semiconductor pixel detectors for characterisation of

AGH University of Science and Technology

Faculty of Physics and Applied Computer Science

Engineering thesis

Semiconductor pixel detectorsfor characterisation of therapeutic

proton beams

Paulina Stasica

Medical Physics

Supervisor: dr inż. Jan GajewskiProton Radiotherapy Group

The Henryk Niewodniczanski Institute of Nuclear PhysicsPolish Academy of Sciences

Kraków, January 2020

dr inż. Jan GajewskiInstytut Fizyki Jądrowej PAN

Merytoryczna ocena pracy przez opiekuna:

Pani Paulina Stasica przygotowała pracę inżynierską, która jest elementem projektu ba-dawczego Fundacji na Rzecz Nauki Polskiej zatytułowanego „Ocena niepewności zasięgu efektubiologicznego w celu poprawy skuteczności radioterapii protonowej w Centrum CyklotronowymBronowic”, realizowanego w Instytucie Fizyki Jądrowej PAN w Krakowie.Praca inżynierska podzielona jest na trzy części: wstęp teoretyczny, część opisującą zasto-

sowane metody eksperymentalne oraz wyniki i dyskusję pomiarów i symulacji Monte Carlo.Pracę kończy rozdział z wnioskami.We wstępie teoretycznym zostały opisane formy oddziaływań wysokoenergetycznych pro-

tonów z materią oraz podstawy radioterapii protonowej, w tym rola względnej wydajnościbiologicznej i jej zależność od liniowego przekazu energii.W kolejnym rozdziale opisano zastosowanie półprzewodnikowych detektorów pikselowych

typu Timepix MiniPIX do pomiaru depozycji energii w mieszanych polach promieniowaniaindukowanych przez wiązkę protonową. Opisano dwa typy eksperymentów przeprowadzonychprzez Autorkę pracy, mających na celu zbadanie zdolności pomiaru depozycji energii przezdetektor Timepix w referencyjnych polach kwazi-monoenergetycznych wiązek protonowych orazw mieszanych polach indukowanych przez wiązki protonowe w wodzie. Dodatkowo Autorkaopisała sposób przeprowadzania symulacji Monte Carlo oraz metody analizy danych.W trzeciej części Autorka przedstawia wyniki zmierzonych depozycji energii pomiarów ka-

libracyjnych oraz pomiarów na różnych głębokościach w wodzie, wzdłuż rdzenia ołówkowejwiązki protonowej. Wyniki pomiarów porównane są z wynikami symulacji Monte Carlo oraz zdanymi tablicowymi.Praca zbudowana jest w sposób uporządkowany i spójny. Zawiera wprowadzenie, metody,

wyniki oraz dyskusję. Praca zawiera bibliografię składającą się z 31 pozycji. Oprawa typogra-ficzna jest wykonana w sposób staranny, a rysunki i wykresy są czytelne i prawidłowo opisane.Wyniki pracy zostały zawarte w streszczeniu przygotowanych przez Autorkę pracy na konferen-cję The European Society for Radiotherapy and Oncology Congresses (kwiecień 2020, Wiedeń),na której będą prezentowane w ustnym wystąpieniu.Końcowa ocena pracy przez opiekuna: 5.0

Data: 7.1.2020r. Podpis: ..................................

Skala ocen: 5.0 – bardzo dobra, 4.5 – plus dobra, 4.0 – dobra, 3.5 – plus dostateczna, 3.0 – dostateczna, 2.0 – niedostateczna

amended), is allowed to use (without renumeration and without attaining the author's consent) the work created by the student resulting from fulfilling the duties connected with his studies, as well as to make the work available to the minister in charge of higher education and science, and to make use of works located in databases kept by the minister in order to verify the thesis with the usage of Jednolity System Antyplagiatowy [the Uniform Anti-plagiarism System]. The minister in charge of higher education and science is allowed to make use of final diploma theses located in databases kept by him to the extent necessary to ensure the correct maintenance and development of these databases and IT systems working with them;

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Acknowledgements

This engineering thesis was performed in the frame of the Foundation of Polish

Science project titled: Quantification of biological range uncertainties towards an

improved patient treatment in CCB Cracow proton beam therapy centre leaded by

dr Antoni Rucinski at The Henryk Niewodniczanski Institute of Nuclear Physics

Polish Academy of Sciences.

Foremost, I would like to thank my supervisor dr inż. Jan Gajewski for providing

me with a continuous support and guidance through this work. I wish to express my

sincere thanks also to dr Antoni Ruciński, for valuable comments on this thesis.

I would like to acknowledge all the great people involved in the project: hab.

prof. Carlos Granja, PhD Cristina Oancea, prof. Angelo Schavi, dr inż. Marzena

Rydygier and doctoral students mgr inż. Jakub Baran and mgr Monika Pawlik-

Niedźwiecka.

