Radiation Exposure Analysis in 3D Cancer Treatment

Dmitriy Dubovitskiy and Valeri Kouznetsov

Oxford Recognition Ltd, Cambridge, U.K.

Keywords:

Cancer Treatment, In Vivo Dosimetry, Radiation Sensors, Pattern Analysis, Decision Making, Object

Recognition, Image Morphology, Computational Geometry.

Abstract:

Dosimetry in the process of treatment of cancer tumour by ionising radiation. It is important and sometimes

very challenging due to the fact that it is necessary to measure the radiation dose in vivo on small areas on the

surface of the composite relief. Recently, in order to reduce the radiation dose to healthy tissues and concen-

tration of the therapeutic effect of radiation directly on the tumour application method of three-dimensional

(3D) irradiation started, in which radiation beams enter the body from different directions concentrating on

the tumour. New methods of treatment correspondingly require more precise and sophisticated methods of

dosimetry. Existing methods of 3D dose measurement are highly labor-intensive and generally suffer from

low accuracy. In this paper, we propose the technical method of 3D measurement of the dose in real-time and

approaches to build volume model of the dose distribution inside the patient’s body using object recognition

technique.

1 INTRODUCTION

For dosimetry of small areas in radiotherapy, common

equipment used includes traditional micro ionisation

chambers, semiconductor diode dosimeters, and in-

creasingly in recent years the very useful MOSFET

transistors. MOSFET transistors provide good accu-

racy and repeatability of results with dimensions of

a few millimetres. In addition they are joined har-

moniously with scanning and information process-

ing systems (Soubra, M., Cygler, J. and Mackay,

G.F.),(Thomson I., Reece M.H.). For 3D dosime-

try, Gel Dosimeters (ﬁrst proposed in the mid 80s)

are the most widely used tools (Yves De Deene, An-

drew Jirasek). These are models to replace the human

body during irradiation (referred to as ’phantoms’ by

radiologists), composed of a gel-like material which

changes its optical properties under the inﬂuence of

ionising irradiation. Once irradiated, optical scanning

reveals radiation focus through altered transparency

of the gel. This method it allows the technician to

customise parameters and 3D geometry of irradia-

tion. However, due to the requirement of fabricat-

ing custom ’phantoms’ for each use, gel dosimeters

are very costly, time consuming, and inconvenient,

while providing only moderately precise targeting and

dosage information. (Yves De Deene, Andrew Ji-

rasek). There are also methods of extrapolating 2D

measurements to 3D models. The most modern meth-

ods (Karthikeyan Nithiyanantham, Ganesh K. Mani,

Vikraman Subramani, Lutz Mueller, Karrthick K.

Palaniappan, Tejinder Kataria) of measurement sug-

gest to use of linear array diodes with 98 measure-

ment points for scanning space inside ’water phan-

tom’ (essentially an aquarium of water which closely

approximate the radiation absorption and scattering of

the muscle and other parts of human body). The data

is then linked to the patient CT image and the Monte

Carlo method used to extrapolate dose distributions

inside the patient’s body. Measurements are con-

trolled during irradiation by single point dosimiters

(diodes), which allows monitoring and adjustment of

treatment in vivo.

Impact of ionising irradiation at different MOS

(metal oxide semiconductor) structure have been

studied for quite a long time, at least since the mid 70-

s due to the start using of electronics based on MOS

technology in space systems (Ma T. P., Dressendorfer

P. V.). The processes occurring in such structures un-

der the inﬂuence of various types and intensity of ra-

diation is very well studied and described in numerous

articles associated with radiation hardness of MOS

IS (Kohler, Ross A., Kushner, R.A.),(S. Kaschieva),

(Claeys, C.,Simoen, Eddy), (G Meurant). For the pur-

poses of measuring the accumulated radiation dose in

medicine, such a structure, is still relatively new and

as an indication of the accumulated dose is used the

102

Dubovitskiy, D. and Kouznetsov, V.

