Preliminary Monte Carlo based Inverse Model to Extract Optical

Tissue Properties from Experimental Diffuse Reflectance

Measurements

Coefficients Extraction for Gastrointestinal Dysplasia Detection

S. Pimenta

, E. M. S. Castanheira

and G. Minas

Centro Algoritmi, University of Minho, Campus de Azurém, Guimarães, Portugal

Centre of Physics (CFUM), University of Minho, Campus de Gualtar, Braga, Portugal

Keywords: Gastrointestinal Cancer, Spectroscopy, Diffuse Reflectance, Monte Carlo Simulations, Absorption

Coefficient, Scattering Coefficient.

Abstract: The ability to detect cancer at its earliest stages, called “dysplasia”, is the key of its successful treatment.

Optical techniques, such as diffuse reflectance and intrinsic fluorescence, may improve the ability to detect

gastrointestinal (GI) dysplasia since they have the potential to provide morphological and biochemical

information of normal and malignant tissues. However, those optical tissue properties can only be provided

if it is possible to extract information from the measured diffuse reflectance and intrinsic fluorescence

signals. This paper presents the implementation and the validation of a preliminary Monte Carlo based

inverse model to extract optical tissue properties, such as the absorption and the scattering coefficients, from

diffuse reflectance experimental measurements in phantoms.

1 INTRODUCTION

The detection of cancer in its initial stage is one of

the major goals of biomedical research.

Gastrointestinal (GI) cancers are usually preceded

by pre-cancerous changes and its early detection,

especially at the dysplasia stage (before

macroscopically visible changes occur on the

tissues), will increase the chances of a successful

treatment to the patient, improving the survival rate

(Georgakoudi et al., 2001, Ell, 2003, Yu et al.,

2008b).

A GI dysplasia is difficult to detect by visual

inspection during endoscopy or colonoscopy, due to

the lack of macroscopically easily visible changes on

the tissues that occur during the early stage of

cancer. Therefore, a large number of biopsies are

performed in order to increase the detection

probability of these invisible lesions (Georgakoudi

et al., 2001, Ell, 2003, Mayinger et al., 2003).

However, biopsies are procedures with sampling

errors (since the sample condition could not be

representative of the tissue malignant stage), high

cost and are uncomfortable to the patients. Finally,

their results are not immediately available, resulting

on a delay of patient’s treatment (Yu et al., 2008b,

Georgakoudi, 2006).

For all these reasons, in recent years, there has

been a strong interest towards the development of

new methods for a more objective detection of the

differences between premalignant and normal GI

tissues. Epithelial tissues acquire altered optical

properties during disease progression, especially due

to the changes on their morphological and

biochemical features. Spectroscopic techniques,

especially diffuse reflectance and intrinsic

fluorescence, have received special attention, due to

their potential to detect small changes in the tissues.

These techniques are based on light-tissues

interactions, allowing the detection of

macroscopically invisible lesions on the tissue

surface, in other words, lesions at the dysplastic

stage (Georgakoudi et al., 2001, Ell, 2003, Yu et al.,

2008b, Georgakoudi, 2006).

Concerning diffuse reflectance measurements, it

is used white light that is directed to the epithelial

tissues where it is absorbed and scattered, and part

of it returns to the tissue’s surface with information

about its optical properties. For the gastrointestinal

tissues, absorption is mainly due to the presence of

114

Pimenta S., Castanheira E. and Minas G..

Preliminary Monte Carlo based Inverse Model to Extract Optical Tissue Properties from Experimental Diffuse Reﬂectance Measurements - Coefﬁcients

Extraction for Gastrointestinal Dysplasia Detection.

DOI: 10.5220/0004790201140121

In Proceedings of 2nd International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS-2014), pages 114-121

ISBN: 978-989-758-008-6

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

hemoglobin, while scattering is caused by the

collagen fibers present in the connective tissue. An

increase in hemoglobin concentration, related to the

angiogenesis (due to the early cancer progression),

results in a reduction of the diffuse reflectance

signal. Moreover, with cancer progression, the

epithelial thickness increases, which reduce the

quantity of light that reaches the collagen fibers.

