SAHLGRENSKA ACADEMY

QUANTIFICATION OF INTERNAL

MOVEMENTS AND THE REPRODUCIBILITY

OF DEEP INSPIRATION BREATH HOLDS

Emelie Wingqvist

Thesis: 30 hp

Program: Medical Physics Programme

Level: Second Cycle

Semester/year: Autumn 2017

Supervisor: Fredrik Nordström, Anna Karlsson, Magnus Gustafsson

Examiner: Magnus Båth

Abstract

Thesis: 30 hp

Program: Medical Physics Programme

Level: Second Cycle

Semester/year: Autumn 2017

Supervisor: Fredrik Nordström, Anna Karlsson, Magnus Gustafsson

Examiner: Magnus Båth

Keywords:

Respiratory gated radiotherapy, deep inspiration breath hold,

diaphragm

Purpose: The aim of this study was to quantify internal anatomy variations in thorax for radiotherapy (RT)

treatments in free breathing (FB) and deep inspiration breath holds (DIBH).

Background: Respiratory gated radiotherapy in combination with DIBH has the potential to reduce tumour

motion and to increase the distance between tumour and organs at risk when treating cancers in

the thoracic region, e.g. lung cancer and mediastinal Hodgkin’s lymphoma. Still, DIBH in

combination with advanced delivery techniques (e.g. volumetric modulated arc therapy and

intensity modulated proton therapy) has, due to motion uncertainties, been restricted. Motion

during dose delivery with these techniques can result in so-called interplay effects.

Method: Materials used in this study were multiple 2D and 3D images of three patient groups and healthy

volunteers. Due to a large amount of available data from breast cancer patients, the data in this

study mainly consists of these patients. Two groups of left sided breast cancer patients treated

during different RT techniques were compared; one group treated during FB and the other treated

during gated RT with DIBH. Image registration was performed in order to evaluate inter- and

intra-fractional movement of the diaphragm, where the spine was used as registration landmark.

The cranio-caudal movement of the diaphragm top was evaluated using a rigid intensity-based

image registration in MATLAB on the spine at the level of the diaphragm. Image registration of

2D and 3D images of Hodgkin’s lymphoma patients treated during gated RT with DIBH was

also performed using both rigid and deformable registration in Raystation 6 software.

Cine MR imaging of healthy volunteers was also performed in order to compare the movement

of the diaphragm with different breathing techniques (thorax respiration, abdominal respiration

and free breathing). A surrogate to the respiratory gating system used in RT treatment was

obtained and used during imaging.

Results: The mean standard deviations of the diaphragm movement of the breast cancer patient groups

were comparable. The diaphragm movement of the DIBH gated Hodgkin’s lymphoma patients

was of the same magnitude as the breast cancer patient groups. The cine MRI showed that

abdominal respiration causes a larger diaphragm movement in the cranio-caudal direction when

compared to thorax respiration and FB.

Conclusions: No significant difference in the diaphragm movement between patients treated during DIBH and

FB could be found in this study. If the DIBH technique is applied to the RT treatment and the

tumour is located close to the diaphragm the margins should not be reduced without other

interventions.

Populärvetenskaplig sammanfattning

Cancer är en av de mest förekommande sjukdomarna i världen och den näst vanligaste dödsorsaken.

Sjukdomen kan behandlas på olika sätt men av alla cancerfall behandlas över hälften med strålterapi.

När man behandlar cancrar som är placerade i bröstkorgen, som exempelvis lungcancer, måste man ta

hänsyn till att cancern kan röra på sig när man andas under strålbehandlingen. Detta kan göras genom

att bestråla ett större område eller genom användning av olika tekniker som gör att tumören får en

minskad rörelse, vilket kallas andningsanpassad strålbehandling. Vissa typer av andningsanpassad

strålbehandling har också fördelen att organ som är känsliga för strålning (riskorgan) kan besparas på

stråldos då avståndet mellan tumör och riskorganet ibland kan ökas.

Osäkerheter i rörelse hos tumören och den interna anatomin i bröstkorgen mellan behandlingstillfällen

vid andningsanpassning har förhindrat kombinationen av andningsanpassad strålbehandling med mer

avancerade strålbehandlingstekniker. Konventionella strålbehandlingstekniker kan ta hänsyn till interna

rörelser genom användning av utökade marginaler. Detta fungerar dock inte med de avancerade

strålbehandlingsteknikerna och rörelse under behandling med dessa tekniker kan leda till att hela

tumören inte får den dos som den har ordinerats, vilket i sin tur kan leda till sämre prognos för patienten.

Ett exempel på en andningsanpassad strålbehandlingsteknik kallas deep inspiration breath hold (DIBH)

och innebär att strålningen levereras till tumören medan patient håller andan efter en djup inandning.

Genom användning av olika hjälpmedel kan patientens andning övervakas, t.ex. med hjälp av ett system

som använder en box som placeras ovanpå bröstkorgen och vars rörelse följs av en videokamera. När

patienten sedan andas följer boxen med andningsrörelsen och när boxen når en förbestämd höjd

levereras strålbehandlingen automatiskt. Boxen kallas i detta fall för ett externt tumörsurrogat och med

denna metod antas det finnas ett samband mellan rörelsen hos tumören och det externa tumörsurrogatet.

Detta samband är svårt att verifiera.

I denna studie har det undersökts huruvida diafragmatoppens placering ändras mellan olika

behandlingstillfällen. Två patientgrupper som diagnostiserats med vänstersidig bröstcancer har jämförts;

en grupp som behandlas under friandning och en grupp som behandlats med den andningsanpassade

tekniken DIBH. En mindre grupp med patienter diagnostiserade med Hodgkins lymfom som behandlats

med DIBH-tekniken är också inkluderad i denna studie. Olika andningstekniker har också studerats med

hjälp av bildtagning med en magnetkamera. Andningsteknikerna som undersökts är friandning, andning

med magen (bukandning) och andning med bröstet (thoraxandning).