6

Acronym List

BP Bragg peak

CCB Cyclotron Centre Bronowice

CERN European Organization for Nuclear Research

dLET Dose - averaged LET

IDD Integral depth dose

LET Linear energy transfer

MC Monte Carlo

MPV Most probably value

RBE Relative biological effectiveness

SOBP Spread-out Bragg peak

UJF Nuclear Physiscs Institute of The Czech Academy of Sciences

7

Abstract

The application of protons in radiotherapy allows to maximize dose deposition in

the tumor, while protecting normal tissue, due to depth-dose characteristics in water

or tissue of this particle type. In the case of photons, the physical dose is correlated to

the biological effect, whereas for charged particles a modifying factor, radiobiological

effectiveness (RBE), has to be applied. In proton therapy clinical practice RBE is

assumed to be a constant value of 1.1, however this assumption does not reflect the

reality. RBE value depends on different factors, as for instance particle ionization

density that can be described by linear energy transfer (LET). Development of

variable RBE-based treatment planning requires experimental validation of proton

LET in water.

In the frame of this work measurements and data analysis were performed, as

well as comparison of experimental results to Monte Carlo (MC) simulations aiming

at more precise characterisation of proton pencil beams in water. Measurements

were performed by means of compact Timepix MiniPIX semiconductor pixel detector

placed in an in-house developed PMMA waterproof detector holder used for detector

positioning in water phantom. MiniPIX chip provides information about energy

deposited by single particle, its position and direction, while penetrating the sensor.

Detector calibration in air was performed for seven proton beam nominal en-

ergies. Next, the energy depositions were measured at different positions in depth

along the beam in water. The experimental LET spectra were compared to MC

GATE simulations. A good agreement between calibration measurements and MC

simulations was observed for measurements performed at energies ranging from 70

to 200 MeV, however there is discrepancy in the case of measurements performed be-

low 70 MeV. The results of the measurements and MC simulations performed along

the proton pencil beam longitudinal profile are presented and discussed.

The software tools developed in the frame of this work will allow further analysis

of data from other measurements performed at different positions in water phantom.

Experimental validation of LET is necessary in order to implement variable RBE-

based treatment planning systems to clinical practice of proton radiotherapy.

8

Contents

Spis treści 9

1 Purpose 10

2 Introduction and research background 11

2.1 Protons interactions with matter . . . . . . . . . . . . . . . . . . . . 14

2.2 Stopping power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Linear energy transfer . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4 Relative biological effectiveness . . . . . . . . . . . . . . . . . . . . . 16

3 Methods 19

3.1 Timepix detectors and PIXet Pro software . . . . . . . . . . . . . . . 19

3.2 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2.2 Longitudinal beam LET profile measurements . . . . . . . . . 25

3.3 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4 Results and discussion 29

4.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.2 Longitudinal beam LET profile characterization . . . . . . . . . . . . 34

5 Conclusions 39

9

Chapter 1

Purpose

In clinical practice of proton radiation therapy, the RBE is commonly used to

relate biological effect of proton and photon radiation. It is usually assumed to be

constant in the irradiation region and equal to 1.1, meaning that protons are about

10% more biologically effective than photons. This assumption does not precisely

reflect the reality. The RBE is not constant and depends on many factors, such

as: treatment fractionation scheme, tissue type and endpoint, cell cycle phase and

oxygenation level as well as penetration depth and LET of particles. Especially in the

last millimeters of the proton beam range, the RBE appears to reach values higher

than 1.1, up to 1.6 [5]. This causes uncertainties in biological dose estimation in the

tumor region and a risk that protons at their residual range will damage healthy

tissue located behind the tumor, with respect to the beam direction. Therefore, in

the last two decades several variable RBE models, depending on the proton LET, as

well as energy deposition spectra of single particles have been developed. Treatment

planning systems currently used in the clinic exploit analytical algorithms for dose

distribution calculation taking into account only the constant RBE. In order to apply

variable RBE models in proton therapy, the possibility of LET calculation is needed.

The proton LET can be calculated using MC methods.

The purpose of this work was to experimentally characterize the radiation field

produced by a proton pencil beam in water in order to validate MC simulations.

For the purpose of measurements, the technology of semiconductor pixel detectors

Timepix was applied. The energy depositions in depth along the longitudinal proton

beam profile were compared to MC simulation results, which allow to evaluate energy

deposition spectra produced by mixed radiation field accounting for contributions

from different particles. Experimental validation of LET is necessary in order to

implement variable RBE-based treatment planning systems to clinical practice of

proton radiotherapy.

10

Chapter 2

Introduction and research

background

The major environmental risk of cancer is caused by unrepaired DNA damage

in cell nucleus, which lead to uncontrollable cell proliferation. Tumors formed this

way can be life-threatening. The World Health Organization states that the amount

of new cancer cases reached 18.1 millions and 9.6 million deaths were caused by this

disease in 2018 [28].

The discovery of X-rays by W. Roentgen in 1895 [23] and radioactive polonium

and radium by Marie Curie-Skołodowska in 1898 [19] laid the foundation of cancer

radiotherapy. Nowadays, radiotherapy is one of the major cancer treatment methods,

next to chemotherapy and surgery.

