Research Papers: Sensing

Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy

[+] Author Affiliations
Ricky Hennessy

University of Texas at Austin, Biomedical Engineering, 107 W. Dean Keeton, Austin, Texas 78712, United States

Will Goth

University of Texas at Austin, Biomedical Engineering, 107 W. Dean Keeton, Austin, Texas 78712, United States

Manu Sharma

University of Texas at Austin, Biomedical Engineering, 107 W. Dean Keeton, Austin, Texas 78712, United States

Mia K. Markey

University of Texas at Austin, Biomedical Engineering, 107 W. Dean Keeton, Austin, Texas 78712, United States

University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States

James W. Tunnell

University of Texas at Austin, Biomedical Engineering, 107 W. Dean Keeton, Austin, Texas 78712, United States

J. Biomed. Opt. 19(10), 107002 (Oct 27, 2014). doi:10.1117/1.JBO.19.10.107002
History: Received July 28, 2014; Revised September 18, 2014; Accepted October 1, 2014
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Abstract.  The sampling depth of light for diffuse reflectance spectroscopy is analyzed both experimentally and computationally. A Monte Carlo (MC) model was used to investigate the effect of optical properties and probe geometry on sampling depth. MC model estimates of sampling depth show an excellent agreement with experimental measurements over a wide range of optical properties and probe geometries. The MC data are used to define a mathematical expression for sampling depth that is expressed in terms of optical properties and probe geometry parameters.

Figures in this Article

Diffuse reflectance spectroscopy (DRS) can be used to noninvasively measure tissue optical properties.111 Typically, DRS uses a fiber to inject light into the tissue. The light undergoes scattering and absorption, and the reflected light is collected by a second fiber placed at a short distance, known as the source-detector separation (SDS), from the illumination fiber. The collected light contains quantitative information which can be extracted using an inverse model that relates the collected signal to tissue optical properties.1,2 Since the reflected light only contains information about the tissue that it passes through, accurate interpretation of the results requires knowledge of the penetration depth. The light penetration depth depends not only on the absorption and scattering properties of the tissue, but also on the geometry of the diffuse reflectance probe.12 Because of this, the depth sampling of a DRS probe can be tuned by adjusting the probe geometry, allowing for the design of application specific probes.13

Many studies have investigated the sampling depth in scattering media both experimentally and numerically.1216 Most of these studies rely on the diffusion approximation, which is not valid for short SDSs and highly absorbing media. Others investigated the sampling depth only for reflectance probes with specific geometries, such as single-fiber reflectance,16 overlapping illumination and collection areas,17,18 large SDSs (SDS>1/μs),19 and diffuse reflectance spectroscopy probes only at specific SDSs and fiber diameters.13,20 Backman and Gomes recently developed an empirical model to describe sampling depth for a DRS probe. This model is based on a previous study on the sampling depth of single-fiber spectroscopy probes and is only valid for DRS probes with fiber diameters of 200μm and an SDS of 250μm.13 A model that can accurately determine sampling depth for any given SDSs and tissue optical properties will allow the development of application specific probes where light sampling from a specific depth is necessary. Additionally, knowledge of the sampling depth can be used to determine wavelength-dependent differences in the sampling depth due to the difference in optical properties across wavelengths.

In this paper, we analyze the effect of probe geometry and optical properties on the sampling depth using both computational and experimental approaches. First, many Monte Carlo (MC) simulations are performed to determine the sampling depth for a range of optical properties and SDSs. Next, the MC results are validated using a set of phantom experiments. Finally, we develop an analytical expression that can be used to quickly determine the sampling depth for a given SDS, absorption coefficient, and reduced scattering coefficient.

Monte Carlo Model

This study adapts the MC model of light transport in layered tissue code developed by Wang et al.21 implemented in parallel on a GPU using NVIDIA’s compute unified device architecture by Alerstam et al.22,23 The MC model for modeling light transport is a stochastic method that simulates light transport in a scattering medium with the probabilities of scattering and absorption events determined by the user-specified optical properties of the medium and the geometry of the light source and measurement probe. Photons step sizes were selected from an exponential distribution that depended on the scattering coefficient, and scattering angles were determined by the scattering anisotropy (g) and the phase function. We used the Heyney–Greenstein phase function. Reflection and refraction due to index of refraction mismatch were calculated using the Fresnel equation and Snell’s law.

