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TitleMonte Carlo Simulations of Light Propagation in Human Sinus Cavities
LanguageEnglish
File Size54.4 MB
Total Pages132
Document Text Contents
Page 1

Monte Carlo Simulations of Light
Propagation in Human Sinus Cavities

Diploma Paper
by

Elias Kristensson and Lisa Simonsson

Lund Reports on Atomic Physics, LRAP-361
Lund, May 2006

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Abstract

A study in Sweden, has shown that about 2.2% of all consultations with the
Primary Care Unit are due to problems with the sinus cavities. In 1993, 2.8%
of the Swedish population got diagnosed with sinusitis. Today, it is hard to,
in a simple way, diagnose sinusitis. It is very difficult to tell sinusitis from
a normal cold. Simple tools and techniques have been requested to improve
the diagnosis for a long time.

We investigate the possibility of using diode laser gas spectroscopy for
sinusitis diagnostics, by simulating light propagation based on the Monte
Carlo method, implemented by the software Advanced Systems Analysis
Program (ASAP™). Simulations and experimental data have been com-
pared for a model based on two scattering plates, representing human tissue,
with an air gap in between, representing the sinus cavity. Simulations have
also been performed to optimize the detection geometries used in the exper-
iments. The possibility of imaging measurements of the sinuses has as well
been studied.

The results show good resemblance between the simulations and exper-
imental data, even though there are differences on a detailed level. No
general optimal detection geometry is found but there are optimal detection
geometries for some properties of the scattering plates and some measure-
ment techniques. Imaging simulations on the frontal and maxillary sinuses
have also been performed, showing that there are possibilities to spatially
study these sinuses with moderate resolution.

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65 4 Results and discussion

Air distance [mm] 1 5 10 30 70
1 pass[%] 41 56 68 84 87

3 passes[%] 24 22 18 12 11
5 passes[%] 14 10 7 3 2
7 passes[%] 8 5 3 1 1
9 passes[%] 5 3 2 0 0

11 passes [%] 3 2 1 0 0
13 passes [%] 2 1 0 0 0

2::: [%J 1 ~ 97 1 ~ 1oo 1 ~ 1oo 1 ~ 1oo 1 ~ 1oo II

Table 4.7: The table shows in percent, how the number of multiple passes
varies with the air distance, in relation to all rays reaching the detector, for
h = 6 mm and l2 = 10 mm and ri = 2.5 mm in transmission geometry.

Air distance [mm] 1 5 10 30 70
1 pass[%] 41 56 66 84 88

3 passes [%] 23 22 19 12 10
5 passes[%] 14 10 8 3 2
7 passes[%] 8 5 3 1 0
9 passes[%] 5 3 2 0 0

11 passes [%] 3 2 1 0 0
13 passes [%] 2 1 0 0 0

2::: [%J 1 ~ 96 1 ~ 1oo 1 ~ 1oo 1 ~ 1oo 1 ~ 1oo 11

Table 4.8: The table shows in percent, how the number of multiple passes
varies with the air distance, in relation to all rays reaching the detector, for
h = 10 mm and l2 = 6 mm and ri = 2.5 mm in transmission geometry.

Air distance [mm] 1 5 10 30 70
1 passes [%] 35 49 61 81 88
3 passes [%] 22 23 20 14 10
5 passes[%] 14 11 9 4 2
7 passes[%] 9 6 4 1 1
9 passes[%] 6 4 2 0 0

11 passes [%] 4 3 2 0 0
13 passes [%] 3 1 1 0 0

2::: [%J 1 ~ 93 1 ~ 97 1 ~ 1oo 1 ~ 1oo 1 ~ 1oo 11

Table 4.9: The table shows in percent, how the number of multiple passes
varies with the air distance, in relation to all rays reaching the detector, for
h = 10 mm and l2 = 10 mm and ri = 2.5 mm in transmission geometry.

Page 67

4.3 Detection aperture optimization 66

is observed, i.e. an increase of the air distance results in less rays detected
with multiple passes. This could again be understood by the finite size of
the detector. An increase of the air distance will result in rays entering
the secondary scatterer further out and the possibility for these rays to be
detected decreases. This also explains why a thicker secondary scatterer
results in more detected multiple passes rays, since more oblique rays will
be collected.

The simulation time of a transmission geometry depends on the same
factors as the backscattering geometry. At larger air distances the photon
statistics are poor as very few (fewer than in the backscattering geometry)
photons reaches the detector, about 0.1% of the injected flux is detected in
the transmission geometry. This means that more injected rays are wanted,
which also increases the simulation time. The transmission geometries are
simulated with 2 000 000 rays injected. Such a simulation takes 6-8 days
per curve.