Thus, a decrease in scattering also causes a decrease

of diffuse reflectance signal (Georgakoudi et al.,

2001, Yu et al., 2008b, Georgakoudi, 2006).

Regarding intrinsic fluorescence (fluorescence

without diffusion or absorption distortions),

epithelial tissues produce fluorescence when excited

by ultraviolet (UV) or blue-wavelength visible light.

GI tissues have multiple fluorophores related to the

cell structure and metabolism, such as collagen and

NADH, which are known as markers of

pre-cancerous changes. A decrease of collagen and

an increase of NADH could be related to dysplasia

progress, which results in a change of the

fluorescence signal (Georgakoudi et al., 2001, Ell,

2003, Georgakoudi, 2006, Skala et al., 2004).

The intensity and shape of diffuse reflectance

and intrinsic fluorescence spectra are dependent on

the tissues pathological state and, therefore, its

measurement can be used to extract information

about the degree of tissue's malignancy. Several

authors (Georgakoudi et al., 2001, Ell, 2003, Yu et

al., 2008b) have developed prototypes that include

those spectroscopy measurements for an accurate

detection of GI dysplasia. However, their

spectroscopy systems use expensive, complex and

bulky illumination and detection equipments, e.g.,

xenon arc lamps or UV lasers, optical fibers, high

quantum efficiency detectors and spectrographs that

may hamper its integration in several endoscopic

equipments.

Other groups have tried to develop spectroscopy

microsystems (Yu et al., 2008a, Lo et al., 2009).

Such microsystem, due to its small size, could be

used at any screening room. Yu et al. developed a

system to extract optical tissues properties from

diffuse reflectance signal, based on the use of

photodiodes as detectors. However, a

monochromator, a xenon lamp and optical fibers

were still used for illumination, which makes

complex the system miniaturization. Lo et al.

developed a system to measure tissues’ diffuse

reflectance signal, based on the use of LEDs (Light

Emission Diodes), as a light source, and

photodiodes, as light detectors. Despite this great

advance in reducing cost and size, the authors only

used five LEDs, which may not be enough to extract

information for a complete diagnosis of dysplasia.

Thus, the development of a spectroscopy

microsystem on a chip, without the need of regular

optical fibers or spectrometer, that might be used

in-loco, will have a high clinical value and

represents the main innovation of the target project

under this paper. The miniaturized system will be

portable and may be integrated, for example, in

endoscopes or colonoscopes, reducing the

limitations of the existing methods for the early

detection of GI dysplasia.

The microsystem is going to combine the

implementation of two optical techniques – diffuse

reflectance and intrinsic fluorescence – and the

concept of thin-film optical filters deposited on

silicon photodiodes. Miniaturized LEDs will be

incorporated on the chip, featuring illumination

sources for fluorescence and diffuse reflectance

measurements. The model presented in this paper

will extract information of the optical tissue

properties from the diffuse reflectance

measurements.

2 METHODOLOGY

As previously described in detail (Pimenta et al.,

2013), to achieve the objectives described above,

towards the microsystem implementation, the work

is divided in several phases. Then, the main tasks of

the project are presented:

 Spectrophotometric measurements in order to

determine the relevant wavelengths/spectral

bands for the detection of GI dysplasia and

determine if the spectroscopic signals are

temperature dependent;

 Implementation of mathematical models to

extract optical tissue properties from diffuse

reflectance and fluorescence measured

signals;

 Design and fabrication of optical filters based

on Fabry-Perot thin-film optical resonators

and centered in the relevant wavelengths;

 Design and fabrication of photodiodes with

improved quantum efficiency at the relevant

spectral bands.

 Selection of miniaturized ultraviolet and

white-light LEDs for fluorescence and diffuse

reflectance measurements, respectively;

 Microsystem implementation and test.

PreliminaryMonteCarlobasedInverseModeltoExtractOpticalTissuePropertiesfromExperimentalDiffuseReflectance

Measurements-CoefficientsExtractionforGastrointestinalDysplasiaDetection

115

3 WORK DEVELOPED

This section presents the experimental work that has

already been developed by the research team,

towards the spectroscopy microsystem

implementation.