A two-layer model was used with reduced scattering in the bottom layer set to zero, and the absorption in the bottom layer was set to 1×1015cm1 so that photons reaching the bottom layer were terminated. Scattering anisotropy was held constant at 0.85. The sensitivity of photon path length and sampling depth to phase function and anisotropy (g) has been explored by Kanick et al. for single-fiber spectroscopy.16 They performed simulations with g=[0.8,0.9,0.95] and with both the Henyey–Greenstein phase function and the modified Henyey–Greenstein phase function. The data showed that path lengths and the sampling depth are independent of anisotropy. The phase function was found to have an observable effect on path length, but the mean sampling depth remained relatively unchanged.

The refractive tissue above the medium was set to 1.452 to match the refractive index of an optical fiber, and the refractive index of the medium was set to 1.33 to match the refractive index of water. The top layer absorption coefficient (μa) ranged from 0 to 30cm1 in 20 increments, the top layer reduced scattering coefficient (μs) ranged from 0 to 30cm1 in 20 increments, and the top layer thickness ranged from 0 to 3000μm in 250 increments. This gave a total of 100,000 separate MC simulations with each using 107 photons. The geometry for the simulations is shown in Fig. 1(a). Spatially resolved reflectance was calculated by convolving the impulse response using a Gaussian-shaped beam profile with radius R1, and the reflectance signal was calculated by summing the reflectance values centered at the SDS with a collection fiber radius of R2. For a given set of optical properties (μa and μs and probe geometry parameters (SDS, R1, R2, we plotted the percentage of photons that never reach depth Z0 versus Z0 [Fig. 1(b)]. If we model the curve in Fig. 1(b) as a sigmoid function, the greatest slope will occur at the depth that is reached by 50% of the photons, meaning that the measured reflectance is most sensitive to optical properties at that depth. Because of this, the sampling depth (Zs) of a probe for a given set of optical properties is defined as the depth reached by 50% of the photons.

Graphic Jump LocationF1 :

(a) A two-layer geometry was used for the Monte Carlo (MC) simulations. The bottom layer had an absorption coefficient of 1×1015cm1 and a scattering coefficient of zero so that photons reaching the bottom layer are terminated. Top layer thickness (Z0) ranged from 0 to 3000μm in 250 increments, the top layer absorption coefficient (μa) ranged from 0 to 30cm1 in 20 increments. Reflectance measurements were recorded out to 1 cm from the source. (b) A plot of showing the percentage of photons that never reach a depth of Z0 versus Z0 with SDS=300μm, R1=100μm, R2=100μm, μa=1.6cm1, and μs=16cm1. Sampling depth (ZS) is defined as the depth reached by 50% of the photons.

Empirical Measurements of Sampling Depth

To validate the computational results, 12 different phantoms were constructed in order to perform an experimental analysis of sampling depth for DRS of varying SDS. The phantoms were composed of 5-mL solutions of water, India ink (Salis International, Golden, Colorado), and scattering microbeads (Polyscienes, Warrington, Pennsylvania), which spanned absorption and scattering values across a range consistent with those normally found in human tissue. Mie theory was used to determine the scattering properties of the 0.99-μm diameter beads. Mix ratios of water and microbeads were determined so that three different scattering spectra from 11 to 25cm1 were achieved at the reference wavelength of 630 nm. Each of these mix ratios was prepared with four different concentrations of India ink, so that the absorption coefficient for the samples ranged from 0 to 23cm1, resulting in 12 total phantoms with different scattering and absorption properties, as seen in Table 1.

Table Grahic Jump Location
Table 1Optical properties of phantoms.

Each of these 12 phantoms was placed into a blackened beaker. Reflection measurements were taken while varying the distance between the probe and the bottom of the beaker from 0 to 3 mm in 50-μm increments. Reflectance spectra were collected at wavelengths from 500 to 700 nm and at SDSs of 370, 740, and 1110μm. Using the known wavelength dependence of scattering and absorption, μa and μs were calculated at each wavelength, and for each set of μa and μs a plot of P versus Z0 was created. These plots were then used to calculate Zs for each set of optical properties.

Mathematical Model of Sampling Depth

The sampling depth for a DRS probe is dependent on the optical properties (μa and μs and probe geometry parameters (SDS, R1, and R2). The sampling depth data from the MC simulations were accurately described by the equation Display Formula

ZS=a1+a2(1(1+a3μa)a4)(1(1+a3μs)a4).(1)

Equation (1) is an empirical expression that accurately describes the MC sampling depth data. This expression was found by trying thousands of candidate functions with the help of TableCurve 2-D (Automated Curve Fitting and Equation Discovery Software, Systat, 2002).

