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DEPTH SENSITIVITY IN DIFFUSE REFLECTANCE SPECTROSCOPY

USING FIBER-OPTIC SENSOR

Akshay Singhal

Project Guide : Dr. N. Sujatha

Bio-photonics Lab , Dept. of Applied Mechanics , Indian Institute of Technology Madras .

Introduction

Bio-optical techniques, particularly diffuse

reflectance spectroscopy (DRS) has applications

in quantitative optical study of tissues, etc.

Knowledge of depth profile of local blood region

(LBR) in skin tissue is important in diagnosis and

treatment, e.g. , in assessing degree of burn.

Present work inquires into depth-sensitivity of

DRS and into optimal source-detector fiber

separation (SDS) for maximum light collection

efficiency (preferential probing) from LBR. It

models an approach based on varying SDS for

estimating depth of LBR in skin.

Determining optimal SD separation

Skin tissue modeling

Simulation results of 4-layer model and

depth-estimation

Conclusion and Future Scope

References

4-layer skin

tissue model

epidermis (melanin)

local blood region (hemoglobin)

depth

source

fiberdetector fibers at

varying SDS

Fig. 1 : Problem statement schematized

perfused dermis

perfused dermis

Yunhan Luo’s [1] analysis for determining optimal

SDS for measuring chromophores in sandwiched

layer, based on Monte-Carlo simulation (MCS)

[2] of mean penetration-depth (mpd) profile of

reflectance, has been extended by varying the

depth of the sandwiched layer .

The SDS values shown in Fig. 2 and Fig. 3 are

matching , indicating optimality of that SDS for

preferential probing into sandwiched layer and

usefulness of mpd in depth-sensitivity of DRS.

Layer n μa [cm-1] μs [cm-1] gthickness

[cm]

1 1.37 4.5 140 0.873 varying

2 1.37 10 140 0.873 0.01

3 1.37 1.8 92 0.873 10

Table 1 : Optical parameters input to MCS

Fig. 2 : mpd v/s SDS

Fig. 3 : profile of fractional reflectance from chromophore region.

Skin’s multi-layered structure and local blood

region has been lumped into 4-layer model

(Fig. 1). Optical properties (μa, μs, g, n) (Fig. 4)

and structural configuration for each layer were

taken from sources [3] and using in-vivo

reflectance measurements of palm skin.

Blood volume % in perfused dermis was fixed at

2% after profile-matching task (Fig. 5 and Fig. 6).

Fig. 4 : Absorption coefficients (4-layer model)

Fig. 5 : Experimental setup for profile matching between simulated and

measured reflectance spectrum at 1.1 mm SDS

SDS = 1.1mm

palm

Fig. 6 : Profile-match between experimental and simulated reflectance

‘mpd profile v/s SDS’ of 4-layer model, for varying

depth of blood and λ=540 nm, is shown in Fig. 7.

It can be used for further optimal SDS analysis.

Spatially-resolved diffuse reflectance intensity

profile for varying depth of blood and λ=540 nm is

shown in (Fig. 8). It is clear that slope of this

curve (Fig. 9) is a depth-indicative feature.

Fig. 7 : mpd profile (4-layer model) at λ=540nm

Fig. 8 : diffuse reflectance v/s SDS at λ=540nm

Fig. 9 : depth-indicative feature (slope of Rd (r) v/s SDS)

In Fig. 9 , slope varies drastically for the case of

λ=540 nm and 580 nm because of strong

absorption of blood , while very marginally for the

case of 620 and 660 nm. This simulated slope

profile can be used for depth-estimation of LBR.

4-layer model of skin tissue with local blood

region, and its simulated mpd profile for varying

depth of LBR which is indicative of optimal SDS

for max collection efficiency from LBR, can be

used for future studies on depth-estimation of

local blood region in skin.

Depth-indicative feature, slope (Fig. 9) can be

used for estimating depth of LBR by quantitative

matching with slope obtained experimentally.

Experimental validation of depth-indicativeness

of slope feature using model-mimicking phantom

shall ready the approach to be used on skin.

[1] Luo, Y., Cui, H., Gu, X., Liu, R., & Xu, K. (2005). “Determination of

optimal source-detector separation in measuring chromophores in

layered tissue with diffuse reflectance.” Chinese Optics Letters, 3(11),

659-661.

[2] Wang, L., Jacques, S. L., & Zheng, L. (1995). “MCML—Monte Carlo

modeling of light transport in multi-layered tissues.” Computer

methods and programs in biomedicine, 47(2), 131-146.

[3] Nishidate, I., Maeda, T., Aizu, Y., & Niizeki, K. (2007). “Visualizing

depth and thickness of a local blood region in skin tissue using

diffuse reflectance images.” Journal of Biomedical optics, 12(5),

054006-054006.