3.1 Validation of the Relevant Spectral

Bands for Diagnosis

Studies performed by D. S. Ferreira (Ferreira et al.,

2011b, Ferreira et al., 2011a) concluded that 16

spectral bands (between 350 nm and 750 nm) would

be an appropriate number of wavelengths for

diagnosis of dysplasia. The authors designed 16

thin-films optical filters centered in 16 spectral

bands, and six of them were fabricated, at

INESC-MN, Lisbon. The validation of the fabricated

optical filters to extract spectroscopic signals was

performed with commercial equipment and

phantoms. As future work, the thin-film optical

filters centered in wavelengths in near-UV/blue and

near infra-red wavelength range will be improved.

3.2 Temperature Dependence of the

Diffuse Reflectance Signal

In order to evaluate the temperature dependence of

the diffuse reflectance signal, experimental

measurements with a set of phantoms were

performed, at four different temperatures: Tr (room

temperature), T1 (37 ºC), T2 (40 ºC) and T3 (42 ºC).

Thus, a set of liquid homogeneous phantoms

were created, with variable concentrations of an

absorber (hemoglobin) and a scatterer (intralipid), in

order to simulate tissues with variable properties.

The hemoglobin (Hb) used is water soluble and was

obtained from Sigma-Aldrich (H0267). The

intralipid used is a 20% emulsion and was also

obtained from Sigma-Aldrich (I141). During the

experimental tests, it was assumed that the

hemoglobin oxygenation was constant.

Table 1 shows the combinations of phantoms

created to the temperature tests. The experimental

measurements were performed in a commercial

UV-Vis-IR spectrophotometer (Shimadzu UV-

3101PC) equipped with integrating sphere.

Figure 1 shows the results for the phantom 5. As

it can be seen, the diffuse reflectance signal is

similar for all the temperatures tested. Similar results

were obtained for all the phantoms, which allow

concluding that the temperature of the sample does

not affect the diffuse reflectance signal. Thus, to

extract the optical properties of a tissue, from the

diffuse reflectance signal, it is not necessary to

consider its temperature.

Table 1: Created phantoms for temperature tests.

Phantom

Intralipid mass

concentration

Hb concentration

(mg/mL)

1 0.50 0.25

2 0.50 0.50

3 0.50 1.00

4 1.00 0.25

5 1.00 0.50

6 1.00 1.00

7 2.00 0.25

8 2.00 0.50

9 2.00 1.00

In the future, similar tests will be performed for

the fluorescence signal.

Figure 1: Diffuse reflectance spectra at different

temperatures for phantom 5.

3.3 Implementation of a Model to

Extract Tissue Properties

As previously mentioned, the intensity and shape of

diffuse reflectance and fluorescence spectra are

dependent on the tissues properties and its

measurement can be used to extract information

about the degree of tissues dysplasia. The extraction

of these parameters requires the application of

mathematical models.

3.3.1 Theoretical Considerations

The diffuse reflectance signal is affected by the

absorption and scattering properties of the tissue,

which are defined by absorption and scattering

coefficients (μ



and μ



, respectively). μ



is related

with the concentration of chromophores in the

tissue, for example hemoglobin, while μ



is related

with the size and concentration of scattering

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molecules, such as collagen fibers in the epithelial

tissues. Another important concept is the reduced

scattering coefficient, μ



’, which is the μ



with a

corrected factor  (anisotropic factor) – equation (1).

 is a factor that takes in account the angular

dependence of a scattering event (it can vary from -1

[scattering in the backward direction] to 1 [scattering

in the forward direction]), and its typical values for

epithelial tissues are in the range of 0.7 to 0.95,

across the wavelengths () 750 nm to 350 nm,

respectively (Yu et al., 2008b, Georgakoudi, 2006,

Zhu, 2007).



’ μ



1 

(1)

The information carried on the diffuse

reflectance signal is difficult to interpret due to the

presence of combined absorption and scattering

events. So, a change in the diffuse reflectance signal

could be related with a change in absorption and/or

scattering. Quantifying the affectation of each event

on the diffuse reflectance signal, allows the

possibility to extract μ



and μ



that define tissue

optical properties.

Several authors have developed models to extract

the absorption and scattering coefficients of tissues

from diffuse reflectance experimental

measurements. Some models are based on an

approximation of the transport diffuse equation,

developed by Zonios et al. in 1999 (Zonios et al.,

1999). Despite the low computational intensity,

these models are not suitable for a wide wavelength

range of the electromagnetic spectrum.

