ap-6-10.dvi


Acta Polytechnica Vol. 50 No. 6/2010

A Morphing Technique Applied to Lung Motions in Radiotherapy:
Preliminary Results

R. Laurent, J. Henriet, R. Gschwind, L. Makovicka

Abstract

Organ motion leads to dosimetric uncertainties during a patient’s treatment. Much work has been done to quantify
the dosimetric effects of lung movement during radiation treatment. There is a particular need for a good description
and prediction of organ motion. To describe lung motion more precisely, we have examined the possibility of using a
computer technique: a morphing algorithm. Morphing is an iterative method which consists of blending one image into
another image. To evaluate the use of morphing, Four Dimensions Computed Tomography (4DCT) acquisition of a
patientwas performed. The lungswere automatically segmented for different phases, andmorphingwas performed using
the end-inspiration and the end-expiration phase scans only. Intermediate morphing files were compared with 4DCT
intermediate images. The results showed good agreement between morphing images and 4DCT images: fewer than 2 %
of the 512 by 256 voxels were wrongly classified as belonging/not belonging to a lung section. This paper presents
preliminary results, and our morphing algorithm needs improvement. We can infer that morphing offers considerable
advantages in terms of radiation protection of the patient during the diagnosis phase, handling of artifacts, definition of
organ contours and description of organ motion.

Keywords: organ motion, morphing, 4DCT.

1 Introduction
Organ motion leads to dosimetric uncertainties dur-
ing a patient’s treatment. Much work has been done
to quantify the dosimetric effects of lung movement
during radiation treatment. In particular, important
work is being done on describing and predicting or-
gan motion. Four Dimensions Computed Tomogra-
phy (4DCT) can easily be performed using Varian’s
Real-timePositionManagementSystem (RPM)with
slices acquired throughout the respiratory cycle. A
reflective marker is fixed on a box positioned on the
chest of the patient. The process is rather compli-
cated, because deformation does not follow the same
speed and amplitude at every point of a given or-
gan. Furthermore, in the lungs, hysteresis must be
considered: the motion in expiration and inspiration
is not symmetrical [44, 45]. We did not account for
hysteresis in this preliminary study.
We had to find a mathematical definition to de-

scribe the movement. We chose to use the morphing
technique, because it permitted both evaluation and
interpolation of movement. Morphing is a computer
sciencewhich consists of blending one image into an-
other. Turning an image at the beginning of inspi-
ration into an image at the end of the inspiration
may therefore provide a precise description of lung
motion. Intermediate files obtained with morphing
were very close to the 4DCT intermediate images.
One of the most important advantages of morphing

over 4DCT is the fact that the organ position can be
precisely known with less exposure to radiation. For
this preliminary study, we chose to study the mo-
tions of several lungs. We also wish to stress that
these results do not yet account for all aspects of or-
gan motion. Nevertheless, the results are promising
and encourage further complex studies. Our morph-
ing program has to be further refined before it can
be offered for routine use in radiation treatment.
The first section of this paper begins with a brief

review of organ motion in radiotherapy, followed by
an overview of the simulation of lung motion, as well
as anoverviewofmorphingalgorithmsand toolsused
inmedicine. The second sectionof thepaperpresents
the material and method used for image acquisition,
and also themorphing algorithm. Finally, the results
are presented and discussed in the third section.

1.1 Organ Motions in radiotherapy

Conformal radiotherapy shapes the treatment fields
to conform to the target geometry in three dimen-
sions. However, the use of a single 3DCT (Three Di-
mensionsComputedTomography)data set for treat-
ment planning assumes that the singleCT represents
the mean positions of the target organ and of any
Organs At Risk (OAR). In reality, the received ab-
sorbed dose may differ from the planned absorbed
dose. Either the dose coverage of the targeted vol-
ume is insufficient or there is an over-dosage of the

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Acta Polytechnica Vol. 50 No. 6/2010

surrounding normal tissue. The motion of the tu-
mour volume is traditionally accounted for by theuse
ofmargins according to InternationalCommission on
Radiation Units and Measurements (ICRU) reports
50 and 62 [14, 15]. The margin used for treatment
between the Gross Tumour Volume (GTV) and the
Planning Target Volume (PTV) is divided into two
parts:
• Internal Margin (IM) to account for Clinical
TargetVolume (CTV) size and shape variations,

