Displacement-based binning of 4-D CT image data sets
George Starkschall1, Himanshu Shukla2, Paul J Keall3, John A Antolak1, Radhe Mohan1
1
Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA, 2Philips Radiation Oncology
Systems, Cleveland, OH, USA, 3Departments of Radiation Oncology and Biomedical Engineering, Virginia Commonwealth University,
Richmond, VA, USA
Abstract
We describe a method for extracting displacement-binned computed tomographic (CT) image data sets from phasebinned data sets acquired on a multislice helical CT scanner. The projection data set is phase-binned at small phase
intervals prior to reconstruction. Phases corresponding to a desired external fiducial displacement are identified on the
basis of the record of motion of the external fiducial. CT image data sets are re-sorted based on these phases. Finally,
displacement-binned image data sets are transferred to a treatment-planning computer for analysis.
Keywords
Radiation therapy imaging, respiratory motion, 4-D CT imaging
Introduction
Respiration causes various tumors in the thorax or abdomen to
move by as much as 2 to 3 cm, which can pose problems for
radiation treatment planning and delivery [1-4]. Until recently,
such tumor motion was displayed using either respiratory-gated
[5] or breath-hold [6] computed tomography (CT) images.
However, several manufacturers of CT imaging equipment
have adopted techniques developed for cardiac CT imaging
that make it possible to acquire image data at specified phases
over several respiratory cycles and then combine the data into
phase-binned images [7]. As long as the motion to be
visualized is periodic, the resultant phase-dependent CT images
can serve as accurate substitutes for time-dependent data sets.
Keall et al. [8] have refined and applied techniques developed
for phase binning based on cardiac motion to phase binning
based on respiratory motion using images acquired by a
commercial CT scanner (MX8000-IDT; Philips Medical
Systems, Cleveland, OH). In this technique, the scanner
acquires a signal from a respiration monitor (RPM; Varian
Medical Systems, Palo Alto, CA) at a specified point in the
respiratory cycle (typically end-inspiration) and tags the
sinogram file at projections corresponding to these points.
Software in the CT system divides the respiratory cycle into a
user-specified number of phases and bins the projections
acquired near each phase point [7]. These binned projections
are then reconstructed to generate a phase-binned CT image
data set.
Problems with this technique are illustrated in Figure 1, which
shows a midsagittal reconstruction of a phase-binned CT image
data set. Note the irregularities in the anterior surface of the
patient. One source of irregularity is that delivery of the phasebased tag by the respiration-monitoring device is determined
by predicting the maximum excursion of the fiducial that
monitors the respiratory cycle rather than by the actual
maximum excursion of the fiducial. This is a reasonable
design decision, given that the point where the tag is delivered

present address: Siemens Oncology Care Systems, Concord, CA, USA
must be determined in real time. However, because of the
likelihood of a slightly irregular respiratory cycle, the tag rarely
occurs at precisely the maximum point of inspiration.
Differences of up to 0.5 sec may occur between actual endinspiration and tag delivery, resulting in differences of up to
0.5 cm in the displacement of the fiducial.
Figure 1: Sagittal reconstruction of single phase of phase-binned CT
image data set of patient
A second cause of irregularity is that in spite of both audio
prompting the patient when to breathe, and visual feedback
showing the patient how deeply to breathe, irregularities in the
patient’s respiratory cycle can cause the external fiducial to be
at different points in each cycle. Figure 2 is a plot of the
fiducial displacement as a function of the phase of the
respiratory cycle, representing 20 contiguous breathing cycles,
with the plot scaled so that the phase points are consistent, and
shows that the displacement may differ by up to 0.5 to 1 cm
depending on the point in the respiratory cycle being binned.
As a possible way to circumvent this problem, we hypothesized
that cycle-to-cycle consistency might be improved by binning
the projections based on the displacement of the external
fiducial rather than based on the phase of the respiratory cycle.
The present software in the CT scanner system, however, does
not support displacement-based binning. It is possible,
however, to extract displacement-binned reconstructed image
data sets from a set of phase-binned data sets. We describe
here a technique that we developed for extracting appropriate
displacement information to allow the displacement-based
binning of CT images from phase-based reconstructions.