Equation (1) has four free parameters (a1, a2, a3, a4) whose values must be determined by fitting the MC data. This was accomplished by minimizing the residual between the MC sampling depth results and the sampling depths calculated using Eq. (1) and a Levenberg–Marquardt algorithm scripted in MATLAB®. The dependence of the free parameters on SDS was then determined so that Eq. (1) could be used to determine sampling depth for a given probe geometry and set of optical properties. SDS and ZS are in units of cm and μa and μs are in units of cm1.

Experimental Validation

The sampling depth results from the phantom experiments were used to validate the computational sampling depth results at SDSs of 370, 740, and 1100μm with optical properties in the range μa[025]cm1, and μs[030]cm1. Figure 2 shows an overlay of the computational (transparent mesh) and the experimental (colored surface) results and provides a visual illustration of the good agreement between experimental and computational results. The root-mean-squared percent error for an SDS of 370μm was 1.71%, for an SDS of 710μm it was 1.27%, and for 1100μm it was 1.24%. This agreement indicates that the MC model accurately returns sampling depth. The ripples in the data indicate that the agreement between the phantom measurements and the MC data is wavelength dependent. We believe this is due to the use of the inverse power law for the wavelength dependent description of scattering in the phantoms containing polystyrene microbeads, when in reality, the true scattering values of the phantoms as a function of wavelength contain “humps” in the curve due to the relatively narrow size distribution of the microspheres.

Graphic Jump LocationF2 :

Plots of ZS predicted by Monte Carlo modeling versus the experimental values for ZS at source-detector separations (SDS) of (a) 370, (b) 740, and (c) 1100μm. An overlay of two-dimensional (2-D) surfaces showing the relationship between scattering and absorption on sampling depth for both Monte Carlo and experimental results. These plots provide a visual illustration of the agreement between the computational (transparent mesh) and experimental (colored surface) results for source-detector separations of (d) 370, (e) 740 and (f) 1100μm.

Analytical Model of Sampling Depth

The analytical model of sampling depth shown in Eq. (1) was fit to MC data for a probe with fiber diameters of 50, 100, 200, and 400μm. For each fiber diameter, SDS ranges from adjacent fibers to 1000μm, μs ranges from 3 to 40cm1, and μa ranges from 0 to 40cm1. Fitting parameters a1 and a2 were found to have a linear relationship with SDS, a3 was found to have a quadratic relationship with SDS, and a4 is a constant. Table 2 gives the fitting parameters for the four different common fiber diameters as a function of SDS. Table 2 allows the fitting parameters in Eq. (1) to be determined for a given fiber diameter and SDS so that Eq. (1) can be used to determine sampling depth for a specific probe geometry.

Table Grahic Jump Location
Table 2Values for fitting parameters at various fiber diameters.

Figure 3 below shows the sampling depth predicted by the analytical model in Eq. (1) and Table 1 versus the MC sampling 3 depth. Model predictions were strongly correlated with the MC data with a mean residual error of 2.89%.

Graphic Jump LocationF3 :

Monte Carlo results for simulation of sampling depth versus sampling depth predictions from the analytical model for all four fiber diameters [Eq. (1)]. The line of unity is shown for comparative purposes. There is a 2.89% error between the Monte Carlo simulation results and the analytical model results.

Effect of Anisotropy and Phase Function on Sampling Depth

Because scattering anisotropy and the choice of a phase function can impact reflectance at short SDSs,24 a subset of MC simulations was performed to investigate the effect of anisotropy and phase function on sampling depth. The data showed no change in sampling depth for simulations of different anisotropy values (g=[0.80,0.85,0.90,0.95]) over a range of optical properties (μa[025]cm1, μs[030]cm1) and probe geometries (SDS[50800]μm), R[50400]μm. This is illustrated in Fig. 4(a), where sampling depth versus SDS is plotted for a probe with 50-μm diameter fibers with a reduced scattering coefficient of 10cm1 and an absorption coefficient of 10cm1 for four different anisotropy values. The mean percent error across all anisotropy values for all probe geometries and optical properties was 3.47%. Additionally, the data showed no change in sampling depth for simulations performed with the Heyney–Greenstein (HG) phase function or the modified Heyney–Greenstein (MHG) phase function. This is illustrated in Fig. 4(b), where sampling depth versus SDS is plotted for a probe with 50-μm diameter fibers with anisotropy values of 0.85, a reduced scattering coefficient of 10cm1 and an absorption coefficient of 10cm1 for both the HG and MHG phase functions. A change in g or the phase function did affect the reflectance values; however, there was no change in the sampling depth as it is defined in this study. These results agree with the findings by Kanick et al. for single-fiber reflectance spectroscopy16 that show sampling depth is unaffected by both the anisotropy value and the choice of phase function.