Radiation Exposure Analysis in 3D Cancer Treatment.

DOI: 10.5220/0005818401020107

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 1: BIODEVICES, pages 102-107

ISBN: 978-989-758-170-0

effect of degradation of MOS structure, particularly

the under-gate dielectric (SiO2). Without going deep

into the details of physical processes, we mention

only the main effect that is used for dosimetry. Un-

der irradiation, gate dielectric accumulates a positive

charge which leads for example to the shift of thresh-

old voltage in a MOSFET transistor or to the shift

volt-farad characteristics of the MOS capacitor. If ap-

ply a positive voltage to the Gate of transistor (MOS

capacitor) in the process of irradiation, the amount

of accumulated charge increases. In the case if no

voltage was applied, it makes possible to irradiate the

passive MOS structure, and then measure the charge

that is equivalent to the dose. Other effects occurring

in the dielectric during irradiation can be ignored in

this case. In the range of doses used in medicine, the

charge accumulation is linear and proportional to the

dose and only at high doses about 6-8 Gy (depends

on technology of production) tends to saturation and

loses linearity. In addition to all of this characteris-

tics, dosimeters on MOS structures are small in size

(around 1 sq. mm ) and very simple in production.

The particular concern of this paper is the use of

such sensors for creation of net bandage dosimetry

system Figure 1, with a MOS capacitor sensor in ev-

ery node of the grid. Such dosimetry net can be placed

(dressed) around any part of the body (or fantom) and

will allow to control the dose of radiation for the in-

coming and outgoing ﬂow of irradiation and from any

side. This will allow to build a 3D model of the ab-

sorbed dose inside the patient’s body.

Figure 1: Net bandage.

2 STRUCTURAL SCHEME OF

DOSIMETRY SYSTEM

The proposed dosimetric net, can be a convenient and

inexpensive tool to verify the dose distribution inside

the body as well as building three-dimensional mod-

els of absorbed dose. Since the MOSFET was well

proven recently (A. Sathish Kumar, S. D. Sharma, and

B. Paul Ravindran),(A. Gopidaj, Ramesh S. Billima

GGA, Velayudham Ramasubramanian), (Bo-Young

Choe), (Briere TM, et al.), (Scalchi P, Francescon

P, Rajaguru P) as an in vivo dosimeter for absorbed

dose, we have decided to focus our attention to even

more simple structures such as the MOS capacitors

since the effects of charge accumulation in under-gate

dielectric of MOSFET (in fact under-gate MOS ca-

pacitor) determines its ability to function as a dosime-

ter. MOS sensors in our case have a number of ben-

eﬁts, MOS capacitors are extremely simple and inex-

pensive in production, we can select and vary any of

the parameters of this structure (thickness and type of

gate dielectric, the area of the structure) to improve its

operation as a dosimeter, because it’s a capacitor and

in this structure there is no need to consider the pa-

rameters necessary for the operation of the transistor.

The scheme of measurement of accumulated charge

which corresponds to the absorbed dose requires less

number of contacts (only 2 and one of them is com-

mon for all sensors) that facilitates the creation of ma-

trix or grid with a large number of sensors.

SWEEP

ADC

.....

MOS

1 2

sin

TEST SIGNAL

FILTER

DATA

TRANSMIT

UNIT

AMPLIFIER

WIRLESS

CONNECTION

Figure 2: The measuring system for collecting data from

sensors. 1- matrix of sensors, 2-multiplexer, 3-analog to

digital converter.

The block diagram of such a system is shown in

Figure 2. The accumulated charge in the oxide (equiv-

alent dose) offered to determine by measuring voltage

- farad characteristics. This method has long been

known as a main and classical method for measure-

ment of MOS structures properties (S. M. Sze), (E.

H. Nicollian, J. R. Brews), (Jay N. Zemel) and well

tested. A typical view of this characteristic for the

silicon substrate n-type is shown on the Figure 3.