The respiratory monitoring device delivered a 5V, 50 msec
pulse to the CT scanner at a specified point in the respiratory
cycle, selected to be at end-inspiration, because it appeared to
be the most well-defined phase point in the respiratory cycle.
The CT scanner tagged the projections acquired at the time of
the pulse.
2.2
Projection sorting
At the conclusion of image acquisition, a file was created that
displayed the tags. This file was transferred to a spreadsheet
(Excel; Microsoft Inc., Seattle, WA). Two additional columns
were added to the spreadsheet. In one column, the table travel
between tags was calculated using the following equation
table travel
Figure 2: Plot of displacement of external fiducial as a function of
phase of the respiratory cycle for each of approximately 20
respiratory cycles.
Materials and methods
2.1
CT image acquisition
detector width (2.4cm) pitch(0.125) 60 sec/min
gantryrotation ime
t (0.5sec)  HRate(min-1)
,
where HRate is the respiratory rate (recalling that the original
software was designed for cardiac gating). In the second
additional column, the number of slices corresponding to this
magnitude of the table travel was indicated. The number of
slices was determined by dividing the magnitude of the table
travel by the slice thickness (typically 0.3 cm). Table 1 shows
an example of such a table.
Patients were placed supine in the CT scanner (MX8000-IDT;
Philips Medical Systems, Cleveland, OH) in a standard
immobilization device used for thoracic radiation therapy
(VacLoc; Med-Tech, Orange City, IA). Respiration was
monitored by means of an external fiducial placed on the
abdomen (RPM). Excursion of the fiducial was tracked
using an infrared light source and a CCD detector.
Patients were instructed to breathe freely for several respiratory
cycles, during which the respiratory rate and the magnitude of
fiducial displacement were monitored. Patients were coached
in breathing at a regular rate by means of an audible instruction
to breathe at a frequency set to the patient’s respiratory rate [9].
Visual feedback was provided by the respiratory monitoring
system. The predetermined magnitude of fiducial displacement
is displayed, as is the instantaneous position of the fiducial.
The patient was instructed to breathe so that the fiducial
traversed the entire range of displacement without exceeding
the range. The information was displayed on a flat-panel
monitor mounted behind the patient and made visible to the
patient by means of a mirror assembly. The mirror assembly is
illustrated in Figure 3.
Table 1. An example of the file containing the tags, as generated on
the sinogram file on the CT scanner.
The respiratory monitoring system generated a file that showed
the displacement of the external fiducial from an arbitrary
starting point as a function of time as well as the locations of
the respiratory tags. This file was also transferred to a
spreadsheet. By noting the intervals between tags on this
respiratory data file, we correlated the motion of the respiratory
monitor to specific intervals on the tag file.
The time values were rescaled to indicate the elapsed time from
each most recent previous tag. In each respiratory cycle, the
row corresponding to the time when the tag occurred, as well
as the maximum and minimum values of the displacement in a
particular respiratory cycle, was identified. The minimum
displacement of the peaks and the maximum displacement of
the troughs were the identified. Table 2 shows an example of
Figure 3: Flat-panel display and mirror assembly enabling visual
feedback to patient of monitored respiratory motion. The subject was
a volunteer faculty member.

The columns labeled “Table Travel” and “# slices” have been
calculated. The column labeled “HRate” indicates the tag frequency,
in pulses/minute.
the tag locations as well as the magnitude and locations of the
maximum and minimum displacements of the external fiducial.
Table 2. An example of the file identifying the row in the gating
trace corresponding to the tag, as well as the maximum and minimum
displacements of the external fiducial.
The interval between the minimum displacement of the peaks
and the maximum displacement of the troughs was divided into
a desired number of “amplitude” displacement bins. Typically,
eight displacement bins were used. In the data file, for each
phase, the row corresponding to the displacement value closest
to the bin displacement was identified and tabulated. In
addition, the percentage of the distance from that row index to
the next tag was also tabulated. This percentage would be the
value of the phase used for reconstruction.