Graphic Jump LocationF4 :

(a) Sampling depth versus SDS for varying anisotropy values with μa=10cm1, μs=10cm1, and fiber diameter at 50μm. (b) Sampling depth versus SDS for both HG and MHG phase functions with μa=10cm1, μs=10cm1, g=0.85 and the fiber diameter at 50μm.

This study utilizes an MC model to investigate how the optical properties of a turbid media and the geometry of a DRS probe affect sampling depth. This MC model for sampling depth was experimentally validated and was shown to accurately predict sampling depth. We developed an analytical model where sampling depth is expressed in terms of optical properties and probe geometry.

The utility of the model prediction of sampling depth is shown in Fig. 5, which plots sampling depth versus fiber diameter for a probe geometry where the source and detector fibers are adjacent for multiple combinations of optical properties. Figure 5 was created using MC simulation and not the empirical model in Eq. (1). This type of probe geometry accurately models the commonly used 6-around-1 fiber orientation, where a center fiber is used for illumination and six collection fibers of the same size are placed around the illumination fiber. All three series have the same value for scattering (μs=10cm1), and series 1 represents a moderately absorbing tissue (μa=10cm1), series 2 represents a highly absorbing tissue (μa=20cm1), and series 3 represents a nonabsorbing tissue (μa=0cm1). As expected, the sampling depth decreases for increasing absorption. Importantly, a relatively small increase in sampling depth results from a large change in SDS. This is especially evident in the highly absorbing tissue. For example, in series 3 (μs=10cm1), μa=20cm1, doubling the fiber diameter from 500 to 1000μm only increases the sampling depth by 17% (from 240 to 270μm). This result indicates that the 6-around-1 orientation is best for interrogating shallow depths and that it may not be possible to substantially increase sampling depth by increasing the fiber diameter.

Graphic Jump LocationF5 :

Mathematical model estimates of sampling depth for adjacent fibers for measurement of mediums containing different optical properties combinations: series 1 (μa=10cm1), μs=10cm1, series 2 (μa=0cm1), μs=10cm1, series 3 (μa=20cm1), μs=10cm1. These data were created using MC simulation and not the empirical model in Eq. (1).

The models developed in this study can also be used to provide an estimate of wavelength-dependent difference in optically sampled tissue volumes, which occurs when optical properties change as a function of wavelength. Figure 6 shows sampling depth as a function of wavelength for a sample containing 1mg/ml of fully oxygenated hemoglobin at three different SDSs. The reduced scattering coefficient is 20cm1 across all wavelengths. The models can also be used to explain discrepancies between measurements of tissue with different probe geometries. The main utility of the proposed model is that it can be used to aid the design of application specific DRS probes. For example, to design a probe that measures the properties of the epidermis, one may desire a sampling depth equal to or less than the epidermal thickness (70μm25) to ensure that most sampled photons only interact with the epidermis and not the dermis. As shown in Fig. 5, achieving a sampling depth of less than 70μm would require a 6-around-1 fiber orientation with fiber diameters of 50μm or less.

Graphic Jump LocationF6 :

Sampling depth versus wavelength for a sampling containing 1mg/ml of fully oxygenated hemoglobin at source detector separations of 250, 500, and 1000μm. Reduced scattering is 201 across all wavelengths.

This study uses an MC model of DRS to investigate the effect of optical properties and probe geometry on the sampling depth of photons collected by a DRS probe. The MC model of sampling depth was experimentally validated and shown to accurately predict sampling depth. An analytical model of sampling depth was developed and is valid for a DRS probe with fiber diameters of 200μm and for a wide range of SDSs (200 to 1000μm), absorption coefficients (0 to 40cm1), and reduced scattering coefficients (0 to 40cm1). The model of sampling depth indicates that for adjacent fibers in the 6-around-1 orientation, the sampling depth cannot be significantly increased by increasing the fiber diameters. This result suggests that deeper sampling depth can only be accomplished by increasing the gap between the source and collection fibers. Future work will involve the application of the sampling depth model to aid in the design of application specific probes that will be used to interrogate the optical properties of specific layers to tissue such as the epidermis and dermis.