Resultatet visar att diafragmatoppen, i medeltal, rör sig ungefär lika mycket hos de båda utvärderade

bröstcancerpatientgrupperna mellan olika behandlingstillfällen. Spridningen mellan patienterna i

gruppen som behandlats under friandning är dock mindre. Resultatet visar att tumörens placering inte

får en minskad osäkerhet, trots att DIBH tillämpas. Resultatet från bildtagning med magnetkamera visar

att diafragmatoppens position påverkas mycket av vilken andningsteknik som används och att

bukandning möjligtvis kan vara fördelaktigt om cancern är nära diafragmatoppen då avståndet mellan

tumör och hjärta kan ökas.

Table of content

1. Introduction ......................................................................................................................................... 1

2. Theory ................................................................................................................................................. 2

2.1. Treatment planning volumes and margins ................................................................................... 2

2.2. Respiratory gated radiotherapy .................................................................................................... 3

3. Materials and methods......................................................................................................................... 5

3.1. Estimation of inter-fractional motion variations using multiple 2D kV images .......................... 5

3.2. Estimation of internal motion variations using cine 2D MR imaging .......................................... 8

3.3. Estimation of internal motion variations using multiple 3D CT images ...................................... 9

4. Results ............................................................................................................................................... 10

4.1. Internal motion estimation using frontal 2D kV images ............................................................ 10

4.1.1. Breast cancer patients .......................................................................................................... 10

4.1.2. Hodgkin’s lymphoma patients ............................................................................................. 14

4.2. Estimation of internal motion variations using cine 2D MR imaging ........................................ 14

4.3. Estimation of internal motion variations using multiple 3D CT images .................................... 18

5. Discussion ......................................................................................................................................... 19

6. Conclusion ......................................................................................................................................... 21

7. Acknowledgements ........................................................................................................................... 22

8. Reference list ..................................................................................................................................... 23

1

1. Introduction

Cancer is one of the most common diseases worldwide and a leading cause of death [1]. The cancer

disease affecting most people is lung cancer with an estimated number of over 1.82 million cancer cases

in 2012 and where men is the most affected group [2]. The most common cancer disease among women

is breast cancer with an estimated number of over 1.67 million cases in 2012 [2]. A not as common

cancer disease is Hodgkin’s lymphoma but with an incidence peak in young adults [3].

A common denominator among the above-mentioned cancer diseases is that they all manifest in the

thoracic region. The treatment of the cancer diseases can differ, although radiotherapy (RT) is a common

method of treatment. Over 50 percent of all cancer patients are treated with RT [4] tending to cure,

moderate pain or increase life expectancy. Since thorax contains vital organs such as heart and lungs,

RT treatment brings risks and may result in more or less severe consequences, e.g. cardiovascular

diseases [5] [6].

Internal motion in the thorax region may be caused by respiratory motion and cardiac motion [7] [8]. In

RT, internal movements of tumour(s) and of organs at risk (OARs) during dose delivery are highly

undesirable and to compensate for the motions increased margins are added [9]. From the clinical target

volume (CTV) an internal margin is added, forming the internal target volume (ITV). Using an ITV,

motions due to respiration are taken into account [9]. However, having a larger treatment volume to gain

high tumour control probability (TCP) may limit the goal of acceptable normal tissue complication

probability (NTCP). Therefore, the risk of radiation induced morbidity may limit the prescribed tumour

doses [9].

In order to adapt the treatment to the motion caused by respiration, a respiratory gating system (RGS)

has been suggested. Relating the tumour position to the respiration, e.g. a certain phase of the respiration

or to a breath hold, enables irradiation during more favourable conditions. This may be when the tumour

has reduced movements and where the distance between the tumour and OARs is increased. Hence,

intra- and inter-fractional movements of tumours located in the thorax might be reduced when using an

RGS, at least in theory. Respiratory gating techniques include phase and amplitude gating. This study

includes left sided breast cancer patients treated with amplitude gating during deep inspiration breath

hold (DIBH). Studies on possible internal anatomy variations when using the DIBH technique are

limited which in turn has restricted the combination of DIBH with advanced delivery techniques (e.g.

volumetric modulated arc therapy and intensity modulated proton therapy). Tumour motion during dose

delivery with an advanced dynamic delivery technique may result in interplay effects with hot and cold

spots in the tumour. If the tumour does not receive the dose required to attain the treatment aims, the

patient prognosis might be affected. The aim of this study was to quantify inter-fractional internal

anatomy variations in thorax for RT treatments in free breathing (FB) and DIBH.

2

2. Theory

2.1. Treatment planning volumes and margins

When treating malignant tumours with RT the aim is to obtain local tumour control [10]. In order to

obtain local tumour control it is of importance to irradiate both the tumour and possible subclinical

spread of the tumour. In some cases, RT can be an adjuvant treatment to surgery since the remaining

tissue after a surgery can hold subclinical spreading. It is important to have definitions of the volumes

to know what is included in each volume prior to the treatment planning and later to be able to report

the accurate doses [9]. Accurate dose reporting is also essential for clinical studies.

ICRU has defined the volumes used in RT, below are four of them

(also shown in Figure 1):

1) Gross tumour volume (GTV)

2) Clinical target volume (CTV)

3) Internal target volume (ITV)

4) Planning target volume (PTV)

In ICRU Report No. 50 the GTV is defined as the “palpable or visible/demonstrable extent and location

of malignant growth” [10]. The GTV can sometimes be detected in computed tomography (CT) images

but to find the exact location and the extent of the GTV other imaging techniques might be required,

e.g. PET, MRI, and SPECT etc. The CTV includes the GTV and microscopic malignant spreading. If

the gross tumour has been surgically removed the CTV consists of the possible subclinical spreading.

