Treatment Planning Including Respiratory Motion Based on
4D Computed Tomagraphy Data
Eike Rietzel1,2, George T.Y. Chen1, Judith A. Adams1, Alvaro H. Hernandez1, Christopher G. Willett1
(1) Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; (2) Abteilung
Biophysik, Gesellschaft für Schwerionenforschung, Darmstadt, Germany
Abstract
4D Computed Tomography records different respiratory states of patient anatomy. These data can be included in dose calculations
to explicitly account for intrafractional organ motion caused by respiration. Based on the magnitude of motion, appropriate
treatment delivery techniques can be selected. Internal target volumes are generated by combining targets at different respiratory
phases. The impact of organ motion on dose distributions is analysed by inspection of dose distributions for time-varying patient
anatomy; dose coverage throughout the respiratory cycle can also be visualized.
Keywords
4D Computed Tomography, 4D treatment planning, dose calculation, respiratory motion, intrafractional motion, non-rigid
registration
Introduction
Intrafractional organ motion can cause significant errors in
radiotherapy imaging, treatment planning, and treatment
delivery. In standard CT scanning, severe imaging artifacts can
be introduced, primarily due to respiratory motion. To reduce
or even eliminate such errors, time-resolved CT data can be
acquired. 4D Computed Tomography [1,2] provides multiple
spatio-temporally coherent volumetric data sets of patient
anatomy, each at a different respiratory state. Such data can be
included in treatment planning to ensure adequate dose
coverage of the target(s) throughout the respiratory cycle. To
generate patient specific composite target volumes that account
for respiratory motion, contours from different respiratory
volumes are combined. Furthermore, the impact of respiratory
motion on dose distributions can be analyzed. Respiration
induced density variations must be considered during treatment
planning especially for charged particle therapy. Particle ranges
strongly depend on time dependent density variations along
their path. To analyze organ motion including deformation or
to add dose distributions from different respiratory phases,
non-rigid registration techniques are needed.
Material and methods
4D Computed Tomography
4DCT data are acquired on a 4-slice General Electric
Lightspeed CT scanner. The scanner is operated in axial cine
mode. At each couch position, data are acquired for a duration
equal to the patient’s respiratory cycle; during this interval, ~5
complete x-ray tube rotations occur. The couch is then
advanced to the next position. During couch movement data
acquisition is paused. At each table index, ~25 images are
reconstructed, evenly distributed over the acquisition time.
Each image represents a different respiratory state. During CT
data acquisition, the patient’s abdominal surface is monitored
with the Varian RPM-system. In addition to surface motion,
the software records correlation signals sent from the CT
scanner whenever data acquisition is turned on or off. GE’s
Advantage4D software facilitates retrospective sorting of the
images into spatio-temporally coherent volumes. Based on the
temporal correlation between surface motion and data
acquisition each reconstructed image is assigned a specific
respiratory phase, as obtained from the surface motion. A
respiratory phase can be chosen and the software automatically
assembles images at the selected phase for each couch index.
Typically, CT studies at 10 different respiratory phases are
generated, equally distributed over the respiratory cycle.
Non-Rigid Image Registration
Non rigid image registraion is key to 4D CT image and dose
analysis. The software package vtkCISG [3] provides voxelbased affine and non-rigid image registration algorithms.
Affine registration involves optimizing 12 degrees of freedom.
Non-rigid image registration describes local deformations
using a free-form deformation model based on B-splines [4].
Different similarity measures and control point spacings for
non-rigid registration were evaluated. Independently moving
anatomical regions of the body must be registered separately.
For the case study presented here, internal anatomy was
segmented. Registration results are poor if registration of the
complete anatomy is attempted simultaneously. Local
deformations cannot be modelled accurately e.g. due to the
quasi-static spine and ribs adjacent to moving internal organs.
Initially full affine registration is performed to obtain global
motion parameters, followed by modelling local deformations
via non-rigid registration. Registration results are visually
inspected for consistency on a patient-by-patient basis with
either difference images or color overlays.
Dose calculations
The impact of respiratory motion on dose distributions is
studied by calculating dose distributions for 4DCT volumes
based on a nominal treatment plan optimized for a single CT
dataset. Standard treatment planning systems do not support
calculations for different anatomical data sets with the same
treatment plan. In the CMS FOCUS treatment planning system
used here, treatment plan parameters were copied and
modified directly at the filesystem level using in-house
software. For the example presented here, a two field proton
plan was optimized.
Results and discussion
Motion assessment
A standard helical CT scan of the abdominal region is shown in
figure 1. Typical motion artifacts are clearly visible. The
identical coronal cut at three different respiratory states as
obtained by 4DCT scanning is shown in figure 2. Motion
artifacts are significantly reduced or absent. Liver motion was
assessed to be 2.25 cm along the cranio-caudal axis from
4DCT. The dataset closest to inhale shows minor residual
artifacts in the lower abdominal region. These artifacts are due
to irregular breathing during 4DCT data acquisition.