• Setup Margin (SM) to account for uncertainties
in patient positioning and beam alignment. The
SM is determined either empirically or by mea-
suring the magnitude of organ motion during
respiration.
Many organ motion studies are available in the

literature. Generally, they relate to the thoracic or
abdominal organs [15], and have used variousmotion
observation techniques: an ultrasound scanner [11],
digital fluoroscopy [33], a gamma scintillation cam-
era [43], or a magnetic method [1]. The purposes of
these observationsaremultiple: to improve imageac-
quisition [11, 43], to investigate field margins [33], or
to examine effects on organs at risk [1]. Not all imag-
ing modalities are appropriate to all tumour sites.
Sixel et al. [33] recommend CT scanner use.
Low et al. [18] studied themotion of a pulmonary

tumour with scanner modality and observed that
between two breathing extreme ranges one part of
the tumour followed a rotational displacement, and
the other part followed a stretching displacement.
They concluded that a simple one-dimensional rigid-
motion model of this tumour would be unable to ac-
curately reflect its motion during the patient’s respi-
ratory cycle. Other authors [4, 19] have noted that
tumours move in translation and simultaneously dis-
tort their shape.
Many models and studies exist concerning or-

gan motion prediction. For example, Schweikard et
al. [32] studied organdeformation from amechanical
point of view (tissue elasticity) and created a math-
ematical model, and Heath et al. [13] considered the
deformation of a voxel. We decided to explore the
possibility of using morphing to simulate organ mo-
tion.

1.2 Simulation of lung motions

The aim of simulating lung motion is to minimize
radiation exposure due to 4DCT and to help trace
the accurate motion of the lung. This knowledge
of motion may allow reduced margins to be used
in Treatment Planning Systems (TPS) in radiother-
apy.
Many works exist on the simulation of lung mo-

tions, eachwith its ownmethod. Villardet al. [41, 42]
developed a method based on the Mechanics of Con-

tinuousMedia lawswith resolutionbyfinite elements.
Thismethodusespatientdataandbiomechanicalpa-
rameters to generate a customized simulation of lung
motion.
Boldea et al. [5, 6] estimated lung deformation

with an algorithm of non-rigid registration. The
implemented algorithm blends one image into an-
otherwithout dimension restriction: the objects rep-
resented in the two images may not be represented
according to the same dimensions. The two tech-
niques were compared and data similar to a 4DCT
was created. This method was used for all respira-
tory motions [48].
Santhanam et al. [30] simulated the motions of a

surface lung with a tumour to have a model work-
ing in real time. The model takes as its input a
subject-specific 4DCT of lungs and computes a de-
formable lung surface model by estimating the de-
formation properties of the surface model using an
inverse dynamics approach.
However precise the above techniques may be,

they are too cumbersome for clinical application as
they are based on complicated computations.

1.3 Morphing for medicine

This section summarizes the applications of morph-
ing to medicine.
The purpose of morphing is to blend one image

into another. Both of the images represent the same
object and use the same structure [29].
Morphing presents many potential uses in

medicine. J. Talairach and P. Tournoux [36] pre-
sented a way to use morphing in order to create a
universal cerebral atlas. Others, e.g. J. R. Mor-
inglane [22], designed some morphing algorithms
to locate precise points in the brain. Other work
has compared and merged data from different pa-
tients or has drawn up statistics of distortions.
P. M. Thompson and A. W. Toga used morphing to
study Alzheimer’s disease [37, 39]. E. Stindel used
morphing to simulate the evolution of trauma to the
patella [34]. B.P.Bergeron et al. [3] developed appli-
cations for educationbasedonmorphing thatareable
to generate variations in medical images. K. Penska
et al. [25] also used morphing for education and di-
agnosis by turning radiographic images into a movie
that demonstrates the healing process of a humerus
fracture. Other examples and approaches were re-
ported by J. Montagner et al. [21].
As presented in Fig. 1, J. B. A. Maintz and

M. A. Viergever distinguished four main families of
transformations [20]:
1. rigid transformations are simple translations
and rotations;