The ﬁgure shows the shift of C-V characteristics

which occurs in the case of accumulating charge in

the gate oxide, the measurement of this shift is our

Radiation Exposure Analysis in 3D Cancer Treatment

103

Figure 3: Volt-Farad characteristic of typical MOS conden-

sator.

task in this case. Capacitive Sensors - dosimeters are

connected in matrix (1). The reading of the sensors

parameters is carried out consistently, analog mul-

tiplexer (2) switches from one structure to another.

Sweep generator G1 provides a slow shift in a range

of of a few volts on MOS and G2 provides a test sig-

nal with a frequency of 1 MHz and amplitude 10 mV.

The amplitude of the alternate voltage on resistor Rn

will be proportional to the capacity of MOS sensor, as

can be seen from the formulas below.

Rc = 1/wC, i = Ug

/Z = Ug

+ 1/w

i f R

= 1/wC >> R

, then i ≈ wC(Ug

)Ug

= iR

= U g

wC (Ug

) where w = 2π f

The measurement process for each sensor will

take approximately no more than 5-10 ms. It means

that the matrix of sensor with dimension 256 sensors

will be read out in 1.5 - 2.5 seconds. These data are

digitised by ADC (3) and sent to computer wirelessly,

where data can be already processed further. The ad-

vantages of MOS as a absorbed dose dosimeters is

also the fact that they kept the charge is quite stable

and readings can be taken long time after exposure.

When a MOS capacitors is irradiated, positive

charge trapped in the gate oxide (Hughes H. L.,

Benedetto J. M.), (Oldham T. R., McLean F. B. ),

(Adams J. R., Daves W. R., Sanders T. J.), it leads

to shift of volt-farad characteristics, see Figure 3. The

magnitude of the shift depends on the absorbed dose

and is approximately 200 mV in the absence of gate

voltage during irradiation. By applying a positive

voltage on the gate , we can increase the sensitivity

and shift can reach 400-500 mV per Gy.

To conﬁrm behaviour of MOS structures under ra-

diation by standard medical equipment the most com-

mon samples were taken, the gate oxide SiO2 was

grown in a dry environment at 1000C, thickness of

oxide was 0.6 micrometres on Si wafer 4.5 Om /cm

conductivity of n-type with F(ﬂuorine) doping. Size

of crystal was 1x1mm. Irradiation was carried out at

Photon clinical linear accelerator 6MeV (Varian 2100

EX), doses were 0 to 10 Gy, at room temperature. As

a control dosimeter, ionisation chamber ROOS was

used. Results of the experiment are shown on Fig-

ure 4.

Figure 4: Characteristic of volt shift for the silicon substrate

n-typer.

In our case we are going to use a successfully

tested system of pattern recognition and bind our

system of sensors on the skin of the patient. The

image recognition system was used for skin cancer

diagnostic and has been published in (Dubovitskiy

and Blackledge, 2008), (Dubovitskiy and Blackledge,

2009), (Dubovitskiy and Blackledge, 2008) (Dubovit-

skiy and McBride, 2013), (Dubovitskiy D, Devyatkov

V and Richer G ) and (Dubovitskiy and Blackledge,

2008) this redundant system was working well and we

expect it to be extremely effective.

3 SKIN PATTERN POSITIONING

SYSTEM

The current medical practice includes several radia-

tion exposure during the course of treatment with a

number of days in between. The position of the net

bandage on the patient’s skin is very important to al-

low consistency for the next treatment of the same

tumour. In order to address this, data from the CT

scan could be used to adjust position for the next treat-

ment. Computation of position is implemented by us-

ing Fractal Geometry theory to get the precise pattern

of the skin. The precise pattern of the skin is corre-

sponded to the calibration points on the net bandage.