Hennessy  R. et al., “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt.. 18, (3 ), 037003  (2013). 1083-3668 CrossRef
Sharma  M. et al., “Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy,” Biomed. Opt. Express. 5, (1 ), 40 –53 (2014). 2156-7085 CrossRef
Ge  Z., Schomacker  K. T., Nishioka  N. S., “Identification of colonic dysplasia and neoplasia by diffuse reflectance spectroscopy and pattern recognition techniques,” Appl. Spectrosc.. 52, (6 ), 833 –839 (1998). 0003-7028 CrossRef
Koenig  F. et al., “Spectroscopic measurement of diffuse reflectance for enhanced detection of bladder carcinoma,” Urology. 51, (2 ), 342 –345 (1998). 0090-4295 CrossRef
Zonios  G. et al., “Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo,” Appl. Opt.. 38, (31 ), 6628 –6637 (1999). 0003-6935 CrossRef
Zonios  G., Bykowski  J., Kollias  N., “Skin melanin, hemoglobin, and light scattering properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy,” J. Invest. Dermatol.. 117, (6 ), 1452 –1457 (2001). 0022-202X CrossRef
Utzinger  U., Richards-Kortum  R., “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt.. 8, (1 ), 121 –147 (2003). 1083-3668 CrossRef
Marin  N. M. et al., “Diffuse reflectance patterns in cervical spectroscopy,” Gynecol. Oncol.. 99, (3 ), S116 –S120 (2005). 0090-8258 CrossRef
Murphy  B. W. et al., “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt.. 10, (6 ), 064020  (2005). 1083-3668 CrossRef
Rajaram  N. et al., “Pilot clinical study for quantitative spectral diagnosis of non-melanoma skin cancer,” Lasers Surg. Med.. 42, (10 ), 716 –727 (2010). 0196-8092 CrossRef
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Arimoto  H., Egawa  M., Yamada  Y., “Depth profile of diffuse reflectance near-infrared spectroscopy for measurement of water content in skin,” Skin Res. Technol.. 11, (1 ), 27 –35 (2005). 0909-752X CrossRef
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Ricky Hennessy is a NSF graduate research fellow in the Department of Biomedical Engineering at the University of Texas at Austin. He is advised by Drs. James W. Tunnell and Mia K. Markey. He earned a BS and MS in biomedical engineering from California Polytechnic State University in San Luis Obispo. He will be graduating with his PhD in February 2015.

Will Goth is a graduate student in the Department of Biomedical Engineering at the University of Texas at Austin. He is currently advised by Dr. James W. Tunnell in the Bio-Photonics Lab and Dr. Michael S. Sacks in the Center for Cardivascular Simulation, and is a member of SPIE.

Manu Sharma: Biography is not available.

Mia K. Markey is a full professor of biomedical engineering at the University of Texas at Austin and an adjunct associate professor of imaging physics at the University of Texas MD Anderson Cancer Center. A 1994 graduate of the Illinois Mathematics and Science Academy, she received her BS degree in computational biology (1998) from Carnegie Mellon University and her PhD degree in biomedical engineering (2002), along with a certificate in bioinformatics, from Duke University.

James W. Tunnell is an associate professor of biomedical engineering at the University of Texas at Austin. He earned a BS in electrical engineering from the University of Texas and a PhD in bioengineering from Rice University. He was awarded a National Research Service Award from the NIH to fund his postdoctoral fellowship at the MIT from 2003 to 2005. He joined the faculty of the University of Texas in the fall of 2005.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation

Ricky Hennessy ; Will Goth ; Manu Sharma ; Mia K. Markey and James W. Tunnell
"Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy", J. Biomed. Opt. 19(10), 107002 (Oct 27, 2014). ; http://dx.doi.org/10.1117/1.JBO.19.10.107002


Figures

Graphic Jump LocationF1 :

(a) A two-layer geometry was used for the Monte Carlo (MC) simulations. The bottom layer had an absorption coefficient of 1×1015cm1 and a scattering coefficient of zero so that photons reaching the bottom layer are terminated. Top layer thickness (Z0) ranged from 0 to 3000μm in 250 increments, the top layer absorption coefficient (μa) ranged from 0 to 30cm1 in 20 increments. Reflectance measurements were recorded out to 1 cm from the source. (b) A plot of showing the percentage of photons that never reach a depth of Z0 versus Z0 with SDS=300μm, R1=100μm, R2=100μm, μa=1.6cm1, and μs=16cm1. Sampling depth (ZS) is defined as the depth reached by 50% of the photons.