2. affine transformations retain parallelism of im-
age lines;

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Acta Polytechnica Vol. 50 No. 6/2010

Fig. 1: The four families of transformations throughmor-
phing

3. projection transformations retain image lines
but not necessarily their parallelism;

4. non-linear transformations turn lines into cur-
ves.
Non-linear transformationsareusuallyapplied for

medical issues. There are three categories ofmethods
for this kind of transformation [23]:
(i) intensity-based methods are based on a global
approach over the different grey colour levels.
These methods are very complex. Intensity-
based methods propose a more global approach.
They are based on minimisation of energy,
which describes the degree of similarity between
the source and the target morphing images.
J.-P. Thirion [38] presented a kind of regula-
tion using iterative screening. During this reg-
ulation, he drew the outlines of the object in
a first image, and then he let the second im-
age filter through the first one. M. Bro-Nielsen
andG.Grambowadopted the same approach [7]
adding a linear elasticity parameter. C. Da-
vatzikos [10]went further, using the elastic prop-
erties of a material. G. E. Christensen added
speediness (fluid model) to elasticity [8] and
defined constraints using parametric equations.
Other multiscale approaches were presented by
H. Lester and S. R. Arridge [17]. These ap-
proaches implemented either multiple decompo-
sition of the images or the distortions. These
transformations are fulfilled according different
parameters. The former consisted in solving
the transformation of one version of the image
into another version and repeating it until the
end. The latter, unusually, consists in consid-
ering successive distortions with a higher and
higher degree of freedom.

(ii) Feature-based methods propose less complex al-
gorithmsand faster calculations. Thosemethods
are based on geometrical transformations [47].
Various geometrical methods have been imple-
mented, such as automatic and semi-automatic
search and manual search of critical points [26,
40]. The search of crest lines, curve points and

shape curves are also geometrical methods that
have been implemented [35]. Methods based on
the use of models which describe areas are also
geometrical methods. These models allow one
to roughly extract the most important anatom-
ical structures, and then to refine them [28].
A. L. Didier et al. [12] and D. Sarrut et al. [31]
simulated lung motion using geometrical trans-
formationsbasedoncontinuousmechanical laws,
whichwere solved by the finite elementmethod.
This approach takes into account the influence
of the organs around the lungs that are respon-
sible for their motion and for limiting their mo-
tion: the ribs, the diaphragm, all their associ-
ated muscles and the pleura. H. Atoui et al. [2]
also implemented a feature-based algorithm to
construct missing intermediate lung slices;

(iii) Hybrid methods have also been implemented by
L. Collins and A. C. Evans [9].

2 Material and method
This sectionpresents thematerials andmethods that
are used. In the first part, we described the way the
patient datawas acquired, and in the secondpart,we
discuss themorphing algorithm that has been imple-
mented.

2.1 Acquisition of patient data

With the respiratory gating radiotherapy technique,
various tools were developed to register the patient’s
breathing pattern. Many techniques exist, only two
ofwhich areusedby the hospital teamwithwhichwe
collaborate: theReal-timePositionManagement sys-
tem, (RPM, Varianmedical systems, PaloAlto, CA)
and the spirometer system. RPM consists of placing
a block with two reflective markers on the patient’s
abdomen in front of a video camera which uses an
infrared signal to register the motion of this block.
This technique is used in 4D-PET (4 Dimensions-
Positron Emission Tomography) and 4DCT-scan ac-
quisitions [24, 27], often in free breathing mode.
The other technique is the spirometer system, which
is often used in breath-hold techniques. The pa-
tient’s air flow circulates in a spirometer through the
mouth [48]. A nose-clip prevents the patient from
nasal breathing.
A patient was set up on the 4DCT (GE Light-

speed 16-slice scanner, GE Medical systems, Wauke-
sha, WI) lying in the treatment position, suspended
in an alpha cradle, his arms folded above his head.
The patient’s data was registered in the free breath-
ing cine-mode: the scans were acquired at each table
location to ensure a complete sampling of data for
one respiratory cycle. The scanning technique used

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Acta Polytechnica Vol. 50 No. 6/2010

120 kVp and 400mAwith slices 2.5mm in thickness.
This processwas repeateduntil the entire thoraxwas
scanned (160 couch positions). The RPM was used
to obtain the retrospective 4DCT. These 4DCT im-
ageswere sortedaccording to the respiratorypattern.
Each 3DCT-scan represents 3D anatomic informa-
tion at a certain phase. In this study, 4DCT-scans
were sorted into ten equal periods. As represented in
Fig. 2, 0 % phase corresponds to the end of inspira-
tion and 50 % phase to the end of expiration.

