Download - Non-Invasive Blood Flow Measurem ents Using … · Non-Invasive Blood Flow Measurem ents Using Ultrasound Modulated Diffused Light N. Racheli a, A. Ron a, Y. Metzger a, I. Breskin

Non-Invasive Blood Flow Measurements Using Ultrasound Modulated Diffused Light

N. Rachelia, A. Rona, Y. Metzgera, I. Breskina, G. Endenb, M. Balberga, R. Shechtera1

a Ornim Medical Ltd, Israel

b Biomedical Engineering Department, Ben- Gurion University, Israel

ABSTRACT Adequate capillary blood flow is a critical parameter for tissue vitality. We present a novel non-invasive method for measuring blood flow based on the acousto-optic effect, using ultrasound modulated diffused light. The benefits of the presented method are: deep tissue sampling (> 1cm), continuous real time measurement, simplicity of apparatus and ease of operation. We demonstrate the ability of the method to measure flow of scattering fluid using a calibrated flow phantom model. Fluid flow was generated by a calibrated syringe pump and the phantom’s sampled volume contained millimeter size flow channels. Results demonstrate linear dependence of flow as measured by the presented technique (CFI) to actual flow values with R2=0.91 in the range of 0 to 2 ml/min, and a linear correlation to simultaneous readings of a laser Doppler probe from the same phantom. This data demonstrates that CFI readings provide a non-invasive platform form measuring tissue microcirculatory blood flow. Keyword List: Ultrasound Modulated Light; Capillary Blood Flow; Phantom; Near Infrared; Acousto Optic Effect;

1. Introduction Measuring capillary blood flow is critical for determining tissue vitality (1). Several optical methods for measuring flow already exist. All these methods utilize Near Infrared (NIR) light due to its low absorption in tissue which allows a large penetration depth of up to several centimeters (2). The two mostly used methods are Laser Doppler (LD) and Diffuse Correlation Spectroscopy (DCS). Both of these methods are based on detecting a change in the phase or frequency of a coherent laser beam that irradiates the tissue. In DCS, the tissue is irradiated with a laser beam and the resulting speckle pattern is detected using photon counting detectors (3).Flow is extracted by measuring the decorrelation time of the detected light, and fitting it to a theoretical model. In DCS, the measured depth is roughly proportional to the source-detector separation. In LD based methods, a laser beam is directed onto the tissue and is sometimes combined with a reference beam to obtain the Doppler shift caused by the flow (4). The drawbacks of this method are: high sensitivity to movements, it is limited to shallow (few millimeters) flows and gives only relative measurements. In order to obtain flow measurements at deeper tissue depth, the LD probe is inserted into the tissue. In this work a novel non-invasive method for measuring blood flow based on the acousto-optic effect is presented. For this method, the sampled tissue is illuminated with a highly coherent light source. A slowly travelling ultrasound (US) beam modulates the light through the acousto-optic effect. The light exiting the tissue at a certain distance produces a speckle pattern which is detected with a single photo-detector. The detected light has a spectral component at the US frequency. Blood flow within the sampled volume affects the photons’ temporal correlation and therefore the amplitude of the spectral component at the US frequency decreases as flow increases while the spectral width around the ultrasound frequency broadens.

1Correspondence to R. Shechter, Ph.D.: E-mail [email protected]

Photons Plus Ultrasound: Imaging and Sensing 2012, edited by Alexander A. Oraevsky, Lihong V. Wang,Proc. of SPIE Vol. 8223, 82232A · © 2012 SPIE · CCC code: 1605-7422/12/$18 · doi: 10.1117/12.906342

Proc. of SPIE Vol. 8223 82232A-1

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2. Materials and methods Experiments were performed on a tissue-mimicking phantom model in order to demonstrate the detection of changes in flow using the proposed method. A tissue mimicking phantom that encapsulates millimeter size flow channels was fabricated and the flow rate of scattering fluid was varied with a syringe pump. The experimental setup used for these experiments is explained in detail in the following sections.

2.1. Tissue-mimicking Phantom structure A phantom model that mimics blood flow in the tissue was designed. The phantom is made of UltraFlex (Douglas & Sturgess Inc) which is a synthetic polymer matrix soaked with oil. TiO2 particles (0.1% by weight) were added as light scattering agents. The optical and acoustic properties of the phantom are similar to those of tissue as listed in Table 1.

