Studies on Rat Brain Phantoms for the Development of

Near-Infrared Spectroscopy (NIRS) System

Gaurav Sharma

, Yashika Arora

and Shubhajit Roy Chowdhury

School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India

Equal contribution

Keywords: Agarose, Intralipid, Near-Infrared Spectroscopy, Phantom, Rat Brain.

Abstract: The present work aims at developing static phantom for rat brain to model the inhomogeneities in the brain

tissues. The inhomogeneities have been modelled by varying the concentration of the fluids that mimic

those inhomogeneities. The local variations in various parts of brain can be considered as different

concentration of substances and accordingly having different optical attributes. Near-infrared spectroscopy

(NIRS) has been used to detect and estimate these local changes that indicate different brain activities. The

paper presents the development of static rat brain tissue phantoms and its analysis using a single channel

near-infrared spectroscopy (NIRS) system. Homogeneous phantoms have been prepared with different

concentrations of agarose and intralipid. For different concentrations, the NIRS signal has been acquired at

dual wavelengths (770 nm and 850 nm). With increase in the concentration of intralipid, an increase in the

amplitude of NIRS signal was noted. The response obtained due to 770 nm and 850 nm sources

corresponded to lower and upper amplitude respectively.

1 INTRODUCTION

Near-infrared spectroscopy (NIRS) is increasingly

becoming popular for monitoring cerebral

oxygenation level by measuring the time variations

in the concentrations of oxygenated haemoglobin

(HbO

) and deoxygenated haemoglobin. Cross-

sectional studies on cerebrovascular artery reveal

that impaired cerebral hemodynamics precede

transient ischemic attack and ipsilateral stroke

(Markus and Cullinane, 2001). The NIRS is a non-

invasive analytical tool that uses the electromagnetic

spectrum of wavelength range 700 nm to 2500 nm.

This spectroscopy technique involves both vibration

and electronic transitions. The basic instrumentation

consists of light sources, photo detectors and

dispersive component. This technology is widely

used in functional mapping of cerebral cortex. The

basic idea behind this technique is the fact that

absorption spectra of oxy- and deoxy- hemoglobin

are different in the optical window of 700 nm to 900

nm (Jobsis, 1977). Hence, these absorption spectra

are used as biomarkers in the analyses that relates to

the changes in hemoglobin concentration.

NIR light range is capable of penetrating a few

centimetres deep into the human brain tissue because

the absorption by the tissues is rather low in the NIR

window (Lin et al. 2002). The most favourable

choice of the dual wavelengths in the NIR range is

crucial for signal sensitivity and minimal crosstalk

(Villringer and Chance 1997). Typically, it is

required to choose one wavelength from greater than

NIR light window and other from lower than NIR

light window (Biswal et al. 2011). The selection of

pair of wavelength is used to measure oxy-

hemoglobin (HbO

) and deoxy-hemoglobin (Hb)

(Reynolds, 1988). Electrical stimulus response can

also be analyzed using NIRS. This is emerging as a

powerful non invasive diagnostic technique for

monitoring cerebral micro vessels (Sharma et al.

2016). The analyses with NIRS provide an aid in

studying various neural disorders (Dutta et al. 2015;

Arora et al. 2016).

In order to develop and evaluate the performance

of a spectroscopic system, a test on a suitable

phantom is required. The phantom should be a

replica of biological tissue and mimic the optical

properties as desirable for a NIRS system (Jindal et

al. 2015). The most popular phantom used in

research is a mixture of intralipid and agarose

solution (Cubeddu et al 1997; Lindquist et al 1996).

Sharma, G., Arora, Y. and Chowdhury, S.

Studies on Rat Brain Phantoms for the Development of Near-Infrared Spectroscopy (NIRS) System.

DOI: 10.5220/0006589801570163

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 1: BIODEVICES, pages 157-163

ISBN: 978-989-758-277-6

157

Some research groups also use gelatine for the

phantom preparations (Madsen et al 1992; Vitkin et

al 1995; Hielscher et al 1996).

In this paper we present an approach of

designing and implementing static phantom of rat

brain with varying concentration of intralipid in

agarose solution. A static phantom is the one whose

properties are almost constant throughout the

experiment whereas in case of dynamic phantom,

the experiment is done by varying the properties of

the phantom. Agarose solution allows more

hardening of the sample in comparison to ink or

gelatine solution and is therefore easy to handle. The

phantoms are placed on NIRS setup that constitutes

of two light emitting diodes and a photodiode. Our

work demonstrates the use of NIRS system on rat

brain phantoms which helps in quantifying the

performance of the NIRS system that can be used for

testing brain structures.