Other models are based in Monte Carlo

simulations (Bender et al., 2009, Palmer and

Ramanujam, 2006, Yu et al., 2008a) and in a scaling

approach developed by Graaff et al. (Graaff et al.,

1993). Monte Carlo simulations allow the simulation

of light propagation through a medium, such as a

tissue, and based on the optical properties of the

medium (μ



and μ



) obtaining the diffuse reflectance

spectrum. The main advantages of these simulations

are related to the facts that are suitable for a wide

range of absorption and scattering coefficients and

for a wide wavelength range of the electromagnetic

spectrum (UV-Vis to NIR). Moreover, the accuracy

of the model is dependent of the high contrast of the

chromophore extinction coefficient, over the

wavelengths, which happens with hemoglobin

(Palmer and Ramanujam, 2006).

In this paper, it is presented a preliminary inverse

model and its primary validation with liquid

homogeneous phantoms, that allow extracting μ



and μ



from the diffuse reflectance signal, using a

Monte Carlo forward model (freely available

software (Wang and Jacques, 1995) and an equation

that relates the diffuse reflectance with absorption

and scattering coefficients of a tissue, presented in

(Graaff et al., 1993). Equation (2) relates the diffuse

reflectance of a tissue (normalized by the diffuse

reflectance of a reference phantom with predefined

optical properties) at each wavelength – R



λ with

its optical properties as a function of wavelength -







and μ



:





N



j

c















(2)

where N represents the mean number of interactions

between each photon (that exits the tissue surface)

and the medium. N



j is the portion of

reflected photons after j interactions with the

medium. cλ represents the ratio

















, that

define the optical properties of the tissue. Finally,



λ represents the ratio



,





,



,



, that

define the optical properties of the reference

phantom. As mentioned above, its diffuse

reflectance (obtained with Monte Carlo forward

model) will be used for normalizing the

experimental diffuse reflectance of the tissue.

Thus, as first step it is necessary to choose a

reference phantom with known optical properties

that will be used in the forward Monte Carlo model

to obtain its diffuse reflectance. It was used a liquid

phantom with the following properties (similar to the

phantom in (Graaff et al., 1993) in order to use the

tabulated values for N and N







 Hemoglobin (absorber) concentration equal to

0.25 mg/mL;

 Intralipid (scatterer) mass concentration equal

to 2%, representing collagen fibers.

Based on the knowledge of hemoglobin and

intralipid concentration, it is possible to obtain the

absorption and scattering coefficients that define the

reference phantom as a function of wavelength

(μ

,

λ and μ

,





, respectively).

The absorption coefficient as a function of

wavelength (μ



λ) could be obtained by the

application of the equation (3):





ln10ε





C



(3)

where ε



λ is the extinction coefficient of the

absorber, that defines its capacity to absorb light as a

function of wavelength; C



is the absorber

concentration in the phantom.

The scattering coefficient as a function of

wavelength could be obtained using the Mie theory

for spherical particles, available as free software in

PreliminaryMonteCarlobasedInverseModeltoExtractOpticalTissuePropertiesfromExperimentalDiffuseReflectance

Measurements-CoefficientsExtractionforGastrointestinalDysplasiaDetection

117

(Mätzler, 2002). The source code can be used in

Matlab, given the volume fraction (vf = 0.1, for the

reference phantom), refractive index (n = 1.362

(Ding et al., 2005)) and size (s = 456 nm (Choukeife

and L’Huillier, 1999)) of the intralipid spheres and

the refractive index of the surrounding medium,

water in this case (n = 1.33).

Once the μ

,

λ and μ

,





are known, they

could be used in the Monte Carlo forward model in

order to obtain the diffuse reflectance of the

reference phantom. As mentioned previously, it was

used a free available software (precompiled PC

version) (Wang and Jacques, 1995). However, some

Matlab functions were created in order to introduce

the features of the reference phantom in the model,

according to the software manual available in (Wang

and Jacques, 1995). The parameters used in the

Monte Carlo simulation were: total number of

photons (30000), μ









, μ









, isotropic

scattering (g=0), model dimensions (1cm(radius)

1cm(depth)), phantom refractive index (1.332) and

above/below refractive index medium (1.0 and 1.5,

respectively).