Absorbed dose is the excepted value of imparted energy dε per mass unit dm in

a given volume and its unit is Gray [Gy] [3]:

D =dε

dm. (2.1)

The main goal of radiotherapy is to deliver to the volume of the tumor a ther-

apeutic dose of ionizing radiation, high enough to kill the cancer cells, while mini-

mizing the dose absorbed by the surrounding healthy tissues to minimise the side

effects, like for example the induction of secondary cancer.

Internal radiotherapy method - brachytherapy uses radiation sources placed in-

side the tumor or in its close proximity in the body. In external radiotherapy (tel-

eradiotherapy), in turn, radiation sources are placed out of the patient body [20].

Conventional radiotherapy uses photon radiation to deliver the required dose

to the tumor volume. Photons ionize indirectly, producing secondary radiation in

the tissue. At the beginning, increasing production of secondary particles leads to

11

increasing deposited dose up to the point of its maximum value and then it declines

exponentially - figure 2.1 (top left) [17]. This leads to delivering the dose not only in

the tumor but also in front and behind with respect to the beam direction. Modern

radiation therapy techniques, thanks to adaptive beam delivery systems, enable to

modulate the radiation source intensity and geometrical configuration. In addition

it is conducted in the presence of image guidance, offering high dose conformity. For

instance, Intensity Modulated Radiation Therapy (IMRT) applies several radiation

fields using beams aimed at the tumor from different angles [22]. It allows to reduce

the dose deposited in healthy tissues near the tumor with respect to static beam

delivery methods like Conformal Radiation Therapy (CRT).

In 1946 Robert R. Wilson for the first time proposed to use protons to fulfill

the objectives of radiation therapy [31]. The advantage of using energetic protons,

or other charged particles, is their distribution of dose in depth and the finite range

- the so called Bragg curve - figure 2.1 (bottom middle).

Figure 2.1: Depth-dose distributions in water for different particles [22].

The amount of electric charge produced by ionizing charged particle rises, while

it loses energy. Just before the charged particle stops, there is a sudden energy loss

in BP and the amount of produced charge reaches the maximum value. The more

massive a particle is, the more pronounced dose peak is obtained. The range of a

charged particle is related to its initial energy. This allows to predict the position

12

of the BP in water or in patient body. The reason why application of protons in

radiotherapy is convenient is that the physics of their interactions with matter is

quite well understood, they produce sharp BP which falls to zero and, because of low

charge, their direction can be relatively easily changed with conventional magnets

[22].

Usually a tumor is larger than the BP width, therefore several beams with

different initial energies, forming the BPs at different depths need to be applied

forming the so called Spread-Out Bragg Peak (SOBP). SOBP is a superposition of

many beams of different initial energies, which means different BP locations, applied

in order to cover homogeneously the whole tumor volume (figure 2.2).

Figure 2.2: SOBP - solid line and component BPs - dashed lines. The superposition of severalBPs allows to deposit the majority of the dose in the tumor region, while saving healthytissues [22].

In the case of photons, the physical dose is correlated to the biological effect,

whereas for charged particles a modifying factor (RBE) has to be applied. However,

there is difficulty of understanding and predicting biological effects caused by charged

particle radiation. The necessity to find the relationship between the clinical effects

of protons and photons is due to almost a century of clinical experience gained

by photon radiotherapy. In clinical routine it is assumed that protons are 10% more

biologically effective than photons. However, the radiobiological in-vitro studies show

that the value might be underestimated [18]. The radiobiological uncertainties in

proton radiotherapy cause problems with evaluating the beam range and the healthy

13

tissues exposure to radiation, making the comparison of proton radiotherapy results

with conventional photon therapy difficult. It has been recognised that the RBE

varies with the LET of particles [5]. Figure 2.3 shows the dose (left) and dLET

(right) distributions produced by a proton pencil beam (nominal energy 150 MeV)

in water computed with MC simulations. Experimental validation of this distribution

in water, especially in terms of the dLET, is crucial in order to implement the variable

RBE-based treatment planning, in which dLET value is used, to clinical practice.

The definition of LET and dLET will be described in detail in section 2.3, whereas

RBE in section 2.4.

0 25 50 75 100 125 150 175 200Z [mm]

150

100

50

0

50

100

150

X [mm

]

0.0

0.2

0.4

0.6

0.8

1.0

Dose

norm

. []

0 25 50 75 100 125 150 175 200Z [mm]

150

100

50

0

50

100

150

X [mm

]

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Dose

-ave

raged LE

T [keV/m

]

Figure 2.3: Characterization of radiation field produced by proton beam of nominal energy150 MeV in water in terms of dose - left and dLET - right (provided by dr inż. Jan Gajewski).

2.1 Protons interactions with matter

Protons interact with matter and transfer the energy by nuclear and electro-

magnetic interactions. How the energy transfer occurs depends on the ratio of two

parameters: average distance from the center of the target atom nucleus to the

boundary of electron shells, a (dozens of pm), and the distance from the center of

the atom to the undisturbed path of the interacting particle, b (Figure 2.4).