CTV can also include volumes at risk such as connected lymph nodes.

In the PTV, all geometric uncertainties have been taken into account, this to make sure the CTV is

receiving the prescribed dose. This margin is an extension from the CTV and different uncertainties, for

example due to movement of tissue, are being taken into account. The margin between the CTV and the

PTV is divided into two parts; internal margin (IM) and setup margin (SM). IM is used to account for

tumour position variations of CTV and variations of the size and shape of CTV. The CTV and the

internal margin is called internal target volume (ITV). In SM, variations in patient positioning in relation

to the beam are taken into account. IM and SM together with CTV form the PTV (PTV =

CTV+ √IM2 + SM2) [10], where the equation applies when IM and SM are random and independent

of each other.

PTV

ITV

CTV

GTV

Figure 1. Schematic image of tumour

volumes used in RT.

3

The CTV to PTV margin can be reduced by optimisation of the patient immobilisation and the patient

set-up [10]. Knowledge of the tumour location may also reduce dose delivery uncertainty and thus the

margins. Patient immobilisation includes fixation devices such as head and shoulder masks, deformable

vacuum bags with polystyrene beads, knee/ankle supports, arm supports, chest board, stereotactic

frames etc. These devices improve the reproducibility of the patient set-up and the inter-fractional

motion. External markers on the patients (e.g. tattoos) or on the immobilisation devices are used when

positioning the patient on the linac couch. Imaging devices are used to verify the patient position prior

to the treatment and enable further correction of the position, two examples are electronic portal imaging

devices (EPID) and linac mounted imaging devices (LMID). EPID acquires images using a detector and

the megavolt (MV) beam from the linac. The high energy makes the soft tissue contrast poor, though

internal fiducials are more easily seen. LMID contains an X-ray tube and flat panel detector mounted

on the linac and enables X-ray images and cone-beam CT (CBCT) images by rotating the LMID 360

degrees.

Intra-fractional motion during dose delivery are usually caused by unavoidable organ motion. Intra-

fractional motion due to respiration may be reduced by the use of a respiratory gating technique as

described below.

2.2. Respiratory gated radiotherapy

The tumour location and the breathing technique are both affecting the motion of the tumour. During

respiration the tumour may move 1-2 cm and even larger movements have been reported [10]. In order

to account for motion due to respiration during dose delivery in radiotherapy a respiratory gating method

can be used. Respiratory gating has the potential to reduce tumour motion and to increase the distance

between tumour and organs at risk. There are two kinds of respiratory gating methods; phase gating and

amplitude gating. The latter mentioned method is the main gating method used at Sahlgrenska

University Hospital and involves delivery of RT when an external tumour surrogate reaches an

amplitude within predefined lower and upper thresholds, also called the gating window (an example

may be seen in Figure 2). Set-up images acquired prior to treatment delivery are acquired in gated mode

as well.

Figure 2. Example of a breathing waveform and the placement of the gating window during a deep inspiration breath hold.

4

To account for motion during dose delivery, the ability of tracking the tumour position is desirable.

External tumour surrogates and internal fiducials may be used for this purpose [10]. In this study, images

of patients treated during gating with external tumour surrogates have been used. This method relies on

a motion correlation between the surrogate and the tumour, yet the motions do not always correlate [10].

A benefit with external surrogates is that it is a noninvasive method. Examples of external surrogates

are infrared (IR) markers and measurements of lung volume [10]. An invasive method is implanted gold

seeds, which are placed proximate to the target.

Varian Medical Systems uses an RGS called Real-time Position Management (RPM) and this system

employs an IR camera (Figure 3 a) to track the movement of IR markers on a plastic box (Figure 3 b)

placed on the abdomen [10]. The amplitude and period of the marker box motion is obtained and when

the marker box is within the gating window specified during treatment simulation, the beam is on. Elekta

uses an RGS called Active Breathing Coordinator (ABC) which uses a mouthpiece to control the airflow

to the patient and to measure the inhaled air volume [9]. After the patient has attained the breath hold

the airflow is turned off while the radiation is delivered. The lung air measurements ensure the same

lung air volume at each breath hold, although it has been suggested that active breathing control brings

increased geometric uncertainties since forced breath holds may be uncomfortable for the patients [11].

Figure 3. a) IR-camera with IR-diodes, b) plastic box with IR reflecting markers.

(a) (b)

5

3. Materials and methods

Materials used in this project were two-dimensional (2D) frontal set-up kV images from RT treatment

of two groups of patients diagnosed with left sided breast cancer. One group was treated during visually

guided voluntary DIBH and the other group was treated during FB. Repeated three-dimensional (3D)

CT-images acquired during DIBH from patients diagnosed with mediastinal Hodgkin’s lymphoma and

2D frontal set-up kV images from the treatment of these patients were used as well.

The 2D set-up images were acquired for all the fractions using the orthogonal on-board kV imaging

device on the linac (Clinac iX, Varian Medical System, Palo Alto, CA, USA) to visually verify the

position of the patient while in DIBH. The RGS used was Real-time Position Management system

(RPM, Varian Medical System Inc.). For reproducible DIBHs, the patients at Sahlgrenska University

Hospital are guided during the reference CT-imaging to perform thorax respiration to an individual level.

The same image exposure settings for the set-up kV images were used throughout the course for each

patient. The CT images of the patients with Hodgkin’s lymphoma were collected during visually guided

voluntary DIBH prior to the treatment start and during treatment to control the reproducibility of the

DIBH. All of the images were acquired according to the clinical routine at Sahlgrenska University

Hospital. Internal anatomy variations were evaluated by estimating the inter-fractional motion

variations.