As a second step, it is necessary to use a Matlab

optimization function, lsqcurvefit, with initial

random input solutions for absorption and scattering

coefficients (λ). This function is based on the

least-squares algorithm and its main goal is

iteratively updated λ until the value of modulated

reflectance (equation (1) output) is similar to the

experimental diffuse reflectance that define our

phantom, allowing to extract its optical properties



λ and μ







. It is important to note again that

both diffuse reflectances (modulated and

experimental) are normalized by the diffuse

reflectance of the reference phantom (previously

obtained with Monte Carlo forward model).

3.3.2 Tissue Phantoms

For a preliminary validation of the implemented

model, a set of liquid homogeneous phantoms were

created, with variable concentrations of the absorber,

hemoglobin (0.27 mg/mL, 0.49 mg/mL and

1.03 mg/mL), and with a mass concentration of

intralipid of 0.5%. The diffuse reflectance spectrum

of each phantom was measured between 350 nm and

750 nm, using an UV-Vis-NIR spectrophotometer

equipped with integrating sphere (Shimadzu

UV-3101PC). Moreover, the wavelength-dependent

hemoglobin extinction coefficient was obtained with

the same spectrophotometer.

3.3.3 Validation of Monte Carlo Forward

Model

Once the implemented inverse model uses the Monte

Carlo forward model to obtain the diffuse

reflectance of the reference phantom, it was

performed a validation using the homogeneous

phantoms created, for evaluating the accuracy of the

free available software used (Wang and Jacques,

1995), in the achievement of the diffuse reflectance

of a medium.

Thus, knowing the hemoglobin and intralipid

concentrations, for each created phantom, the optical

properties that define each of them – μ



λ and







– were obtained by the application of

equation (3) and the freely available software for

Mie theory. After that, these properties were used in

a Monte Carlo simulation with the following

parameters: total number of photons (30000), μ

















, anisotropic scattering (g=0.8), model

dimensions (1cm(radius)  1cm(depth)), phantom

refractive index (1.332) and above/below refractive

index medium (1.0 and 1.5, respectively).

Figure 2 shows the ratio between the modeled

diffuse reflectance (DR

mod

) and the measured diffuse

reflectance (DR

exp

) for each created phantom as a

function of wavelength. Despite the slight deviations

from unity (the perfect agreement), especially in

wavelengths that correspond to the hemoglobin

absorption peaks (420 nm, 540 nm and 580 nm), in

general it can be concluded that there is a good

agreement between the modeled (obtained from

Monte Carlo forward model) and the

experimental/measured diffuse reflectance, since de

ratio between them is close to unity across the

analyzed wavelengths.

Figure 2: Ratio between the modeled diffuse reflectance

(DR

mod

) and the measured diffuse reflectance (DR

exp

)

spectra for each created phantom.

3.3.4 Validation of the Inverse Model

A preliminary validation of the implemented inverse

model was performed using the created liquid

phantoms. Thus, for each phantom, the optimization

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function (lsqcurvefit) was repeated several times

with different initial input solutions, for absorption

and scattering coefficients, in order to increase the

probability of extracting a global minimum.

Moreover, the obtained coefficients were

mathematically approximated, in order to ensure that

the values follow the form of the equations that

characterize them.

Next, the absorption and scattering coefficients

extracted from the inverse model were compared

with the expected/theoretical coefficients of each

phantom. Figures 3 and 4 show the plots of the

extracted versus expected absorption and reduced

scattering coefficients, respectively, for all

wavelengths and each created phantom.

Figure 3: Extracted versus expected absorption coefficient

(μ









) for each created phantom.

Figure 4: Extracted versus expected reduced scattering

coefficient (μ



′







) for each created phantom.

As it can be seen in figures 3 and 4, the

implemented inverse model allows the extraction of

the absorption and scattering coefficients with low

differences between the expected and extracted

tissue optical properties, especially for the

absorption properties, since the extracted

coefficients are very similar to the expected.