The real time computation system will allow a doc-

tor to dynamically move the bandage and see the off-

BIODEVICES 2016 - 9th International Conference on Biomedical Electronics and Devices

104

set from the last treatment position. When offset ap-

proaches zero, the exact same position of the radiation

sensors will be reached.

The skin has texture and a particular skin region

could be characterised by Fractal features called Frac-

tal parameters. An image of a skin sample has been

taken by a specially designed dermatological image

acquisition camera on Figure 5.

Figure 5: Dermatological image acquisition camera.

The correspondent points are calculated from

Fractal parameters. If we consider the proﬁle of a

typical skin image, then the curve does not coincide

with a sin-wave signal. To obtain adequate accuracy,

it is necessary to magnify the resolution of the image,

which in turn introduces distortion. For increased ac-

curacy on low-resolution data, we consider a convolu-

tion function of a form more consistent with the pro-

ﬁle of a video signal. For a signal I we consider the

representation

F (k) =

∑

n=1

I (n)

arccos



cos



2π(k − 1)(n − 1)

−



−

−iarcsin



cos



2π(k − 1)(n − 1)



and for an image I with resolution m ×n,

F (p, q) =

∑

m=1

∑

n=1

I (m,n) (1)



arccos



cos



2π(p − 1)(m − 1)

−



−





arccos



cos



2π(k − 1)(n − 1)

−



−



−iarcsin



arccos



2π(k − 1)(p − 1)



×arcsin



cos



2π(k − 1)(n − 1)



(2)

In this work, application of the power spectrum

method used to compute the fractal dimensions of a

skin surface is based on the above representations for

F (k) and F (p,q) respectively. We then consider the

power spectrum of an ideal fractal signal given by

P = c|k|

−β

, where c is a constant and β is the spectral

exponent. In two dimensions, the power spectrum is

given by P(k

) = c|k|

−β

, where

+ k

. In

both cases, application of the least squares method or

Orthogonal Linear Regression yields a solution for β

and c, the relationship between β and the Fractal Di-

mension D

being given by

+ 2 − β

for Topological Dimension D

. This approach allows

us to drop the limits on the recognition of small ob-

jects since application of the FFT (for computing the

power spectrum) works well (in terms of computa-

tional accuracy) only for large data sets, i.e. array

sizes larger than 256 and 256×256. Tests on the accu-

racy associated with computing the fractal dimension

using equations (1) and (2) show an improvement of

5% over computations based on conventional Discrete

Fourier Transform.

The setup calculates Fractal features dynamically

from the centre of an image. The testing GUI software

is presented on Figure 6:

Figure 6: GUI software.

The original skin image from the camera is pre-

sented on Figure 7.

The current position of the net bandage and cam-

era is given from optical calibration marks Figure 8.

The corespondent points of the current Fractal

marks and optical position gives us the offset num-

ber which guide the doctor to the original position of

the sensor net bandage.

Radiation Exposure Analysis in 3D Cancer Treatment

105

Figure 7: The original skin image.

Figure 8: The optical calibration mark.

4 CONCLUSIONS

The focus of this paper is creation of the simple and

convenient system which allows to control the spatial

distribution of the accumulated dose inside body. The

use of modern image recognition technique allows us

to position the sensor net bandage in exact position

like it has been used. The measurement of input beam

radiation and output radiation (after passing the tis-

sue) gives us unique possibilities to provide more ac-

curate results. The calculation of exact accumulation

dose and its conﬁrmation by correct measurements is

the key to the right healing. Simple and reliable sys-

tem monitoring of 3D dose distribution will allow to

provide treatment in the safest way. The safe way

means that the healthy cells will not be the subject

of unnecessary exposure (as much as possible) and

will be be able maintain healthier life support. This

work represents the new approach to accurate radia-

tion exposure treatments. Industrial implementation

will require further experiments and technological in-

put in order to calibrate, coordinate and synchronise

use of this technology in a clinical setting. However,

we hope that our work will lay a foundation for the

next step in safe cancer treatment, ultimately prolong-

ing and improving the lives of many people.