3.1. Estimation of inter-fractional motion variations using multiple 2D kV images

All of the images were evaluated in MATLAB (version R2017a, Mathworks Inc., Natick USA). Frontal

set-up images from 20 left sided breast cancer patients treated with the DIBH-technique, 20 left sided

breast patients treated during FB and four Hodgkin’s lymphoma patients were included in the study and

images from all fractions where the diaphragm was visible were used. Variations in position of the top

of the diaphragm, i.e. the movement of the diaphragm top, in the cranio-caudal (cc) direction were

evaluated.

In order to compare position variations of the diaphragm the caudal part of the spine in the images was

registered to a reference image. The reference image was in this case defined as the first image in the

treatment series with a visible diaphragm. The first step in the image registration was to apply image

processing by adjustment of the histograms. The histogram window width and window centre were

adjusted and the bins were evenly distributed between these to leave only the spine visible in the image.

The histogram adjustment was set individually for each patient. To obtain a registration of the caudal

part of the spine, a segmentation was performed by cropping the lower half of the spine from the image

and placing it in a zero-filled matrix to retain the same image size (768x1024 pixels).

A rigid image registration, allowing rotation and translation, on the spine between the reference image

and all the subsequent images was then performed by using intensity-based automatic image registration

in MATLAB. One-plus-one evolutionary optimizer and Mattes mutual information metric were used to

align the images. The transformation between the reference image and the evaluated image was

estimated using the MATLAB function imregtform and the transformation was then applied on the

whole evaluated image by using imwarp. A composited image with both the reference image and the

evaluated, both applied with contrast limited adaptive histogram equalization (CLAHE) filter, was

obtained with the function imshowpair to visualize the quality of the registration, see Figure 4.

6

Figure 4. Example of an image registration, the green coloured image is the reference image and the magenta coloured image

is the evaluated image.

A visual verification was made to verify that the registration was correct at the spine in the level of the

diaphragm using the composited image and also by comparing the individual images. Registrations

deviating more than half a vertebra in the level of the diaphragm was considered incorrect and was not

included in the result.

To estimate the relative variations of the position of the top of the diaphragms, a rectangular region of

interest (ROI, with a width of 13.6 mm) was placed over the left diaphragm, approximately over the

highest point of the diaphragm, see Figure 5. A mean value of all the pixels in each row of the ROI was

then calculated, leaving a vector of the mean pixel intensity in the y direction. The derivative between

each pixel of the obtained vector was calculated and the top of the diaphragm was defined to be at the

position of the maximum derivative, see Figure 6. The obtained position was then visually verified in

the image, since some of the cropped images contained ribs and other structures giving larger

derivatives. The ROI was individually placed for each patient and the same ROI was used within each

of the images, for the same treatment plan.

7

Figure 5. Example of the placement of the ROI over the diaphragm.

Figure 6. The derivative between each pixel is shown in the figure. The maximum derivative defines the diaphragm top.

8

The standard deviation (σ and 2σ) of the diaphragm positions for each patient was obtained using Excel

(Microsoft Office Excel 2013) and the function stdev.p, which is using Equation 1 to calculate the

standard deviation:

𝜎 = √∑(𝑥 − �̅�)2

𝑛 (1)

Where 𝑥 is the diaphragm position, �̅� is the mean diaphragm position and 𝑛 is the number of diaphragm

positions.

The number of used diaphragm positions for each patient (denoted data points) was noted. For the DIBH

gated group the DIBH amplitude (the lower level of the gating window) was obtained for each patient.

A mean value of all standard deviations from all the patients and a standard deviation of all the calculated

individual standard deviations were obtained. An epidemiological table comparing different parameters

(age at treatment, number of fractions and number of fields) between the DIBH gated and FB group was

also acquired. A two-sample t-test was performed in order to test the null hypothesis that the diaphragm

movement of two groups comes from normal distributions with equal means. This was done using

MATLAB function ttest2 which returns either h = 1 or h = 0, the first mentioned alternative if the null

hypothesis is rejected (at 5 % significance level) by the test, or vice versa for the second alternative. A

p-value was also obtained.

3.2. Estimation of internal motion variations using cine 2D MR imaging

A feasibility study comparing different breathing techniques for one healthy volunteer was done using

cine 2D MR imaging. Cine sequences (balanced steady-state free precession, 2D TrueFISP) of sagittal

slices containing the lower half of the left lung and the abdomen was obtained using an MRI scanner

(MAGNETOM Aera 1.5 T, Siemens Medical Solutions USA, Inc.) and a flex body coil. Prior to the

imaging, the cine sequence was obtained and optimized by imaging of another healthy volunteer. A

wooden tongue depressor was attached to the body coil in order to control the breathing amplitude, see

Figure 7. The lower end of the stick was in contact with the healthy volunteer in the level of the xiphoid

process when an amplitude of 1.2 cm from baseline was reached.

During cine 2D MR imaging with sequences lasting 22 seconds (13.6 images/second), the healthy

volunteer performed DIBH breathing with two different techniques; thorax respiration and abdominal

respiration. FB was also performed. The diaphragm top in the expiration phase was compared to the

diaphragm top in the inspiration phase/in the breath hold for each technique, this to obtain a

measurement of the maximal movement of the diaphragm in the cc direction. The location of the

diaphragm in relation to the heart was visually compared between the different techniques. Intra-

fractional motion variations during the breath hold were also investigated for thorax- and abdominal

respiration by comparing the position of the diaphragm in the beginning of the breath hold and the last

image of the breath hold. This comparison was done using MATLAB and the function imfuse, which is

similar to the function imshowpair and makes a composited image of two images. The position of the

diaphragm top was determined as in the previous section.

9

Figure 7. The body coil with the attached wooden tongue depressor.