Figure 1: Standard helical CT scan of the abdomen acquired under
light respiration. Typical respiratory artifacts are present, e.g. jagged
edges of the liver.
Target delineation
Dose calculations
The hepatocellular tumor of the patient shown in figures 1 and
2 was contoured on the 4DCT datasets closest to inhale and
exhale. The union of these GTVs was generated to account for
respiratory motion. To assess the adequacy of coverage, the
composite target volume was overlayed on all respiratory
phases obtained by 4DCT scanning. Visual inspection verified
that the composite contour encompassed all intermediate
respiratory states of the GTV.
Dose distributions were calculated for ten 4DCT volumes
based on the optimized treatment plan. Figure 3 shows
distributions for respiratory phases close to inhale, mid-exhale,
and exhale on a coronal cut. Visual inspection showed that the
composite target volume is included in the high dose area for
all respiratory states. Dose volume histograms were not
calculated for all datasets because contours were only drawn on
volumes close to inhale and exhale.
The impact of density variations on proton ranges is clearly
visible. The shape of iso-dose lines changes between
respiratory phases. For the most inferior liver position at inhale
low iso-dose lines extend beyond the liver. At inhale, densities
in the beam paths are decreased compared to the exhale
volume. The liver moves inferior and is replaced by less dense
lung tissue. Full dose coverage of the target is maintained at
exhale due to the density changes applied during treatment
planning.
Treatment planning
A proton treatment plan was optimized for the composite target
volume. Additional target expansions were used to account for
interfractional organ motion and residual intrafractional motion
uncertainties. The plan consisted of a right lateral and a
posterior field. Variations in water equivalent pathlength
caused by density changes perturb proton ranges. The liverlung interface was within both treatment fields. Adequate
proton ranges to the distal edge of the target throughout the
respiratory cycle had to be ensured. Therefore maximum
densities throughout the respiratory cycle within each beam’s
path had to be considered for treatment planning. A composite
liver volume based on contours drawn on exhale and inhale
volumes was generated. Densities inside the composite liver
were set to liver density. Setting the higher liver density instead
of lung tissue density guaranteed sufficient penetration to the
distal edge of the target throughout the respiratory cycle.
Registrations
Figure 4 shows transformation vectors obtained by registration
of 4DCT volumes closest to exhale and inhale. Only internal
Figure 2: Respiratory organ motion imaged with 4D Computed Tomography. Left to right corresponds to inhale, mid-exhale, and exhale
respiratory phases. Note the significant reduction of motion artifacts compared to figure 1. Liver motion was 2.25 cm.
anatomy that is subject to respiratory motion was considered
for registration as shown in figure 4, top. The similarity
measure used for registrations was sum of squared differences.
Initially, volumes were registered with translations, rotations,
and scaling. The following non-rigid registration was
performed using a control grid spacing of 10 mm. Manual
segmentation of internal anatomy was performed for one of the
volumes (exhale). During registration only voxels within the
segmented volume contributed to the similarity measure, other
non-overlapping voxels of the inhale volume were disregarded.
Figure 5 shows coronal difference images between the
registered volumes pre and post registration. Differences
between the unregistered volumes clearly show motion of
internal anatomy, especially at edges of organs. After
registration, only small differences remain in the image. These
differences are due to residual artifacts in the 4DCT data and/or
volumetric averaging effects during transformations.
Conclusion
4D Computed Tomography permits inclusion of information
on respiratory organ motion in treatment planning. Composite
target volumes can be generated based on multiple contours at
different respiratory phases. Visual inspection of composite
targets overlaid on 4DCT or dose volume histograms can be
used to assess adequate dose coverage of the target throughout
the respiratory cycle. For charged particle treatment planning,
respiration induced range variations can directly be calculated.
Non-rigid registration techniques can relate patient anatomy at
different respiratory states to each other. Such information
facilitates improved analysis of organ motion and deformation.
Furthermore, dose distributions can be calculated including
motion. In principle each voxel can be tracked throughout the
respiratory cycle to add the deposited dose per voxel.
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Figure 3: Impact of organ motion on a proton dose distribution.
Respiratory phases correspond from top to bottom to inhale, midexhale, and exhale. GTVs contoured on the inhale and exhale dataset
are shown as thin lines. Iso-dose lines displayed are from 99% to 10%
of the prescribed dose.
Figure 5: Subtraction images pre (top) and post (bottom) affine
followed by non-rigid registration between 4DCT datasets
closest to inhale and exhale.
Figure 4: Transformation between 4DCT volumes closest to
inhale and exhale; top image: segmented internal anatomy at
exhale; middle image: transformation vectors from inhale to
exhale obtained by affine and non-rigid registration; bottom:
non-rigid part of transformation vectors only.