14

Figure 2.4: Illustrated parameters a and b [8].

In the case b >> a the transferred energy is very low, but the chances of this

interaction are high, and it is responsible for approximately half of the deposited

energy along the particle path.

Stopping of the proton takes place when b ≈ a. Then it is likely that proton will

transfer energy to atomic electron and cause ionization of the atom. Deflection of

the primary particle is negligible.

When b << a proton interact with the nucleus of the atom. This mechanism

is known as multiple Coulomb elastic scattering and it is responsible for angular

spread of the beam. Heavy atoms scatter stronger than light ones.

Proton can also scatter inelastically with atom nucleus, which absorbs its energy.

Excited nucleus decays producing secondary particles and/or gamma photons. This

phenomena is relatively rare [3] [8] [22] [27].

2.2 Stopping power

The mean rate of the energy loss of a charged particle, which travels through

a matter, in other words - stopping power of the material, which is a result of

interactions with atomic electrons - is described by the Bethe - Bloch formula [10]:

15

−dEdx

= 2πNer2emec2Z2

β2

[ln

2mec2γ2β2Wmax〈I2〉

− 2β2 − 2C(β)Zt− δ(β)

], (2.2)

where dE is the energy lost along the track length dx, Z - charge of the particle

which loses the energy, β = v/c - relative speed of the particle, Zt - atomic number,

Ne - electron density of the medium, 〈I〉 - average ionization potential, me - electron

mass, re - electron radius, Wmax - maximum possible energy loss in single collision

with free electron, C - electron shielding correction (for very low energies), δ - density

effect correction (for very high energies).

To obtain the total stopping power the component from Coulomb interactions

and component from nonelastic nuclear interaction need to be taken into consid-

eration. However, from the proton radiotherapy point of view, the latter one is

negligible.

2.3 Linear energy transfer

The mean value of stopping power is described by LET:

LET =dE

dx. (2.3)

Average LET is often calculated as track average LET or dose average LET - dLET.

dLET is frequently used to evaluate the RBE in proton radiotherapy. dLET can

be calculated as the average stopping power of all particles at a given point in

a radiation field [29]. In the case of MC simulations results, as well as data from

MiniPIX detector (which provide information about every single event) analysis, the

dLET can be calculated as [9]:

dLET =

∑n

(dEdl

)dE∑

n dE, (2.4)

where n is the event index and dl is the track length for each event.

2.4 Relative biological effectiveness

RBE is used in order to describe biological effects of different radiation types. It

is defined as the ratio of the dose of a reference (photon) radiation DX to the dose

16

of a given type of radiation DT which induces an equivalent biological effect [1] [18]:

RBE =DXDT

. (2.5)

The biological dose prescribed in Gy depends on the physical dose and is scaled by

the value of RBE:

Dbiological = Dphysical ∗RBE . (2.6)

Clinical effect for given biological dose is known, but only the physical dose is mea-

surable. In conventional radiotherapy by definition:

RBE = 1 (2.7)

and

Dbiological = Dphysical . (2.8)

This enables to predict the clinical outcome by physical dose measurements in con-

ventional radiotherapy. Higher biological response to protons provides stronger ther-

apeutic effect, than in the case of photons [16]. RBE value for protons in clinical

routine is assumed to be 1.1, which means that protons are 10% more clinically

effective than photons. In fact the RBE is not constant and varies with physical and

biological parameters such as particle type, tissue type and spatially increases with

the LET [26]. As a consequence, accuracy of variable RBE-based treatment planning

depends on the proper calculation of LET values. There are many mechanistic and

phenomenological RBE models. Variable RBE is usually described as a function of

the dose, LET and tissue specific parameters (α, β). The surviving fraction of cells

is given according to the linear quadratic model (LQ model) by [30]:

SF = exp(−αD − βD2) , (2.9)

where D is dose delivered to the cells, while α and β parameters describe intrinsic ra-

diosensitivity of the cells. Taking into consideration two survival curves as a function

of dose - one for reference radiation (αX , βX) and the second for proton radiation

(αp, βp) - the RBE can be calculated as a ratio of doses for the same survival level:

RBE(Dp, αX , βX , αp, βp) =

√α2X + 4βXDp(αp + βpDp)− αX

2βXDp. (2.10)

17

α and β factors describe the tissue and biological endpoint. For protons values of

these parameters change with dLET (L), specially in the case of αp:

αp(L) = α0 + λ ∗ L . (2.11)

β dependence on dLET is negligible:

βp(L) = βX . (2.12)

Finally RBE can be described by following formula:

RBE(Dp, L, α0, λ, αX , βX) =

√α2X + 4βXDp(α0 + λL+ βXDp)− αX

2βXDp. (2.13)

This generic definition of RBE was addressed by several research groups by

fitting the results of in-vitro cell survival experiments and applying different charac-

terization methods. The consideration of different approaches to the RBE calculation

is out of the scope of this thesis. However, RBE calculation requires accurate LET

computing methods which are validated experimentally in the frame of this work.