3.3. Estimation of internal motion variations using multiple 3D CT images

Estimation of internal motion variations in multiple 3D CT images of a patient diagnosed with

mediastinal Hodgkin’s lymphoma was done using RayStation 6 software (RaySearch Laboratories,

Stockholm, Sweden) and a biomechanical deformable image registration (DIR) developed from the

Morfeus algorithm [12]. The benefit of using this algorithm is that it takes into account that tissues have

different properties. Bones, for example, cannot be deformed and therefore a rigid registration of bones

are performed prior to the DIR.

All of the 3D CT images were acquired during DIBH. The registration process included a grey level

based registration prior to the DIR. The first CT was defined as the reference image and all subsequent

images were registered to this. The deformed images were evaluated by obtaining the deformation vector

field, displaying the magnitude of the motion variations between the CT scans.

10

4. Results

4.1. Internal motion estimation using frontal 2D kV images

A summarising table of the results of internal motion estimation using 2D images is shown below, see

Table 1. A mean standard deviation of each group, including the minimum and maximum standard

deviation, of the inter-fractional diaphragm movement (in cc direction) are presented in the table. The

same results are shown in the following paragraphs in further detail.

Table 1. The mean standard deviation of each group, including the minimum and maximum standard deviation, of the cc

diaphragm movement are shown in the table.

�̅� [mm] σmin [mm] σmax [mm]

Breast cancer (DIBH) 7.4 2.2 19.2

Breast cancer (FB) 7.1 3.7 10.6

Hodgkin’s lymphoma (DIBH) 6.7 3.6 8.6

4.1.1. Breast cancer patients

The results of the standard deviation (σ and 2σ) of the inter-fractional diaphragm movement in the cc

direction from the left sided breast cancer patients treated during DIBH gating (patient A to T, denoted

with ‘BC,DIBH’) are shown in Table 2. The number of data points, i.e. the number of images used when

calculating the standard deviation, is shown. The DIBH amplitude, meaning the amplitude of the box

placed on the patient’s chest during irradiation, is also shown. The average deviation of the diaphragm

position varied from 2.2 to 19.2 mm. The mean standard deviation of the gated group was 7.4 mm and

the dispersion of all individual standard deviations was 3.9 mm.

Table 2. Results of the standard deviation of the diaphragm position in the cc direction for the gated group. The number of

data points used when calculating the standard deviation and the DIBH amplitude is also shown.

Patient Number of data

points DIBH amplitude

[cm] σ [mm] 2σ [mm]

ABC,DIBH 12 1.2 7.1 14.3

BBC,DIBH 10 1.1 7.7 15.5

CBC,DIBH 5 1.6 19.2 38.5

DBC,DIBH 4 1.1 2.2 4.5

EBC,DIBH 5 1.6 13.5 26.9

FBC,DIBH 10 1.8 8.4 16.8

GBC,DIBH 7 1.0 3.9 7.7

HBC,DIBH 4 0.9 6.7 13.5

IBC,DIBH 9 1.3 2.7 5.5

JBC,DIBH 7 1.3 10.4 20.8

11

KBC,DIBH 4 1.1 2.4 4.8

LBC,DIBH 3 1.3 4.1 8.2

MBC,DIBH 6 1.0 8.5 17.0

NBC,DIBH 11 1.3 9.6 19.2

OBC,DIBH 7 1.5 5.7 11.4

PBC,DIBH 16 1.4 6.3 12.5

QBC,DIBH 6 1.2 9.0 18.1

RBC,DIBH 4 1.4 8.5 17.0

SBC,DIBH 15 1.0 5.3 10.7

TBC,DIBH 7 1.6 7.4 14.7

In Table 3, the results of the standard deviation (σ and 2σ) of the inter-fractional diaphragm movement

in the cc direction of the breast cancer patient group treated during FB (patient A to T, denoted with

‘BC,FB’) are shown. The number of data points used to calculate the standard deviation is also shown

in the table. The individual standard deviations of this group varied from 3.7 to 10.6 mm. The mean

standard deviation was similar to the DIBH-gated group, 7.1 mm. The two sample t-test of the mean

diaphragm movement of the two groups gave a p-value of about 0.76, displaying that there is no

significant difference between the two breast cancer patient groups. The dispersion of all individual

standard deviations was 2.1 mm.

Table 3. Results of the standard deviation of the diaphragm position in the cc direction for the non-gated group. The number

of data points used when calculating the standard deviation is also shown.

Patient Number of data points σ [mm] 2σ [mm]

ABC,FB 3 3.7 7.4

BBC,FB 6 9.4 18.7

CBC,FB 11 7.1 14.2

DBC,FB 16 5.9 11.8

EBC,FB 13 6.6 13.3

FBC,FB 8 9.1 18.1

GBC,FB 8 7.2 14.4

HBC,FB 25 7.9 15.9

IBC,FB 10 6.1 12.1

JBC,FB 5 3.8 7.6

KBC,FB 5 3.7 7.4

LBC,FB 6 6.2 12.5

MBC,FB 14 8.7 17.3

NBC,FB 8 10.6 21.2

OBC,FB 14 9.3 18.5

PBC,FB 8 6.0 11.9

QBC,FB 10 10.3 20.6

RBC,FB 3 5.5 11.0

SBC,FB 4 6.3 12.7

TBC,FB 13 9.0 17.9

12

Table 4 contains patient and treatment characteristics for both of the breast cancer patient groups (called

DIBH and FB in the table). The mean age at treatment was 52 (range of 38-70) and 55 (range of 39-70)

years for the DIBH and FB group. The mean number of treatment fractions was 19.4 (16 and 33) and

16.9 (16-25) respectively. The mean number of irradiation field was 5.3 (2-9) and 3.3 (2-7) respectively.

The same linear accelerator and RGS were used within the groups. All of the patients were treated during

2016 and 2017.

Table 4. Patient and treatment characteristics for both of the breast cancer patient groups.