ACKNOWLEDGEMENT

The work reported in this paper is supported by the

Oxford Recognition Ltd. The authors are grateful to

Robert Vyn for his help in the preparation of this pa-

per.

REFERENCES

Soubra, M., Cygler, J. and Mackay, G.F. (1984). Evaluation

of a Dual Metal Oxide-Silicon Semiconductor Field

Effect Transistor Detector as a Radiation Dosimeter.

Med. Phys. , 21 (4) April.

Thomson I., Reece M.H. (1988). Semiconductor MOSFET

Dosimetry. Proceedings of Health Physics Society

Annual Meeting.

Yves De Deene, Andrew Jirasek (2008). Uncertainty in 3D

gel dosimetry, 8th International Conference on 3D Ra-

diation Dosimetry (IC3DDose). Journal of Physics:

Conference Series 573 (2015) 01.

Karthikeyan Nithiyanantham, Ganesh K. Mani, Vikraman

Subramani, Lutz Mueller, Karrthick K. Palaniappan,

Tejinder Kataria (2015). Analysis of direct clinical

consequences of MLC positional errors in volumetric-

modulated arc therapy using 3D dosimetry system.

Journal of Applied Clinical Medical Physics Vol 16

No 5.

Ma T. P., Dressendorfer P. V. (1989). Ionizing radiation

effects in MOS devices and circuits. New York: Wiley

Interscience.

Kohler, Ross A., Kushner, R.A., (1988). Total dose radi-

ation hardness of MOS devices in hermetic ceramic

packages. Nuclear Science, IEEE Transactions on

(Volume:35 ,Issue: 6) .

S. Kaschieva (1994). Improving the radiation hardness of

MOS structures. International Journal of Electronics

Volume 76,Issue 5.

Claeys, C.,Simoen, Eddy (2002). Radiation Effects

in Advanced Semiconductor Materials and Devices.

Springer Science and Business Media, Aug 21.

G Meurant (1999). New Insulators Devices and Ra-

diation Effects. 1st Edition, Print book ISBN

9780444818010, Holland.

A. Sathish Kumar, S. D. Sharma, and B. Paul Ravindran

(2014). Characteristics of mobile MOSFET dosimetry

system for megavoltage photon beams. J Med Phys.

Jul-Sep; 39(3): 142?149.

A. Gopidaj, Ramesh S. Billima GGA, Velayudham Rama-

subramanian (2008). Performance characteristics and

commissioning of MOSFET as an in-vivo dosimeter

for high energy photon external beam radiation ther-

apy. Reports of Practical Oncology and Radiotherapy,

Volume 13, Issue 3, Pages 114-125.

BIODEVICES 2016 - 9th International Conference on Biomedical Electronics and Devices

106

Bo-Young Choe (2013). Dosimetric Characteristics of

Standard and Micro MOSFET Dosimeters as In-vivo

Dosimeter for Clinical Electron Beam. Journal- Ko-

rean Physical Society., 03/2013; 55:2566-2570.

Briere TM, et al. (2005). In vivo dosimetry using disposable

MOSFET dosimeters for total body irradiation. Med

Phys, 32:1996.

Scalchi P, Francescon P, Rajaguru P (2005). Characterisa-

tion of a new MOSFET detector conﬁguration for in

vivo skin dosimetry. Med Phys, 32(6):1571?8.

S. M. Sze (1981). Physics of Semiconductor Devices. Wil-

ley, New York, 2nd ed.

E. H. Nicollian, J. R. Brews (1982). MOS (metal oxide

semiconductor) physics and technology. Wiley, New

York.

Jay N. Zemel (1979). Nondestructive Evaluation of Semi-

conductor Materials and Devices. Nato Science Series

B, ISSN: 0258-1221.

Hughes H. L., Benedetto J. M. (2003). Radiation effects

and hardening of MOS technology devices and cir-

cuits. IEEE Trans. on Nuclear Science.