18

Chapter 3

Methods

3.1 Timepix detectors and PIXet Pro software

Timepix is a commercial version of Medipix semiconductor detector developed

at CERN. This detectors technology is widely used in radiation research, e.g. for

ion beam therapy, radiation dosimetry, particle accelerator environments, or space

radiation detection on board the International Space Station [13]. The advantages

of these detectors are single-quantum sensitivity and particle tracking capability.

In this work MiniPIX Timepix with 300 µm thick silicon sensor was used (figure

3.1). It is compact and does not require a cooling system. MiniPIX has dimensions

77 x 21 x 10 mm and its total weight is only 25 g. It contains fully integrated data

acquisition electronics which is connected to the computer via USB port. The active

area of semiconductor sensor plane has dimensions 14.08 x 14.08 mm and is protected

by a removable cover [11] [13].

Figure 3.1: MiniPIX detector and its dimensions (picture taken by the author of this work).

19

The ionizing particle penetrating the sensitive volume of the detector produces

electric charge, which is collected by proper pixels’ electrodes (figure 3.2). Detector

bias voltage is ∼100V. PN junction is reverse-biased. A single pixel read-out elec-

tronics consists of an amplifier, an amplitude comparator and a counter. Sensitive

area is an array of 256 x 256 pixels, each has dimensions of 55 x 55 um [12] [14]. Signal

from an ionizing particle forms a cluster, which consists of many pixels (figure 3.3).

The pattern which ionizing particle leaves in the sensor depends on its direction,

track and deposited energy. The charge sharing effect i.e. charge distribution into

adjacent pixels leads to increasing thickness of the cluster at the beginning of the

acquired track.

Figure 3.2: Single pixel of MiniPIX detector chip [12] (left) and ASIC chip construction [13](right).

Figure 3.3: Illustration of particle tracking in MiniPIX detector. [13].

20

Different particles of different energies can produce clusters of various shapes and

energy distributions. Dedicated algorithms compute different cluster parameters,

such as angels α and β (presented in the figure 3.3), area, volume, height, roundness,

length in sensor plane, linearity, which potentially enable to recognize particle type

[13]. It is however challenging, as in some cases two different particles of two different

energies can produce a cluster of a similar morphology.

PIXet Pro software (by ADVACAM) allows to the control acquisition, visualize

and carry out the pre-processing of the data of energy deposition in the MiniPIX

detector [2].

3.2 Data collection

In frame of this thesis calibration of the MiniPIX with proton beams was per-

formed (section 3.2.1) and followed by measurements of LET in water phantom (sec-

tion 3.2.2). Later, the collected data were compared to MC simulations (described

in section 3.3).

3.2.1 Calibration

A part of calibration measurements were performed using the scanning pencil

beam available in gantry treatment room in Kraków proton beam therapy centre

CCB (Institute of Nuclear Physics of Polish Academy of Sciences). A Cyclotron

Proteus C-235 (figure 3.4) accelerates protons to 230 MeV. They are transported

through energy selection system that can decrease their energy down to 70 MeV.

Next, a series of bending and shaping magnets allows to transport the beam to one of

two treatment rooms equipped with rotational gantry [7]. A scanning system (Pencil

Beam Scanning technique) is mounted in the nozzle of each gantry allowing to direct

pencil beam to the tumor volume. The primary proton energy of 70 and 230 MeV

corresponds to the proton beam range in water from 42 to 318 mm. Additionally a

PMMA range shifter, mounted at the gantry nozzle can be used to obtain the proton

ranges below 42 mm.

Measurements were performed using MiniPIX camera placed in an in-house de-

signed, thin and waterproof PMMA holder (figure 3.5). It was positioned at an angle

of 45 degrees with respect to the beam direction inside a water phantom (BluePhan-

tom by IBA). The BluePhantom is equipped with step motors allowing to precisely

position the detector. Detector was connected by a USB to the computer equipped

with PIXet Pro software inside the gantry treatment room. Remote desktop allowed

to control acquisition of the data from the gantry control room. Also the position of

21

Figure 3.4: Opened cyclotron Proteus C-235 (picture taken by the author of this work).

Figure 3.5: Schematic illustration of a waterproof holder for MiniPIX detector designed forthe purpose of the measurements conducted in the frame of this project (drawings providedby dr inż.Jan Gajewski).

22

the detector in the water phantom was remotely controlled from the control room

using standard BluePhantom software tool. Lasers, typically used for pre-treatment

patient positioning, were used to position the detector sensor plane (figure 3.6).

The calibration in CCB was performed with the primary beam in air for four

nominal energies: 70, 100, 150 and 200 MeV, in order to evaluate the response of the

detector to well-defined proton fields.

Additional calibration measurements for energies lower than in CCB ie. 13, 22

and 31 MeV, were performed in Nuclear Physics Institute of The Czech Academy

of Sciences UJF. The proton beam was produced by cyclotron U-120M (figure 3.7),

which accelerates protons to maximal energy 50 MeV. Proper configuration of several

PMMA energy modulators at the end of the beam line allows to decrease the energy.