4.3 Detection aperture optimization

The annular detection aperture in the backscattering model, can be seen as a
ring with an inner radius, ri and an outer radius, r 0 ; see Fig. 3.11 in Chap.
3.5. Its task is to stop directly backscattered photons, shortcut photons,
which only have interacted with S1. A larger inner radius of the detection
aperture will sample photons from deeper volumes. At the same time the
detected flux will decrease with a large inner radius. A larger outer radius
will rise the value of the equivalent mean path length as well, as more of the
deep going photons will be sampled. The flux is getting lower at larger radii,
thus the size of the outer radius will have a smaller impact than the size of
the inner. It may also have the effect of moving the maximum Leq towards
larger values of d. This behavior is appreciated as the maximum value of Leq
should be located at an air distance d of about 10 mm, which is the depth
of a human frontal sinus cavity [1]. All these behaviors were investigated
in simulations. The detection geometry of the transmission model do not
affect the curve shape or size, as seen in Fig. 4.6.

To be able to give an indication of which dimension on the detection
aperture that is the optimal, an optimization number Q st must be found.
To find an equation for Q8t, it is important to know what parameters that
are of interest. In this case there are three factors of impact; the flux, the
equivalent mean path length (Leq) and the air gap d, corresponding to the
maximum Leq·

Qst = (Leq)max · r/Jdet · d d ~ 10mm (4.2)

In Fig. 4.7 (a) it is seen how the flux increases with the outer radius of
detection, while keeping the inner radius fixed at 3 mm. Fig 4.8 (a) shows

Page 131

05/30/06 09:55:49

! ! SPATIAL RESOLUTION MAXILLARY SINUSES

$DO 1 9 2
{
$TIC
! !
SYSTEM NEW
RESET

UNITS MM 'mW'
WAVELENGTH 760 NM

LEVEL 2E9

COATING PROPERTIES
0 1 'TRANSMIT'
1 0 'REFLECT'
0 0 ' ABSORB '

MODEL
VOLUME 0.99 1E - 9'2E - 5 1

MODEL
VOLUME 0.87 16 1

MEDIA
1.0 'AIR'
1.53 'SCHOTT'
1.49 'PLEXI'
1.0 SCATTER .2 'OXY'
1.48 SCATTER .1 0 1 10000000 10000000 'DELRIN'

FRESNEL TIR

SPLIT 10 MONTECARLO

HALT 1000 1E - 6
! !-------------------------------------------------- ---------------------

! !DETECTOR
ENT OBJECT

ELLIPSOID 79 129 104 0 0 0 'PMT'
INTERFACE COATING ABSORB AIR AIR
REDEFINE COLOR 5

! ! FILTER
ENT OBJECT

ELLIPSOID 78.5 128.5 103.5 0 0 0 'FILTER.1'
INTERFACE COATING BARE SCHOTT AIR
REDEFINE COLOR 5

! ! FILTER
ENT OBJECT

ELLIPSOID 75.5 125.5 100.5 0 0 0 'FILTER.2'
I NTERFACE COATING BARE SCHOTT AIR
REDEFINE COLOR 5

! !HEAD
ENT OBJECT

ELLIPSOID 75 125 100 0 0 0 'HEAD'
INTERFACE COATING BARE DELRIN AIR
REDEFINE COLOR 6

! !MAXI LLARY SI NUS 1
ENT OBJ ECT

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Page 132

05/30 / 06 09:55:49

ELLIPSOID 15 17.5 20 33 - 10 - 58 'BIHALA . 1'
INTERFACE COATING BARE DELRIN OXY
REDEFINE COLOR 7
ROTATE Y 40

!!MAXILLARY SINUS 2 ------------------------------
ENT OBJECT

ELLIPSOID 14.95 17.45 19.95 33 - 10 - 58 'BIHALA . 2'
INTERFACE COATING BARE OXY
REDEFINE COLOR 8
ROTATE Y 40

! ! ______________________________________________ __

! !ORAL CAVITY
ENT OBJECT ------------------------------------

ELLIPSOID 1 1 1 45 - 26 - 43 'ORALCAV'
INTERFACE COATING BARE AIR DELRIN
REDEFINE COLOR 9

! ! ______________________________________________ __

! !-----------------------------------------------

! !SOURCE
SEED 2000010?
EMITTING DISK Y 0 0.3 0.3 80000 10 1 0

FLUX TOTAL 2
SHIFT X 45
SHIFT Y - 26
SHIFT Z - 43
ROTATE X - 60 - 26 - 43
ROTATE Z 30 45 - 26

! !SOURCE

! !----------------------------------------------

! ! TRACE RAYS

SAVE 1 0

TRACE
STATS

CONSIDER ONLY PMT

$IO OUTPUT SEED10?.0UT ONLY
HISTORY LIST
$IO OUTPUT CLOSE

RETURN

$TIC
$BEEP
}

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