Fig. 2: Respiratory cycle and selection process achieved
by 4DCT

Fig. 2 describes the principles of an acquisition
performed during one patient’s breathing cycle [33].
The respiratory signal and the data are acquired at
the same time in order to be correctly sorted. Each
3D-reconstruction corresponds to one of the six dif-
ferent phases of the respiratory cycle. The entiremo-
tion is constituted from the set of 3D sorted data.
The obtained slices are numbered relatively:

Slice 0 is the sagittal slice in the middle of the pa-
tient’s lung, positive slices are toward the lung apex,
and thenegativesare towardthediaphragm. It is im-
possible to obtain one scan that represents each slice
at exactly 0 %, 10 %, 20 %, 30 %, 40 % and 50 %
expiration. Consequently a tolerance value is intro-
duced in order to use one scan for eachphase. So, the
accurate moment does not always correspond to the
phase inwhich it is used. TheAdvantage4Dsoftware
(by GE Medical systems) is in charge of this retro-
classification. Table 1 shows the correspondences be-
tween the accurate moments and the phase in which
they are sorted by Advantage4D.As observed in Ta-
ble 1, for slices 75,0, −50 and −100, the same CT
scans are used in the 0 % and 10 % phases leading
to artifacts in the final 3D scan reconstruction. For
example, Table 1 shows that slice number 75was ob-
tained at the precisely 14% expiration and it is used
to construct two 3D images: the image of the 0 %

expiration phases and also the image of the 10 % ex-
piration phase.

Table 1: Accurate respiratory moments used for 0 %,
10 %, 20 %, 30 %, 40 % and 50 % phases

Slice 0 % 10 % 20 % 30 % 40 % 50 %
Numbers phase phase phase phase phase phase

75 14 % 14 % 22 % 30 % 38 % 53 %

0 16 % 16 % 16 % 33 % 41 % 50 %

−50 17 % 17 % 17 % 33 % 41 % 49 %
−100 2 % 2 % 34 % 34 % 43 % 52 %

Morphing was performed for several couch posi-
tions on the first 2D image (classified in 0 % phase)
and the final image (classified in 50 % phase). The
morphing intermediate files obtained were compared
to the intermediate images obtained with 4DCT. All
the compared images and intermediate files have the
same number of voxels (512 by 256). The voxels of
all those files have the same dimensions (28 mm by
38mm by 50mm) andCartesian coordinates. Thus,
to compare one file with another, we counted the
number of voxels which did not have the same value
in both files.

3 Morphing

This section describes the morphing algorithm that
we designed and implemented.
Whereas the other models presented are predic-

tive (cf. section 1.3), our model is an interpolated
model based on morphing. The algorithm is pre-
sented in Fig. 3. It has three input parameters: the
source and target files, containing the organ contour
at the end of and at the beginning of the respiration
cycle, respectively, andan integerwhich regulates the
deformation, as explainedbelow. There is oneoutput
parameter: the array of intermediate files generated
at each step. 4DCT generates image files. Then, as
explained in section 2.1, the Advantage4D software
sorts the files. Next, the AdvantageSim software (by
GE Medical systems) detects and stores the target
organ contours.
As a first step, this algorithm reads the file cor-

responding to the initial image of the organ (at the
beginning of expiration). Actually, the only informa-
tion used is either each voxel is inside or outside the
lung. So, the values that the algorithm deals with
are ‘0’ and ‘1’. Actually, the contour of the organ
is a set of voxels. When a contour voxel Vsource is
found, eachvoxel located around takes the value that
it has in the final image (at the end of expiration):
‘0’ if it is outside the organ at the end, ‘1’ otherwise.
Fig. 4 illustrates the voxels that are taken into ac-
count around Vsource (the black voxel). This step is
repeated until it obtains the final image of the organ