The detector was placed in a rotational holder (figure 3.8) connected to the

computer in the control room in the same way as during the experiments at CCB.

Position of the beam spot at the sensor plane was checked by means of special laser

positioner. Measurements were taken at different angles, incl. 45 degrees with respect

to the beam direction.

Figure 3.6: MiniPIX positioning in the water phantom (picture taken by the author of thiswork).

23

Figure 3.7: Cyclotron U-120M in Nuclear Physics Institute CAS in Prague (picture takenby an author of this work).

Figure 3.8: MiniPIX positioning during measurements (picture taken by an author of thiswork).

24

3.2.2 Longitudinal beam LET profile measurements

Longitudinal beam LET profile measurements were performed in CCB after

filling the phantom with water. Detector was positioned the same way as during cal-

ibration, in the room isocentre (figure 3.9). The proton beam of the cyclotron beam

current 1 nA and the nominal beam energy 150 MeV corresponding to the position

of the BP in water at 156.61 mm was used. Seven measurements were performed on

different depths in water: 30, 120, 145, 149, 153, 157, 161 mm.

Figure 3.9: Experimental setup during measurements in water (picture taken by an authorof this work).

3.3 Simulations

The MC simulation were performed with Gate toolkit in version 8.2, which was

an interface to Geant4 version 10.4.p2 MC engine [25]. The QGSP BIC HP EMY

physics list was used. The validated physical beam model used clinically at CCB,

describing the energy, energy spread and the lateral propagation of the beam was

used [24]. The detector was simulated as a 300µm thick and 14×14mm size slice

of pure silicon positioned at 45◦ with respect to the beam direction. During the

simulations the total energy deposition of each particle in the detector region were

25

scored as well as the type and the angle of the particle impinging the detector

surface. Number of particles was 106 for each simulation. Figure 3.10 presents MC

simulation scene.

Figure 3.10: MC simulation scene with the detector (yellow) and the proton tracks (blue).

3.4 Data analysis

Detector registers a particle, which penetrates the sensor, as a cluster of pixels.

The signal in each pixel corresponds to the energy deposited in each pixel. Figure

3.11 shows an example frame from the detector in PIXet Pro software (proton beam

nominal energy was 150 MeV, measurement at 3/4 of the beam range, ie. 117 mm,

and 6 cm away from the beam core). Pre-processing of the raw data from the detector

was performed by means of PIXet Pro track processing tool. The output file was a

list of clusters from each measurement with 21 parameters for each cluster including

deposited energy, β angle (presented in the figure 3.3), position of the cluster centre

in the sensor and length of the cluster in the sensor plane.

26

Figure 3.11: An example frame form the MiniPIX in PIXet Pro software. There are clustersproduced by different particles. For instance long, winding truck can be produced electron,while perpendicular heavy particle will produce round big cluster. Proton beam nominalenergy was 150 MeV, measurement at 3/4 of beam range, ie. 117 mm, and 6 cm away fromthe beam core (picture taken by the author of this work).

Due to the lack of effective particle identification tool there was a problem with

evaluating different particles’ contribution to radiation field produced by the proton

beam. On the other hand the MC simulations provide information about each energy

deposition in Timepix 300 µm silicon sensor such as position in the sensor, value

of deposited energy, type and direction of the interacting particle. Based on this it

was possible to recognize the peak from protons in energy spectra from Timepix

measurements by comparing it with the spectra from proper simulation results.

To quantify the energy deposition spectra a fitting function was applied in order

to obtain MPV. The Landau distribution describes fluctuations of energy loss by a

charged particle in a thin layer of matter. Landau probability density function is

asymmetric and has a long tail, because of the decreasing number of collisions, in

which larger amounts of energy are being deposited [15] and corresponds well to the

received data. Landau curve was fitted to the simulation and the measurement LET

27

spectra in the range, which corresponds to peak produced by protons.

Next the analysis of the longitudinal beam profile in terms of the LET was

performed. Taking into consideration position (depth and angle) of the detector

and the location of the cluster center in the sensor for each cluster the real depth

was calculated. Then all seven measurements were merged. Length of each particle

track in the sensor was calculated basically from Pythagoras’ theorem, knowing the

length of the cluster in sensor plane and the sensor thickness. LET was calculated

by dividing deposited energy in one cluster by the length of the track (equation

2.3). Then on every 2 mm (if there was enough amount of clusters) LET spectra

was calculated and the MPV of protons’ LET was obtained. Counts (number of

clusters) were normalised to the amplitude of the Landau fit. Values of the dLET

were calculated according to the equation 2.4. Longitudinal profile of MPV of LET,

as well as dLET in the silicon, was made and compared with integral deph dose

(IDD). IDD is calculated from measurements or simulations of dose absorbed in a

plane-parallel ionization chamber [6].