Graphic Jump LocationF2 :

Plots of ZS predicted by Monte Carlo modeling versus the experimental values for ZS at source-detector separations (SDS) of (a) 370, (b) 740, and (c) 1100μm. An overlay of two-dimensional (2-D) surfaces showing the relationship between scattering and absorption on sampling depth for both Monte Carlo and experimental results. These plots provide a visual illustration of the agreement between the computational (transparent mesh) and experimental (colored surface) results for source-detector separations of (d) 370, (e) 740 and (f) 1100μm.

Graphic Jump LocationF3 :

Monte Carlo results for simulation of sampling depth versus sampling depth predictions from the analytical model for all four fiber diameters [Eq. (1)]. The line of unity is shown for comparative purposes. There is a 2.89% error between the Monte Carlo simulation results and the analytical model results.

Graphic Jump LocationF4 :

(a) Sampling depth versus SDS for varying anisotropy values with μa=10cm1, μs=10cm1, and fiber diameter at 50μm. (b) Sampling depth versus SDS for both HG and MHG phase functions with μa=10cm1, μs=10cm1, g=0.85 and the fiber diameter at 50μm.

Graphic Jump LocationF5 :

Mathematical model estimates of sampling depth for adjacent fibers for measurement of mediums containing different optical properties combinations: series 1 (μa=10cm1), μs=10cm1, series 2 (μa=0cm1), μs=10cm1, series 3 (μa=20cm1), μs=10cm1. These data were created using MC simulation and not the empirical model in Eq. (1).

Graphic Jump LocationF6 :

Sampling depth versus wavelength for a sampling containing 1mg/ml of fully oxygenated hemoglobin at source detector separations of 250, 500, and 1000μm. Reduced scattering is 201 across all wavelengths.

Tables

Table Grahic Jump Location
Table 1Optical properties of phantoms.
Table Grahic Jump Location
Table 2Values for fitting parameters at various fiber diameters.

References

Hennessy  R. et al., “Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy,” J. Biomed. Opt.. 18, (3 ), 037003  (2013). 1083-3668 CrossRef
Sharma  M. et al., “Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy,” Biomed. Opt. Express. 5, (1 ), 40 –53 (2014). 2156-7085 CrossRef
Ge  Z., Schomacker  K. T., Nishioka  N. S., “Identification of colonic dysplasia and neoplasia by diffuse reflectance spectroscopy and pattern recognition techniques,” Appl. Spectrosc.. 52, (6 ), 833 –839 (1998). 0003-7028 CrossRef
Koenig  F. et al., “Spectroscopic measurement of diffuse reflectance for enhanced detection of bladder carcinoma,” Urology. 51, (2 ), 342 –345 (1998). 0090-4295 CrossRef
Zonios  G. et al., “Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo,” Appl. Opt.. 38, (31 ), 6628 –6637 (1999). 0003-6935 CrossRef
Zonios  G., Bykowski  J., Kollias  N., “Skin melanin, hemoglobin, and light scattering properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy,” J. Invest. Dermatol.. 117, (6 ), 1452 –1457 (2001). 0022-202X CrossRef
Utzinger  U., Richards-Kortum  R., “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt.. 8, (1 ), 121 –147 (2003). 1083-3668 CrossRef
Marin  N. M. et al., “Diffuse reflectance patterns in cervical spectroscopy,” Gynecol. Oncol.. 99, (3 ), S116 –S120 (2005). 0090-8258 CrossRef
Murphy  B. W. et al., “Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy,” J. Biomed. Opt.. 10, (6 ), 064020  (2005). 1083-3668 CrossRef
Rajaram  N. et al., “Pilot clinical study for quantitative spectral diagnosis of non-melanoma skin cancer,” Lasers Surg. Med.. 42, (10 ), 716 –727 (2010). 0196-8092 CrossRef
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