Concerning the reduced scattering coefficient,

phantom 3 has a slight deviation from the expected

result. So, in the future it will be important to

evaluate if this deviation affects the final tissue

diagnostic. If it is significant in the diagnostic result,

the implemented model must be improved in order

to avoid diagnostic errors.

3.3.5 Validation of the Inverse Model with

the 16 Spectral Bands

As previously mentioned, studies performed by D.

S. Ferreira (Ferreira et al., 2011b, Ferreira et al.,

2011a) concluded that 16 spectral bands (350, 370,

380, 400, 420, 450, 480, 510, 540, 560, 580, 600,

620, 650, 700 and 750 nm) would be an appropriate

number of wavelengths for diagnosis of dysplasia.

So, based on the diffuse reflectance values at the

16 spectral validated bands, it was reconstructed the

total diffuse reflectance, for each phantom, using a

Matlab fitting function – spline. The main advantage

of using splines is related with the fact that they can

be used to represent functions over large intervals,

where it would be impractical to use a single

approximating polynomial, since they are smooth

piecewise polynomials.

Figure 5 shows the measured (experimental)

diffuse reflectance spectra (blue curve – R1) and the

reconstructed diffuse reflectance spectra (red curve –

R1 recons) from phantom 1. As mentioned above,

R1 recons are obtained based on the use of the 16

spectral bands previously validated (green points -

Spectral bands) and the spline function.

Figure 5: Diffuse Reflectance from phantom 1:

experimental spectra (R1), reconstructed spectra (R1

recons) and the 16 spectral bands used for reconstruction

(Spectral bands).

Based on the reconstructed spectra of the liquid

created phantoms (1, 2 and 3), a validation of the

implemented inverse model was performed, in order

to compare the results obtained with the original or

experimental spectra. Again, for each phantom, the

optimization function (lsqcurvefit) was repeated

several times with different initial input solutions, in

order to extract the absorption and scattering

PreliminaryMonteCarlobasedInverseModeltoExtractOpticalTissuePropertiesfromExperimentalDiffuseReflectance

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119

coefficients. Figures 6 and 7 show the plots of the

extracted versus expected absorption and reduced

scattering coefficients, respectively, for all

wavelengths and for each created phantom.

As it can be seen in figures 6 and 7, the use of

the reconstructed spectra (from the 16 spectral

bands) in the implemented inverse model allows the

extraction of the absorption and scattering

coefficients with low differences between the

expected and extracted tissue optical properties,

especially for the absorption coefficient. As

previously referred, some slight deviations in the

reduced scattering coefficient must be evaluated.

Figure 6: Extracted versus expected absorption coefficient

(μ









), using the reconstructed spectra for each

created phantom.

Figure 7: Extracted versus expected reduced scattering

coefficient (μ



′







), using the reconstructed spectra

for each created phantom.

4 CONCLUSIONS AND FUTURE

GUIDELINES

The work performed until now allow to conclude

that the temperature of a tissue doesn’t affect the

diffuse reflectance signal. Similar tests will be

performed for the fluorescence signal and using

different phantoms.

The absorption and scattering properties of a

tissue could be extracted from the diffuse reflectance

signal, by the implementation of a Monte Carlo

based inverse model. The extracted coefficients

obtained, with a set of phantoms, were similar to the

expected coefficients. Moreover, the implemented

inverse model was validated using the reconstructed

spectra of the phantoms, based only in 16 values of

diffuse reflectance, for each phantom. This

validation will allow the design and fabrication of

optical filters and photodiodes, centered in these 16

spectral bands.

Nevertheless, in the future the model will be

tested with more and different phantoms, for

evaluating its accuracy in the extraction of optical

tissue properties. Moreover, different reference

phantoms will also be used to test their influence in

the accuracy of the model.

Finally, the other tasks described in section 2 are

ongoing towards the full microsystem

implementation.

ACKNOWLEDGEMENTS

This work is funded by FEDER funds through the

"Eixo I do Programa Operacional Fatores de

Competitividade (POFC) QREN, project reference

COMPETE: FCOMP-01-0124-FEDER-020241, and

by FCT- Fundação para a Ciência e a Tecnologia,

project reference PTDC/EBB-EBI/120334/2010.

S. Pimenta thanks the FCT for the

SFRH/BD/87605/2012 PhD grant.

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