The paper is divided into four sections. Section I

presents the introduction to the study. Section II

describes the various materials used and procedures

followed up in the study. The description of NIRS

signal acquired by a single channel setup is given in

section III. Various issues related to phantom design

and NIRS functionality are discussed in section IV.

2 METHODS & PREPARATIONS

2.1 Phantom Preparation

The flowchart for preparing a static rat brain tissue

phantom model is shown in Figure1. The

development of static phantom of a rat brain follows

a four step process.

In the first step, a 3-dimensional rat brain

structure was simulated in Siemens PLM Software

SOLID EDGE ST9. Generally, model designs are

based on length, width, height and volume of

specific rat brain. The final simulation rat brain

model is stored in the “.par” format. This format is

compatible with 3D printer.

The second step involves positioning the rat

brain. In order to minimise the amount of supporting

material, best settings and position of rat brain

model was selected. The various processing

methods, take different amounts of time mainly

depending upon selected material. Polylactic Acid

(PLA) material is used in our experiment. It took

around thirty minutes to print the structure. Acetone

solution was used for surface finish and removal of

the supporting material.

In the third step, PDMS (Polydimethylsiloxane)

was used to make the mould around the printed rat

brain structure. The mould was prepared in two

halves using clay as a separating agent. For curing,

the mould structure was heated in microwave oven

for an hour at 90° C. After curing both the sides, a

cavity is formed in two moulds by removing the

PLA structure.

Figure 1: Flowchart to prepare a static phantom.

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In the final step, a solution of 1% of agarose in

distilled water was prepared by heating in the

microwave oven at 90°C. This solution has very low

absorption and therefore intralipid is used as a

scattering agent to get the preferred optical

properties. The important concern in designing a

phantom that closely resembles rat brain is the

amount of scattering and absorption agents. Agarose

powder (A9539, Sigma, and Life Science) was used

to make a solution in distilled water. Samples of the

solution were prepared by adding different

concentrations of intralipid in 1% of agarose

solution. The amount of intralipid in different

samples was: 0.625%, 1.25% and 2.5%. The

solution was stirred continuously to get the

uniformity in the sample. The mixing of intralipid in

agarose solution was done at 53°C and the mould

was filled using this solution at about 40°C. It was

then kept for some time to get proper hardening and

shape. It can be immersed in cold water for some

time for hardening. Later, the mould was removed.

The phantoms were stored in a moist petri dish

sealed with parafilm in refrigerator. Figure 2 depicts

the steps followed in preparing the phantoms.

Figure 2: Steps involved in preparing phantom.

2.2 Near Infrared Spectroscopy System

Set-up

Our NIRS system is shown in Figure 3. The NIRS

system consists of two light emitting diodes of 770

nm and 850 nm, one photodiode on neoprene fabric

base and NI myRIO board. NI myRIO board is used

as a controller for LEDs and signal acquiring unit for

the photodetector. The two wavelengths of 770 nm

and 850 nm are chosen because they are on the

opposite sides of the point: ~810 nm where oxy-

hemoglobin and deoxy- hemoglobin have identical

absorption coefficients (Villringer et al. 1993). The

two LEDS are placed together and photo diode is at

a distance of 1cm from the LEDs.

NI LabVIEW system design software has been

used for signal analysis. The simulate LabVIEW VI

was used to provide alternate analog input to both

LEDs and also for acquiring signal in the waveform

chart. This VI is compatible with NI myRIO board

as shown in Figure 4.

Figure 3: One channel NIRS setup.

Figure 4: LabVIEW VI for one channel NIRS system

setup.

2.3 Experimental Set-up

The phantom has been placed on NIRS set-up and it

has been covered with a black box to avoid ambient

light interference in the experiment as depicted in

Figure 5. The two LEDs were alternately made on

and off. The test is done for three phantoms: 0.625%

of intralipid in 1% agarose solution, 1.25% of

intralipid in 1% agarose solution and 2.5% of

Studies on Rat Brain Phantoms for the Development of Near-Infrared Spectroscopy (NIRS) System

159

intralipid in 1% agarose solution.