DIBH FB

Age at treatment [years] 52 (38-70) 55 (39-70)

Number of treatment fractions 19.4 (16, 33) 16.9 (16-25)

Number of irradiation fields 5.3 (2-9) 3.3 (2-7)

Figure 8 shows the standard deviation of the inter-fractional diaphragm movement (in cc direction) as a

function of the DIBH amplitude, for the DIBH-gated breast cancer patient group including the

Hodgkin’s lymphoma patients who were treated during DIBH as well (data from Table 5). There was

no clear relationship between the standard deviation and the DIBH amplitude, although the standard

deviation of the diaphragm movement slightly increased with an increased DIBH amplitude.

Figure 8. The DIBH amplitude is plotted against the standard deviation.

13

Figures 9 and 10 show the normal probability plot of the breast cancer patient groups (DIBH and FB).

The sample data is the individual standard deviation of the inter-fractional diaphragm movement in the

cc direction. If the data set derives from a normal distribution the data should be located proximate to

the straight red line. Both of the groups appeared to be normally distributed, although some outliers in

the normal probability plots were observed.

Figure 9. Normal probability plot for the breast cancer patient group treated during DIBH.

Figure 10. Normal probability plot for the breast cancer patient group treated during FB.

14

4.1.2. Hodgkin’s lymphoma patients

The results of the standard deviation of the diaphragm position in the cc direction of the Hodgkin’s

lymphoma patients treated during DIBH are shown in Table 5. The number of data points used when

calculating the standard deviations and the DIBH amplitude are also shown. The standard deviation

varied from 3.6 to 8.6 mm, the mean standard deviation was 6.7 mm and the dispersion of all individual

standard deviations was 1.9 mm.

Table 5. Results of the standard deviation of the diaphragm position in the cc direction for the Hodgkin’s lymphoma patients.

The number of data points used when calculating the standard deviation and the DIBH amplitude are also shown.

Patient Number of data points DIBH amplitude σ [mm] 2σ [mm]

AHL,DIBH 14 1.6 8.6 17.1

BHL,DIBH 3 1.5 3.6 7.2

CHL,DIBH 9 1.3 8.1 16.1

DHL,DIBH 5 1.7 6.7 13.4

4.2. Estimation of internal motion variations using cine 2D MR imaging

A summarising table of the results from the 2D cine MR imaging of a healthy volunteer can be observed

below. The movement of the top part of the diaphragm under the left lung in sagittal slices was studied.

The largest movement of the diaphragm between expiration and inspiration/breath hold and the intra-

fractional movement during breath holds for three different breathing techniques (FB, thorax respiration

and abdominal respiration) are shown. The largest movement of the diaphragm in cc direction was 14.8,

50.5 and 77.2 mm respectively. The intra-fractional diaphragm movement for thorax and abdominal

respiration was 8.9 mm for both breathing techniques.

Table 6. The largest diaphragm movement between expiration to inspiration and the intra-fractional movement of the

diaphragm during a breath hold are shown.

Diaphragm movement (from expiration to inspiration)

[mm]

Intra-fractional diaphragm movement (during breath hold)

[mm]

Free breathing 14.8 -

Thorax respiration 50.5 8.9

Abdominal respiration 77.2 8.9

15

In Figure 11, internal variations in the lungs during free breathing can be observed. Image (a) shows the

end expiration phase, (b) shows the end inspiration phase and (c) is a composited image of (a) and (b)

to visualize the internal differences. Between (a) and (b) the diaphragm top moves about 14.8 mm.

Figure 11. Internal anatomy variations during free breathing. a) Expiration b) Inspiration c) Composited image of (a)

(magenta coloured image) and (b) (green coloured image).

Figure 12 shows internal anatomy variations during thorax respiration. Image (a) shows the end

expiration phase, (b) shows the DIBH and (c) is a composited image of (a) and (b) to visualize the

internal differences. Between the different phases the top of the diaphragm moves about 50.5 mm. It can

be observed that the posterior part of the diaphragm has a larger deformation and the movement may be

larger in this region.

Figure 12. Internal anatomy variations during thorax respiration. a) Expiration b) DIBH c) Composited image of (a)

(magenta coloured image) and (b) (green coloured image).

(a) (b) (c)

(a) (b) (c)

16

Intra-fractional internal anatomy variations during the (15 seconds long) DIBH at thorax respiration can

be observed in Figure 13. Image (a) shows the internal anatomy during the first part of the breath hold

while (b) shows the last image of the breath hold in the current sequence. In image (b) the heart has

moved in the slice direction and is no longer visible. Between image (a) and (b) the diaphragm top

moves about 8.9 mm and the movement may be larger in other parts of the diaphragm.

Figure 13. Intra-fractional internal anatomy variations during thorax respiration. a) DIBH b) DIBH (last image in sequence)

c) Composited image of (a) (green coloured image) and (b) (magenta coloured image).

Figure 14 shows internal anatomy movements during abdominal respiration. Image (a) shows the end

expiration phase, (b) shows the DIBH at abdominal respiration and (c) is a fused image of (a) and (b).

As in the cases shown above, the posterior part of the diaphragm has an increased deformation. Between

the different phases the diaphragm top moves about 77.2 mm.

Figure 14. Internal anatomy variations during abdominal respiration. a) Expiration b) DIBH c) Composited image of (a)

(magenta coloured image) and (b) (green coloured image).

(a) (b) (c)

(a) (b) (c)

17

Figure 15 shows the intra-fractional internal anatomy variations during the DIBH at abdominal

respiration. Image (a) shows the internal anatomy during the first part of the breath hold while (b) shows

the last image of the breath hold in the current sequence. Between image (a) and (b) the diaphragm top

moves about 8.9 mm and the movement may be lager in other parts of the diaphragm.

Figure 15. Intra-fractional abdominal respiration a) Breath hold b) Breath hold (last image in sequence) c) Composited image

of (a) (magenta coloured image) and (b) (green coloured image).

