal of tube B is stand out obviously, and the SNR can be computed as 47.19 dB. To evaluate the spatial resolution for the system proposed, we cut across the profile with the half-amplitude and quarter-amplitude lines at points A1–2 and B1–2 , respectively. The value of jB1 B2 − A1 A2 j is about 45 μm. Thus, the spatial resolution of this system is better than 45 μm. The spatial resolution may be further improved by more homogeneous structured illumination field, by increasing the number of translating and rotating steps, and by using a stable laser light source. Figures 3(e) and 3(f) show the 3D reconstructed OPT and LSPT image, respectively. In Fig. 3(f), only the flow phantom can been reconstructed successfully. The experimental results show that LSPT is capable of constructing 3D flow structure. However, scatters concentration and the flow speed directly affect the image quality. If scatters concentration change in the solution, the distance between the scatters varies as well. It directly influences the interference of the surrounding particles and the intensity of the dynamic scattered light. Figures 4(a) and 4(c) show an example of the influence of different concentrations, where the intralipid concentration of tubes C and D are 1% and 0.5% (flow speed 1 mm/s), respectively. The compared result shows the higher concentration the stronger image intensity. On

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OPTICS LETTERS / Vol. 38, No. 15 / August 1, 2013

by imaging two tubes embedded within agarose gel. The reconstructed results demonstrate a spatial imaging resolution of 45 μm. Our experiment also shows that the concentration and the flow speed can influence the tomographic image quality. The capability of LSPT displays a potentially useful application to image 3D circulation systems. This work is supported by the National Natural Science Foundation of China (Nos. 61008063 and 61275214) and the Science and Technology Plan Projects of Foshan city.

Fig. 4. (a) Different intralipid concentration LSPT image, tubes C and D with concentration 1% and 0.5%, respectively. (b) Different flow speed LSPT image, tubes E and F with velocity 3 mm∕s and 0.5 mm∕s, respectively. Curves (c) and (d) along the horizontal dashed line indicate (a) and (b).

the other hand, Figs. 4(b) and 4(d) show that the higher flow speed reduces the image quality, where the flow speed of tube E and F are 3 and 0.5 mm∕s (intralipid concentration 1.5%), respectively. The main reason is that the directional flow speed blurs the laser speckle image. We also must point out that the two facts are intrinsically a nonlinear process. Their specific physical relationship and some optimized reconstruction algorithms should be further studied in the future work. In summary, we have proposed the approach of LSPT. We have experimentally validated this approach

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