28

Chapter 4

Results and discussion

4.1 Calibration

Figures 4.1-4.3 show the LET spectra of the calibration measurements for the

beam nominal energies 13, 22 and 31 MeV measured at UJF. In turn, figures 4.4-4.7

show calibration LET spectra for higher beam nominal energies, i.e. 70, 100, 150 and

200 MeV measured at CCB. Table 4.1 presents values of LET MPV for measurement

and simulation results compared to data from PSTAR [21] and figure 4.8 illustrates

these calibration data.

Simulation results well correspond to PSTAR data. A discrepancy between ex-

perimental and simulation results for lower energies measured at UJF is observed.

The reason for this may be that the beam does not seem to be monoenergetic, espe-

cially for beam nominal energies 13 and 22 MeV, where the discrepancy is the most

significant. In the case of cyclotron U120-M the beam energy is degraded by means

of PMMA plates inserted in the beam at the end of the beam line in the experi-

mental room. No additional magnetic energy selector is used. Therefore the beam

energy will be blurred more than in CCB where a dedicated energy selector system

with bending magnets is used. Moreover, a simple beam model with a monoenergetic

beam was used for the simulations of the lower energies. There is a need to prepare

a new beam model/simulation setup to better reproduce the beam at U-120M cy-

clotron (UJF). In the case of higher energies measured in CCB (figures 4.4-4.7) the

beam was accurately modeled in the MC simulations (uncertanity of about 2%) and

the energy spread of the beam was < 0.8%. Therefore the measurement data, the

simulation results, as well as the data from PSTAR data base [21] are consistent.

See table 4.1 and figure 4.8 for cumulative presentation of the data.

29

0 2000 4000 6000 8000 10000 12000 14000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Coun

ts (n

orm

alize

d)

Meas. mpv=8839.52 eV/ mSim. mpv=7125.21 eV/ m

Figure 4.1: Calibration LET spectra for beam nominal energy 13 MeV.

0 2000 4000 6000 8000 10000 12000 14000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Coun

ts (n

orm

alize

d)



30

0 2000 4000 6000 8000 10000 12000 14000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Coun

ts (n

orm

alize

d)



0 1000 2000 3000 4000 5000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Coun

ts (n

orm

alize

d)



31

0 1000 2000 3000 4000 5000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Coun

ts (n

orm

alize

d)



0 1000 2000 3000 4000 5000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Coun

ts (n

orm

alize

d)



32

0 1000 2000 3000 4000 5000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Coun

ts (n

orm

alize

d)



Table 4.1: Values of LET MPV for measurement and simulation results compared to datafrom PSTAR [21].

LET [eV/µm]Beam nominalenergy [MeV] Experimental Simulation PSTAR

13 8839.52 7125.21 6581.7522 5130.31 4379.43 4366.8831 3740.55 3281.16 3335.1370 1600.72 1642.41 1773.30100 1194.33 1222.92 1359.67150 860.53 881.58 1020.80200 689.53 711.33 844.96

33

25 50 75 100 125 150 175 200

Nominal energy [MeV]

1000

2000

3000

4000

5000

6000

7000

8000

9000

LET

[eV/

m]

Sim.Meas.PSTAR

Figure 4.8: MPV of LET for calibration measurements compared to simulation results anddata from PSTAR [21].

4.2 Longitudinal beam LET profile characterization

Figure 4.9 shows example LET spectra for 4 selected depths: 31, 117, 145 and

157 mm. Based on the simulation results it can be concluded that mostly protons

were registered by the sensor. Considerable amount of clusters for very low energy

values as well as for energy values higher than the MPV appears in measurement

results. The clusters of very low energy might be produced by the noises of the

electronics, which is not modeled in the MC simulations, while the clusters of higher

energies are probably caused by an overlapping effect. This effect occurs when 2 or

more particles produce clusters which are so close to each other that they overlap and

are recognized by the PIXet Pro software as one cluster of larger energy deposition.

Bragg curve compared to the longitudinal beam profile of the LET MPV values

is shown in the figure 4.10, while in the figure 4.11 it is compared to beam dLET

profile.

The LET spectra measured by means of MiniPIX for the purpose of radiotherapy

applications need to be converted from silicon (material of detector sensor) to water

(tissue equivalent). The conversion was performed using the following formula [4]:

log(LET∞H2O) = −0.2902 + 1.025 log(LET∞Si) . (4.1)

34

Figure 4.12 presents an example LET spectra in water. Figures 4.13 and 4.14

show LET MPV and dLET longitudinal beam profiles in water compared to Bragg

curve.

There is a good agreement of measurement and simulation results up to the

BP for the LET MPV profiles. However, some discrepancy occurs in deeper regions.

Proton LET spectra for simulations seem to be slightly wider (for example figure

4.9, 157 mm of depth) and moved towards higher values. Another reason for that

could be high gradient at the distal edge of the BP and limited accuracy of the

MiniPIX positioning, as well as possible different detector response for higher LET

particles. In the case of dLET profiles there is also some discrepancy closer to the

surface, which need further analysis to survey the reason for that.