Table 1- optical and acoustic properties of the phantom and the tissue (2;11) Property Tissue – muscle/brain Phantom Light Effective decay coefficient 2.17/2.12 cm-1 2.2 ±0.2cm-1 Sound Velocity 1.5 ·105 cm/s 1.43 ·105 cm/s Acoustic impedance 150-170 Kg/cm2s 149 Kg/cm2s

Phantom preparation TiO2 powder was mixed with mineral oil and the suspension was inserted into an ultrasonic bath to disintegrate aggregates. UltraFlex was heated to 200oC and stirred with the TiO2 suspension using a magnetic stirrer. The mixture was then poured into a special rectangular mold with 20 parallel fibers (outer diameter of 1mm) serving as a temporary support structure for the channels. After solidification of the phantom, the fibers were pulled out to create empty channels (Figure 3).

Figure 3 – a) A 3D scheme of the phantom and the measuring probe. The optic fibers and US transducer are within the probe. b) the Phantom cross section.

Phantom assembly The channels are connected to silicon tubes (each channel separately) while each row of channels is connected to a separate valve, as seen in Figure 4. Using these valves flow to each row can be controlled (turned on/off) separately.

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2.5. Experimental procedure Experiments were carried out with different flow rates. In each session a chosen flow rate was set and fluid flow was kept constant for 2 minutes. Six experiments were performed to test the sensitivity to fluid flow in deeper channels. In the first experiment all channels were activated. In the second experiment, the shallowest row of channels (4.5mm depth) was activated and in the third experiment the central row of channels (8.5mm depth) was activated.

3. Data analysis and results

3.1. Extracting the Flow Index from the UTL curve As a first step, the UTL curve (Figure 6) is normalized to the average light intensity (DC value) to account for the effect of the light intensity on the amplitude of the cross correlation. Next, the Flow Index (CFI) is calculated from an interest range k to k+N of the normalized UTL curved:

( ) = ∗ ( ) ∑ ( ) ( 2 )

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Figure 6 – An example of a UTL curve. The solid line over the dashed UTL curve is the interest range from k to k+N

The chosen interest range is the range over which the UTL curve is most sensitive to flow variations. To find this range, a linear regression between CFI and the real velocity rates was calculated for different interest ranges for the configuration with all channels active. The interest range length (N) was chosen to be 5mm.

3.2. Experimental results Experiments were performed with a sequence of ten flow rates in the range of 0 to 450 ml/min. The same sequence was repeated while different channels were activated, as mentioned in section 2.5. As expected, the amplitude of the UTL curves decreased as the flow rate through channel increased. In Figure 7 the average UTL curves for different flow rates are presented. The most sensitive interest range was in the range of 8 to 13 mm. This interest range was used for all future CFI calculations.

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Figure 7 – The UTL curves averaged over 2 min periods for three different flow rates.

After choosing the most sensitive interest range, CFI was calculated over 2 minutes for each flow rate. Abnormal UTL frames were rejected before calculating the CFI at time points where the DC light intensity exceeded 2.5 standard deviations from its mean value. As a robust estimator, the median value of CFI was calculated. The results for three configurations of active channels: flow in all channels, flow only in upper row of channels (row a) and flow only in central row of channels (row c) are plotted in Figure 8. The CFI linearly depends on the flow velocity. Different slopes were obtained for different depths since light decrease exponentially in the phantom and thus, the portion of photons scattered from moving particles relative to the overall light intensity decreases as the depth of flow increases.

Figure 8 - CFI vs. Flow Velocity for three channels configuration.

For evaluation of CFI, a comparison to Laser Doppler was performed. A Moor Instrument Laser Doppler DRT4, was used with a DP3b probe. The probe was positioned at the distance of approximately 1cm from the prototype probe. For each flow level generated by the syringe pump we obtained an averaged reading of the Laser Doppler system and Ornim’s system. The correlation between the laser Doppler’s flow index and CFI is presented in Figure 9. The correlation coefficient (r2) between CFI and Laser Doppler’s Flow Index was 0.94.