Oldham T. R., McLean F. B. (2003). Total ionizing dose

effects in MOS oxides and devices. IEEE Trans. on

Nuclear Science Vol. 50.

Adams J. R., Daves W. R., Sanders T. J. (1977). A radiation

hardened ﬁeld oxide. IEEE Trans. on Nuclear Science.

Vol. NS-24, N 6.

E.R.Davies (1997). Machine Vision: Theory, Algorithms,

Practicalities. Academic press, London.

Dubovitskiy, D. A. and Blackledge, J. M. (2008). Sur-

face inspection using a computer vision system that

includes fractal analysis. ISAST Transaction on Elec-

tronics and Signal Processing, 2(3):76–89.

Dubovitskiy, D. A. and Blackledge, J. M. (2009). Texture

classiﬁcation using fractal geometry for the diagnosis

of skin cancers. EG UK Theory and Practice of Com-

puter Graphics 2009, pages 41– 48.

Dubovitskiy D., Devyatkov V. and Richer G. (2014). The

Application of Mobile Devices for the Recognition

of Malignant Melanoma. BIODEVICES 2014: Pro-

ceedings of the International Conference on Biomedi-

cal Electronics and Devices, Angers, ISBN: 978-989-

758-013-0, Page 140, France 03 ? 06 March.

Dubovitskiy, D. A. and Blackledge, J. M. (2011). Moletest:

A Web-based Skin Cancer Screening System. The

Third International Conference on Resource Intensive

Applications and Services, vol: 978-1-61208-006-2,

pages: 22 - 29, Venice, Italy, 22 - 27 May.

Dubovitskiy, D. A. and Blackledge, J. M. (2008). Object

Detection and Classiﬁcation with Applications to Skin

Cancer Screening. International Society for Advanced

Science and Technology (ISAST) Intelligent Systems,

No. 1, Vol. 1, ISSN 1797-1802, pages: 34 ? 45.

Dubovitskiy, D. A. and Blackledge, J. M. (2012). Tar-

geting cell nuclei for the automation of raman spec-

troscopy in cytology. In Targeting Cell Nuclei for

the Automation of Raman Spectroscopy in Cytology.

British Patent No. GB1217633.5.

Dubovitskiy, D. A. and McBride, J. (2013). New ‘spider’

convex hull algorithm for an unknown polygon in ob-

ject recognition. BIODEVICES 2013: Proceedings of

the International Conference on Biomedical Electron-

ics and Devices, page 311.

Freeman, H. (1988). Machine vision. Algorithms, Architec-

tures, and Systems. Academic press, London.

Grimson, W. E. L. (1990). Object Recognition by Comput-

ers: The Role of Geometric Constraints. MIT Press.

Louis, J. and Galbiati, J. (1990). Machine vision and digital

image processing fundamentals. State University of

New York, New-York.

Nalwa, V. S. and Binford, T. O. (1986). On detecting

edge. IEEE Trans. Pattern Analysis and Machine In-

telligence, 1(PAMI-8):699–714.

Ripley, B. D. (1996). Pattern Recognition and Neural Net-

works. Academic Press, Oxford.

K.Clarke and D.Schweizer (1991). Measuring the fractal

dimension of natural surfaces using a robust fractal

estimator. Cartography and Geographic Information

Systems, (18):27–47.

K.Falconer (1990). Fractal Geometry. Wiley.

L.DeCola (1989). Fractal analysis of a classiﬁed land-

sat scene. Photogrammetric Engineering and Remote

Sensing, 55(5):601–610.

Snyder, W. E. and Qi, H. (2004). Machine Vision. Cam-

bridge University Press, England.

Y.YAGI, J.R.GILBERSON: Digital imaging in pathology:

The case for standardisation. J Telemed Telecare 11

(2005), 109–16.

Radiation Exposure Analysis in 3D Cancer Treatment

107