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Program Morphing ;

// Input and output parameters
Input: (Dicom_Struct) Source_File, Target_File ;

Input: (Number) Def_default ;
Output: (Dicom_Struct) Interm_Files[] ;

// Main Program

// Initialisations

iter := 0 ;
Intermediate_File[iter] := Source_File ;

// Main loop
While (Interm_File[iter] <> Target_File) Do

// Mean distance from the final organ contour
D:=MeanDist(Interm_Files[iter],Target_File);

// Voxels to take into account around Vsource
For each voxel Vsource of the contour Do

// Distance between Vsource and final Vsource
Dsource := Distance(Vsource, Target_File) ;

Dist := Def_default;

If (D < Dsource) Then
Dist := Dist x Dsource / D ;

End If ;

VoxelSet := VxTakenIntoAccount(Vsource,Dist) ;

// Voxel transformation
For each v in VoxelSet Do

// v takes the value it has in the target file
Interm_Files[iter](v) := Target_File(v) ;

End For ; // voxel transformation

End For ; // voxel around Vsource

// Next iteration

iter := iter+1 ;
Interm_File[iter]:=Interm_File[iter-1] ;

End While ; // Main loop
End Program ;

Fig. 3: The morphing algorithm

Fig. 4: Voxels that are taken into account at each step of
the transformation

slice: this treatment is applied to each voxel of the
organ contour of the considered image. The voxels of
the initial image progressively take the ‘0–1’ values
of the voxels of the final image. The result of this
loop is an intermediate file which is not the final file.
Thus, this loop is repeated until the intermediate file
obtained is exactly equal to the final file: the inter-
mediate file obtained will be considered as the new
initial file during the next step. The intermediate
files obtained, placed one after another in chronolog-
ical order, create a motion. Thus, our purpose is to
make this motion be close to the real lung motions
of the patients.
The very first version of this algorithm took

into account only the grey-coloured voxel of Fig. 4.
In fact, the obtained contours were very jagged,
whereas in reality the contours are more circular.
For this reason, the algorithmnowtakes into account
grey-coloured and green-coloured voxels around each
Vsource.

This algorithm implements a feature-based me-
thod (cf. section 1.1). Indeed, it is based on organ
contourdetectionand its geometrical transformation.
There is a kinetic regulation to determine the num-
ber of voxels taken into account around Vsource. This
method allows us to apply the transformation to dif-
ferent distances of Vsource: the voxels just in touch
(8 voxels), or the voxels that are 2 voxels away from
Vsource or n voxelsaway from Vsource. Moreprecisely,
the mean distance Dsource between Vsource and the
voxels of the organ contour of the target image is cal-
culated (by superimposing the two files). Vsource is
not unique: the same mean distance is calculated for
all the voxels of the contour. The algorithm obtains
D, which is themean distance of themean distances.
Eachcalculated Dsource is compared to D. If Dsource
is less than D, the number of voxels transformed is
the number defined as the input parameter; other-
wise this input parameter is weighted by Dsource/D.
Thus, the deformation applied is more important

if Vsource is far from the final position of the organ
contours. Furthermore, since these distances vary at
each step of the algorithm, the deformation is faster
during the construction of the first intermediate files
than during the last files. Nevertheless, even if this
version is very simple (2 grey levels and 2 dimen-
sions), we wanted to validate our approach before
improving it (multiple grey levels, 4 dimensions).
In order to validate our approach on a qualita-

tive level, we compared the iterative organ contours
obtainedbymorphing to the intermediate organcon-

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tours drawn by Advantage4D. We applied this tech-
nique to a patient and to different lung slices. In the
next part of this paper, we present and analyze the
results obtained.

4 Results
We selected four lung slices. We analyzed the
scanned images from 4DCT and compared them to
the images obtained by morphing. The first part of
this section presents the results, the second part is
a discussion regarding the advantages of morphing
in radiotherapy, and the limitations of this algorithm
and technique are presented in the last part.