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Figure 9 - CFI vs. Laser Doppler’s Flow Index

In order to determine the effect of the concentration of scattering particles on CFI the scattering fluid described in section 2.2 was diluted with purified water to reduce the concentration of scattering particles from 20% to 13%. As CFI’s principles are similar to laser Doppler’s theory, CFI depends on both the scatterers’ velocity and concentration. For two trials, where all channels were activated, the velocity values were multiplied by the concentration, and the data was combined together, as presented in Figure 10. The regression of combined results shows good linearity with r2 of 0.87.

Figure 10 - CFI vs. Flow Velocity times concentration for the two scattering particles concentration.

4. Discussion This study demonstrates a very good linear correlation between the calculated Flow Index (CFI), defined by the UTL curves, and the actual flow rates in an artificial flow phantom. In the experimental setup presented herein, the velocity is linearly proportional to the flow rates. Therefore, this data also shows a linear correlation between CFI to velocity, very similar to that obtained with Laser Doppler, in addition to a good correlation to Laser Doppler readings from the same phantom. Accounting for variations in the concentration of the scattering elements, we show that the calculated CFI depends linearly on the product of concentration*velocity, as is the case for Laser Doppler measurements (10). This preliminary data shows that CFI depends on the flow of scattering particles through the measured volume, similar to the flux quantity measured by Laser Doppler.

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The described experimental setup demonstrates the linear dependence of CFI on flow of scattering particles through the channels, but is not used to calibrate the CFI dependence on flow in live tissue. This is due to the different decorrelation time of the phantom’s solid matrix and due to the different coupling efficiency between the probe and the phantom relative to those of tissue. For clinical application CFI depends on several parameters, including the US coupling efficiency between the transducer and the skin, the decorrelation time of the tissue and on the concentration of moving particles inside the measured volume.

5. Conclusions A novel method for continuously and non invasively measuring flow in deep tissue based on ultrasound modulated diffused light is presented. The data demonstrates a linear correlation of CFI readings to flow rates in channels that are deeper than 1cm in a synthetic phantom. The CFI readings are shown to depend on the concentration of scattering centers within the channels. A very good correlation to Laser Doppler readings from the same phantom is also demonstrated. CFI readings provide a non-invasive platform form measuring tissue microcirculatory blood flow.

6. Bibliography

1. Louis, Jean Vincent and De Backer, Daniel. “Microvascular dysfunction as a cause of organ dysfunction in severe sepsis,” Critical Care , pp. s9-s12 (2005). 2. Welch, Ashley J., Prahl, Scott A. and Wai-Fung, Cheong. “A Review of the Optical Properties of Biological Tissues,” IEEE Journal Of Quantum Electronics, VOL. 26. No. 12, December, pp. 2166-2185 (1990) 3. Cheung, Cecil, et al. “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol. 46, pp. 2053–2065 (2001) 4. Fredriksson I., Fors C. and Johansson J., "Laser Doppler Flowmetry – a Theoretical Framework," Department of Biomedical Engineering, Linköping University, www.imt.liu.se/bit/ldf/ldfmain.html (2007) 5. Guoqiang Yu, Turgut Durduran, Gwen Lech, Chao Zhou, Britton Chance, Emile R. Mohler III, and Arjun G. Yodh, J. “Time-dependent blood flow and oxygenation in human skeletal muscles measured with noninvasive near-infrared diffuse optical spectroscopie” Biomed. Opt. 10, 024027 (2005) 6. Yodh, Arjun G. and Boas, David A. “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A/ Vol. 14, No. 1/January, pp. 192-215 (1997) 7. Uzgiris, E., et al. “Ultrasonic tagging of light: Theory,” Applied Physical Sciences, Vol 95, pp. 14015-14019 (1998) 8. Guyton, Arthur C. and Hall, Jhon E. [Medical Physiology] Elsevier Health Sciences , pp. 150-158. (2005) 9. Schaller, B. “Physiology of cerebral venous blood flow: from experimental data in animals to normal function in humans,” Brain Research Reviews 46, pp. 243-260. (2004) 10. Nilsson, E. Gert., Tenland, Torsten and Ake, P. Oberg. “Evaluation of a Laser Doppler Flowmeter for Measurement of Tissue Blood Flow,” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, pp. 597-604. (1980) 11. Christensen, Douglas A. [Ultrasonic Bioinstrumentation] Wiley , (1988)