(a) (b) (c)

18

4.3. Estimation of internal motion variations using multiple 3D CT images

The results from the 3D DIR of CT images acquired during DIBH are shown below. Four CT images of

a patient diagnosed with Hodgkin’s lymphoma were included. Table 7 shows the largest inter-fractional

movement of the left part of the diaphragm between the first acquired CT (CT1) and the following three

CTs (CT2, CT3 and CT4). Unlike the previous results presented above, the diaphragm movement is

now in 3D. The standard deviation of the diaphragm movement was 4.7 mm.

Table 7. The largest inter-fractional movement of the diaphragm between CT1 and CT2-CT4.

Largest movement of

diaphragm [cm]

CT1 – CT2 0.89

CT1 – CT3 1.25

CT1 – CT4 1.00

Figure 16 shows an example of the deformation vector field after the DIR between CT1 and CT2. The

image is of a coronal slice.

Figure 16. The deformation vector field in a coronal slice is shown in the image. CT 2 has been registered to CT 1.

19

5. Discussion

Inter-fractional and intra-fractional internal anatomy movement, in particular the movement of the

diaphragm, have been studied in this study. The magnitude of the diaphragm movement was found to

be comparable between the two groups (DIBH and FB).

As shown in Table 1 the mean standard deviation of the diaphragm movement between the two breast

cancer patient groups, DIBH and FB, were comparable to each other; 7.4 mm and 7.1 mm. Since the

standard deviations in each group appeared to be normally distributed (Figure 9 and 10), a dispersion of

all the individual standard deviations was calculated. The dispersions (1σ) of all individual standard

deviations of the two groups were 3.9 mm and 2.1 mm. The individual standard deviations of the

diaphragm movement ranged from 2.2 to 19.2 mm in the DIBH-group and from 3.7 to 10.6 mm in the

FB-group. As shown in Table 5 and Table 7, the mean standard deviation of the diaphragm movement

of the Hodgkin’s lymphoma patients was comparable to those for the breast cancer patient groups. The

results imply that the use of DIBH does not necessarily minimize the diaphragm movement uncertainty,

though the range of the standard deviations show that DIBH might be suitable for some patients (for

example patient DBC,DIBH, IBC,DIBH and KBC,DIBH) with consistent breath holds. This in turn leads to that if

the tumour is located close to the diaphragm top, the margins should not be reduced when using DIBH

without application of other interventions.

The mean age at treatment of the breast cancer patient groups was 52 and 55 years for the DIBH-group

and the FB-group, respectively (Table 4), and the other characteristics were comparable to each other.

However, in the gated group some of the patients had a higher number of treatment fractions (33

fractions), the increased number of fractions may affect the results since it may be more difficult to

repeat the same DIBH over a longer period of time. The patient with the largest standard deviation

(CBC,DIBH) also had a large amount of treatment fraction (33 fractions).

Possible explanations to the results might include that the diaphragm and chest movement have a weaker

correlation than expected. Another possible explanation to the results may also include that some

patients may sometimes enter the gating window and the breath hold from the upper threshold while

other times enter it from the lower threshold. This may result in that the set-up images have been

acquired at different amplitudes since the images are acquired instantly when entering the gating

window. Even though the use of DIBH might not minimize the inter-fractional movement of the

diaphragm top, and consequently not the margins and the irradiated volume, the method can still be

useful for other purposes. The internal geometry at DIBH can decrease the absorbed dose to OARs.

It can be observed that the standard deviation of the diaphragm movement slightly increased with an

increased DIBH amplitude (Figure 8). A study by Koivumäki et al. [13], who investigated the

geometrical uncertainty of the heart position in relation to the PTV for breast cancer patients treated

during DIBH, found that the patients with a higher DIBH amplitude (13-20 mm) had an increased

reproducibility of the relative heart and PTV position. They also found that the variations in the distance

between the diaphragm and left lung apex were decreased for patient with a higher DIBH amplitude. In

this study the breath hold was monitored with an optical chest surface monitoring system and the PTV

was in the left breast, making both the RGS and investigated anatomy differ from what is used and

studied in this study, and might be the explanation to why the results are different.

20

As shown in the normal probability plots, Figure 9 and 10, the diaphragm movement for both of the

breast cancer patient groups are approximately normally distributed. No significant difference in the

diaphragm movement between the two groups could be found (p ≈ 0.76).

A study by Scherman Rydhög et al. suggests that the diaphragm is not optimal as a surrogate when

investigating motion of tumours in the mediastinum (e.g. lymph nodes), where motion of lymph nodes

are mainly caused by cardiac motion [8]. The results does not necessarily apply to the investigated

groups, breast cancer and Hodgkin’s lymphoma patients, since the tumour(s) is located in mediastinum

or in the chest. The application of DIBH might in these cases bring minimized tumour movement

uncertainties. A tumour in the chest has presumably a stronger movement correlation with the external

tumour surrogate (i.e. the plastic box). The cine MR Imaging used in this study could be used in the

future to further investigate the movement correlation among different organs.

The 2D cine MR imaging showed that the diaphragm position is highly dependent on the respiration

technique. During FB (Figure 11) the top of the diaphragm moved about 14.8 mm (in cc direction)

between end expiration and end inspiration. In Figure 12 where the DIBH was accomplished with thorax

respiration, the diaphragm top moved about 50.5 mm (in cc direction). Between the beginning and the

end of this DIBH, the intra-fractional movement of the diaphragm top was about 8.9 mm. In Figure 14

where the DIBH was attained with abdominal respiration, the diaphragm top moved about 77.2 mm (in

cc direction) and in this case the intra-fractional movement was also about 8.9 mm (Figure 15). The

images show that the diaphragm may deform during respiration and that the deformation is not

continuous, which in turn leads to smaller/larger movements in different parts of the diaphragm. As

earlier mentioned in the discussion, it can be observed that the chest wall has a decreased movement in

comparison with the diaphragm top.