0 1000 2000 3000 4000 5000 6000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

31.0 mm of depth

Sim. for all particles

Sim. for p + mpv=972.79 eV/ mMeas. mpv=970.52 eV/ m

0 1000 2000 3000 4000 5000 6000 7000 8000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

117.0 mm of depth


Sim. for p + mpv=1617.17 eVMeas. mpv=1603.84 eV

0 2000 4000 6000 8000 10000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

145.0 mm of depth


Sim. for p + mpv=2660.67 eVMeas. mpv=2582.60 eV

0 2000 4000 6000 8000 10000 12000 14000 16000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Counts

(norm

aliz

ed)

157.0 mm of depth



Figure 4.9: Example LET spectra (in silicon) for four selected depths: 31 mm, 117 mm, 145mm and 157 mm. Beam nominal energy was 150 MeV and the measurements were performedin the beam core.

35

0 25 50 75 100 125 150 175

depth [mm]

1

2

3

4

5

6

MPV

of L

ET [k

eV/

m]

MeasurementSimulation

0.0

0.2

0.4

0.6

0.8

1.0

IDD norm. [-]

Dose

Figure 4.10: Beam LET profile for measurements and simulations (in silicon) compared toBragg curve.

0 25 50 75 100 125 150 175

depth [mm]

2

4

6

8

10

dLET

[keV

/m

]


0.0

0.2

0.4

0.6

0.8

1.0

IDD norm. [-]

Dose

Figure 4.11: Beam dLET profile for measurements and simulations (in silicon) compared toBragg curve.

36

0 500 1000 1500 2000 2500 3000 3500 4000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

117.0 mm of depth



0 500 1000 1500 2000 2500 3000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

31.0 mm of depth



0 2000 4000 6000 8000 10000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

145.0 mm of depth



0 2000 4000 6000 8000 10000 12000

LET [eV/ m]

0.0

0.2

0.4

0.6

0.8

1.0

Counts

(norm

aliz

ed)

157.0 mm of depth



Figure 4.12: Example LET spectra for 4 selected depths: 31, 117, 145 and 157 mm. LETvalues were converted from silicon to water. Beam nominal energy was 150 MeV and themeasurements were performed in the beam core.

0 25 50 75 100 125 150 175

depth [mm]

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

MPV

of L

ET [k

eV/

m]


0.0

0.2

0.4

0.6

0.8

1.0

IDD norm. [-]

Dose

Figure 4.13: Beam LET profile for measurements and simulations compared to Bragg curve.LET values were converted from silicon to water.

37

0 25 50 75 100 125 150 175

depth [mm]

1

2

3

4

5

6

7

dLET

[keV

/m

]


0.0

0.2

0.4

0.6

0.8

1.0

IDD norm. [-]

Dose

Figure 4.14: Beam dLET profile for measurements and simulations compared to Bragg curve.LET values were converted from silicon to water.

38

Chapter 5

Conclusions

In order to properly account for the RBE variation as a function of LET in

treatment planning it is essential to characterize the particle field produced by pro-

ton therapeutic beams. The Timepix detectors technology enables to characterise

experimentally mixed radiation field produced by protons directly in water. The

purpose of this work was to characterize the particle field produced by proton pencil

beam at different positions in water to validate MC simulations.

The calibration measurements for proton radiation fields and preliminary LET

distribution measurements in water were performed and compared to the state-of-

the-art MC simulations. There is a good agreement between calibration measure-

ments and MC simulations for 70-230 MeV beams, however there is a discrepancy

for lower measured energies caused probably by unspecified energy distribution for

the experiments in Prague.

The longitudinal beam profile analysis show the discrepancy of the LET between

the measurements and simulations results, especially in the BP region. The simulated

LET seems to be moved towards higher values, than the results from MiniPIX. More

measurements were performed, than it is presented in this work and some on-going

analysis reveals the same behavior at some distance from the beam core.

Development of a software tool for identification of different particle types mea-

sured is needed. The MiniPIX detector is single quantum sensitive and enables mea-

surement of deposited energy per pixel. The features of cluster, e.g. height, linearity,

total deposited energy or roundness characterize the incident particle and are the

basis of particle identification methods that have been already developed by AD-

VACAM. These tools were not yet validated for mixed radiation field produced by

therapeutic protons’ beams and does not work for the data collected in this work.

Development of particle identification methods will enable the detailed investigations

of LET spectra for different particle types.

39

The tools prepared in the frame of this work will allow to perform further

analysis of data from measurements at different positions in the water phantom.

The experimental validation of the MC simulations with MiniPIX will eventually

improve biological modeling in proton therapy.

This work and further measurements in water were awarded by an oral talk

at The European Society for Radiotherapy and Oncology Congresses ESTRO 2020,

held in Vienna, Austria from 3 to 7 April 2020, titled Timepix for characteriza-

tion of mixed radiation field produced in proton radiotherapy (authors: Paulina Sta-

sica, Jakub Baran, Jan Gajewski, Carlos Granja, Cristina Oancea, Monika Pawlik-

Niedźwiecka, Marzena Rydygier, Angelo Schavi, Antoni Ruciński).

40

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semiconductor pixel detectors for characterisation of

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