The duration of experiment for each phantom is

10 minutes. The signal sample points are recorded

from each phantom using NI myRIO board. In this

experimental approach dual wavelength LEDs were

alternately made on and off. The signal samples

have been recorded at the rate of eleven samples per

second. The first five sample points corresponded to

first LED (850 nm) and last five sample points

corresponded to second LED (770 nm).

Figure 5: Experimental set-up for testing phantoms.

3 RESULTS & DISCUSSION

The experimental measurements are taken on three

phantom samples: 0.625% of intralipid in 1%

agarose solution, 1.25% of intralipid in 1% agarose

solution and 2.5% of intralipid in 1% agarose

solution. The photo diode voltage output for the

samples is shown in Figure 6. Figure 7 illustrates the

photodiode output variation in the first testing

phantom sample 0.625% of intralipid in 1% agarose

solution.

Figure 6: Photodetector output voltage for three test

phantoms.

(a)

(b)

(c)

(d)

Figure 7: (a) Photodiode output signal in the first testing

phantom: 0.625% of intralipid in 1% agarose solution, (b)

First 100 sample points of recorded signal, (c) Upper

amplitude variation in the signal which corresponded to on

time period of LED source 850 nm, (d) Lower amplitude

variation in the signal which corresponded to on time

period of LED source 770 nm.

The output signal behaviour observed in the

second testing phantom sample: 1.25% of intralipid

in 1% agarose solution is shown in Figure 8. In the

second phantom, sample percentage concentration of

intralipid was increased twice as compared to the

first testing phantom sample. The photodiode output

signal performance detected in the third testing

0 1000 2000 3000 4000 5000 6000 7000

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phantom sample: 2.50% of intralipid in 1% agarose

solution is shown in Figure 9.

(a)

(b)

(c)

(d)

Figure 8: (a) Photodiode output signal in thesecond testing

phantom: 1.25% of intralipid in 1% agarose solution, (b)

First 100 sample points of recorded signal, (c) Upper

amplitude variation in the signal which corresponded to on

time period of LED source 850 nm, (d) Lower amplitude

variation in the signal which corresponded to on time

period of LED source 770 nm.

(a)

(b)

(c)

(d)

Figure 9: (a) Photodiode output signal in the third testing

phantom: 2.5% of intralipid in 1% agarose solution, (b)

First 100 sample points of recorded signal, (c) Upper

amplitude variation in the signal which corresponded to on

time period of LED source 850 nm, (d) Lower amplitude

variation in the signal which corresponded to on time

period of LED source 770 nm.

The increase in photo diode voltage with

increase in concentration of intralipid is clearly

visible. Intralipid is a scattering agent. Therefore

more is the concentration of intralipid in the sample,

more is the back scattering, and hence more will be

the output voltage of photo diode. The effect is

clearly visible on higher amplitude of NIRS acquired

signal that corresponds to higher wavelength of 850

nm as depicted in Figure 10. This effect is less

prominent in case of lower wavelength of 770 nm.

The intensities profile due to the phenomenon of

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161

backscattering is linked to the structural and

functional parameters of tissues. These analyses are

required for characterization of optical phantoms in

medical applications.

Figure 10: Amplitude profile for the three phantoms.

4 CONCLUSIONS

The work presented here describes the preparation of

solid static phantoms of agarose and intralipid. They

are tested using single-channel time domain NIRS

system. The phantom design presented is easy to

prepare and can be used to get inhomogeneous

samples with preferred optical properties. Using

similar procedures, it is possible to get multi-layered

structure with varying optical properties so as to

mimic real brain tissues. The NIRS system design is

based on a dual‐core ARM Cortex‐A9 processor

(myRIO Student Embedded Device) and have high

speed NIRS data acquisition rate. For this study, we

attempted to see the affect of scattering agent in the

medium on photo detector output. Similar attempts

can be made on sophisticated designed phantoms

that replicate the real brain. Such experiments

provide an insight in analysis of in vitro complex

structures.

ACKNOWLEDGEMENTS

This work was supported by DST-DAAD project

(POCT-NIRS), Indian Institute of Technology

Mandi, MeitY, Govt of India and University

Medical Center Freiburg. The authors would like to

thank Prof. Dr. Ulrich G. Hofmann, Rand K.

Almajidy for their significant suggestions and help

regarding this work.

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