4.1 Morphing analysis

Figs. 5 and 6 display the results obtained by 4DCT
and morphing. The results are superimposed in
Fig. 5: black lines represent the organ contours ac-
cording to 4DCT and white lines represent the or-
gan shapes according to themorphingalgorithm. For
slice 75 of the left lung, there were five 4DCT scans.
We applied morphing as if the first and last scans
(white shapes) were the lung image at maximal in-
spiration and maximal expiration, respectively. In
that case, there are less than 2 % different voxels
at each step (each slice is composed of 512 by 256
voxels). As shown in Fig. 5, similar results are ob-
tained with slice 0. The most important difference
is obtained with the right lung slice −100: 2 % dif-
ferent voxels between morphing intermediate images
and 4DCT scans for one respiratorymoment (34%).

Fig. 5: Superimposing of the images obtained with mor-
phing (white shapes) and 4DCT (black lines). Percent-
ages are the accurate moments of each phase (Table 1)

Fig. 6: Right lung images superimposed, 4DCT in white,
morphing in red, phase 10 % (a), phase 20 % (b), phase
40 % (c), slice −37.5 mm

In Fig. 6, the lung obtained by morphing (in red)
is superimposed over the lung image obtained with
4DCT.
The differences between the images are accept-

able. Indeed, these results tend to prove that the
morphing algorithm that we have designed both de-
scribes and predicts lung motion. Now we have to
account for the rhythm of the motion in morphing.
Lung motion is relatively simple, but this may not
be the case for other organs.

4.2 Benefits of morphing

Even if this version of our morphing implementa-
tion requires improvement, the present study sug-
gests that applyingmorphing technique in radiother-
apy is promising, and that it presents several consid-
erable advantages.
Themost important benefit is that it protects the

patient from radiation. Indeed, the combination of
4DCTandmorphing requires only two scans for each
organslice insteadof six ormore scans for each organ
slice with the use of 4DCT alone. Morphing will not
totally replace 4DCT scans, but it may considerably
decrease the number of required scans.
The second advantage is that it improves contour

detection and description. Indeed, morphing will en-
able the creationof an entire organ imageat a precise
moment t, even if there is no scan at t. Furthermore,
morphing reduces the number of artifacts, since ar-
tifacts appear when no scan is available at t for a
specific slice. Therefore, morphing may be used in
order to decrease the number of artifacts: consider-
ing lung slice −100 of Table 1 for example, instead
of using a 2 % scan in 10 % phase, morphing could
extrapolate a scan for 10 % phase from the 2 % scan
and one other scan.
Finally, the description of the entire range ofmo-

tion for an organwill be more precise. This will con-
siderably benefit synchronization during the treat-
ment phase.

5 Conclusions
These preliminary results concerning the use ofmor-
phing for organ deformation analysis are promising.

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The maximum morphing deviation is only 2 %. The
lack of 4DCT scan patient data was highlighted. To
overcome this limitation, morphing is a possible in-
terpolation method. It can give intermediate organ
contours in order to supplement 4DCT scan sorting.
This will reduce the internal margin for the target
volume and will enable the patient to be treated in
gated-radiotherapy [16]. It will then be required to
apply morphing in 3D.
Our goal is to further refine the algorithm. Step

by step, we will study ways to distinguish transfor-
mation speediness from organwall elasticity and sec-
ondly to simulate the movement of the entire target
organ. A further stepwill involve representing defor-
mations of other organs and also taking into account
the motion of organs at risk. Indeed, some organs
may not have linear moves; there are accelerations
and slowdowns. In addition, organwalls are not uni-
formly elastic. In order to have a global vision, it is
necessary to apply separate speeds and distortions to
simulate the motions of a group of organs.

Acknowledgement

The authors acknowledge financial support from
LCC (Ligue Contre le Cancer), STIC (Soutien Tech-
nique aux Innovations Coûteuses) gating, région
Franche-Comté,CancéropôleGrand-Est, CAPMand
Dr. R. Hamlaoui (CHU BesanC̨on) for organ delin-
eations. Wewould like to thankDr.G.Hruby for his
help with translation and proof reading.

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Dr. Robert Laurent, Julien Henriet,
Régine Gschwind,
Prof. Ing. Libor Makovicka, DrSc.
E-mail: Julien.henriet@pu-pm.univ-fcomte.fr
IRMA/ENISYS/FEMTO-ST, UMR 6174 CNRS
Montbéliard, France

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