At Sahlgrenska University Hospital the patients treated with the DIBH technique are guided during

reference CT-imaging to perform reproducible DIBHs. The patients are informed to perform thorax

respiration and to facilitate the breath hold the patients are informed to tighten/pull in the abdominal

muscles. The latter mentioned step may push the diaphragm in a cranial direction, which can be observed

in the MRI cine. If there is a desire to increase the space between targets located proximate to the

diaphragm and OARs (e.g. the heart), the use of abdominal respiration might be favourable.

The set-up images used in this study includes both the spine and the diaphragm to be able to make the

registration and to determine the diaphragm position. Set-up images that did not include both structures

in the FOV were therefore not included in the evaluation. Regarding the breast cancer patient, of a total

of 726 images, about 32 percent were excluded since the images did not contain both structures and

about 21 percent were excluded due to incorrect registration. The exclusion of images not containing

both structures may have resulted in an underestimation of the standard deviations. Due to deformation

of the diaphragm during respiration it is possible that the movement is increased in other parts of the

diaphragm. Depending on the deformation, the diaphragm top in the reference image may move outside

the measuring ROI in other images, this may have resulted in a different part of the diaphragm being

measured.

It is important to keep in mind that the data sets are relatively small and to obtain statistical significance

larger data sets are probably needed. In the section “Internal motion estimation using frontal 2D kV

images” 40 breast cancer patients and four Hodgkin’s lymphoma patients and a total of 373 images were

included while one healthy volunteer was included in the other part of this study.

21

6. Conclusion

An estimation of the inter-fractional movement of the diaphragm between treatment fractions using

DIBH and FB has been done. The influence of different breathing techniques (FB, thorax respiration

and abdominal respiration) on the inter- and intra-fractional movement of the diaphragm has also been

investigated using cine MRI. No significant difference in the diaphragm movement between the two

breast cancer patient groups could be found. The results imply that DIBH does not reduce the inter-

fractional position variation of the diaphragm top when compared to FB and therefore the margins may

not be reduced if the tumour is located close to the diaphragm. Possible reasons might be that the

monitoring of the tumour surrogate on the thorax has a weaker correlation to the tumour motion than

expected.

22

7. Acknowledgements

I would like to thank my supervisors Fredrik Nordström, Anna Karlsson and Magnus Gustafsson for

your help and guidance through this work.

I would also wish to thank Maja Sohlin for your help with the MRI acquisition!

23

8. Reference list

[1] L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, J. Lortet-tieulent, and A. Jemal, “Global Cancer

Statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 65, no. 2, pp. 87–108, 2015.

[2] J. Ferlay et al., “Cancer Incidence and Mortality Worldwide: Sources, Methods and Major

Patterns in GLOBOCAN 2012,” International Journal of Cancer, vol. 136, no. 5, pp. E359–

E386, 2015.

[3] A. Smith et al., “Lymphoma Incidence, Survival and Prevalence 2004-2014: Sub-type

Analyses From the UK’s Haematological Malignancy Research Network,” British Journal of

Cancer, vol. 112, no. 9, pp. 1575–1584, 2015.

[4] R. Baskar, K. A. Lee, R. Yeo, and K.-W. Yeoh, “Cancer and Radiation Therapy: Current

Advances and Future Directions,” International Journal of Medical Sciences, vol. 9, no. 3, pp.

193–199, 2012.

[5] S. C. Darby et al., “Risk of Ischemic Heart Disease in Women after Radiotherapy for Breast

Cancer,” New England Journal of Medicine, vol. 368, no. 11, pp. 987–998, 2013.

[6] D. Lundstedt et al., “Symptoms 10-17 Years After Breast Cancer Radiotherapy Data From the

Randomised SWEBCG91-RT Trial,” Radiotherapy and Oncology, vol. 97, no. 2, Elsevier

Ireland Ltd, pp. 281–287, 2010.

[7] Y. Sppenwoolde et al., “Precise and Real-Time Measurement of 3D Tumor Motion in,”

International Journal of Radiation Oncology, Biology, Physics, vol. 53, no. 4, pp. 822–834,

2002.

[8] J. Scherman Rydhög et al., “Target Position Uncertainty During Visually Guided Deep-

Inspiration Breath-Hold Radiotherapy in Locally Advanced Lung Cancer,” Radiotherapy and

Oncology, vol. 123, no. 1, pp. 78–84, 2017.

[9] E. B. Podgorsak, Radiation Oncology Physics: A Handbook for Teachers and Students.

Vienna: International Atomic Energy Agency, 2005.

[10] P. Metcalfe, The Physics of Radiotherapy X-Rays and Electrons. Madison Wisconsin: Medical

Physics Publishing, 2007.

[11] M. Josipovic et al., “Geometric Uncertainties in Voluntary Deep Inspiration Breath Hold

Radiotherapy for Locally Advanced Lung Cancer,” Radiotherapy and Oncology, vol. 118, no.

3, Elsevier Ireland Ltd, pp. 510–514, 2016.

[12] M. Velec et al., “Evaluation of Biomechanical Deformable Image Registration (DIR) in a

Commercial Radiation Therapy Planning System,” International Journal of Radiation

Oncology, Biology, Physics, vol. 93, no. 3, pp. S215–S216, 2015.

[13] T. Koivumäki, J. Tujunen, T. Virén, J. Heikkilä, and J. Seppälä, “Geometrical Uncertainty of

Heart Position in Deep-Inspiration Breath-Hold Radiotherapy of Left-Sided Breast Cancer

Patients,” Acta Oncologica, vol. 56, no. 6, pp. 879–883, 2017.