Ideally, a bin would be selected for reconstruction and a
reconstruction would be performed with phases at a suitable set
of values that spanned the percentages tabulated for the bin.
The present reconstruction software, however, limits
reconstruction to 10 phase bins per reconstruction process.
Consequently, two sets of reconstructions at 10 phase bins
were generated, providing reconstructions at 5% intervals.
For each phase in the respiratory cycle, a reconstruction phase
closest to the target phase was selected. The row in the tag
trace corresponding to the reconstruction phase was identified
and the displacement corresponding to the reconstruction phase
was determined. Table 3 illustrates this information for the
phase identified as “End Inspiration.”
Next, the reconstructed transverse CT images were sorted so
that the slices between each set of tags belonged to the
appropriate phase. For example, on the basis of the data
presented in Table 3, the slices between tags 1 and 2 came
from the 0% phase, while the slices between tags 4 and 5 came
from the 10% phase. To initiate the sorting, all reconstructed
data sets were transferred to a workstation that supported the
vendor’s image analysis software (MXView; Philips Medical
Systems). Each CT image data set was stored in a folder, the
name of which was related to the phase. Within each data set,
each transverse CT image was stored in a file, the name of
which was related to the slice number. Consequently, given a
phase and a CT slice number, it was relatively straightforward
to identify the file containing the CT image data.
Table 3. Illustration of comparison of desired phase displacement
with actual displacement of selected phase for reconstruction. The
example in this case is that of end-inspiration.
Each transverse CT image was assigned to a respiratory cycle,
which was possible because the last column of the tag table
(Table 1) indicated the number of CT images between each tag.
For example, in Table 1, the 1st, 2nd, and 3rd respiratory
cycles include seven images, while the 4th cycle includes eight
images. This only allows a preliminary assignment of images
to respiratory cycles, however. The assignment may not be
quite correct, because the assumption is made that the first tag
is coincident with the first transverse CT image. In reality, the
tag could have occurred before the projections that generated
the CT image were acquired. Thus, the entire assignment of
respiratory cycles may have to be shifted.
To determine the amount of this possible shift, a sagittal
reconstruction of the CT image data set was displayed. An
example of such a sagittal reconstruction is shown in Figure 4.
In this figure, the horizontal lines indicate boundaries between
CT images acquired during different respiratory cycles. The
image indices corresponding to these boundaries were noted,
and the assignment of the respiratory cycles was shifted
appropriately. From the table of slice displacements (Table 3),
phase values were assigned to the slice numbers.
Figure 4: Sagittal reconstruction of phase-binned CT image data set
illustrating correlation of transverse CT images with respiratory
cycles. The white and green horizontal lines indicate boundaries
between images acquired during different respiratory cycles.
The reconstructed images were sorted using the file structure of
the Windows operating system.
From the information
generated, it was noted that the CT images with indices 1
through 4 were to contain 0% phase data, so the corresponding
files in the 0% phase folder were left alone. Images with
indices 5 through 11 were to contain 5% phase data, so the files
containing images 5 through 11 were copied from the folder
containing 5% phase data to the 0% phase folder, replacing the
CT images 5 through 11 that were originally in the folder.
Images 19 through 26 were to contain 10% phase data.
Consequently, the appropriate files were copied from the folder
containing 10% phase data to the 0% folder. This was done
until all appropriate files were copied and verified. The final
step was to transfer the data to the treatment-planning system
for display and analysis (Pinnacle3; Philips Medical Systems,
Milpitas, CA).
Results and discussion
Displacement-based binning improves the reliability of fourdimensional (4-D) CT image acquisition using a multislice
helical CT scanner. Rebinning phase-binned CT image data
sets therefore appears to be a dependable approach to
displacement-based binning.
Figure 5 shows an example of phase-based and displacementbased binning. Both images are reconstructions of the sagittal
midplane at end-inspiration. Substantial differences between
phase-based and displacement-based binning could not be
detected, except in the inferior region, where the anterior
surface of the abdomen appeared to be less variable in the
displacement-based binned images. Gaps in the reconstructed
image, especially in the inferior region, resulted when the table
travel was too rapid, resulting in a table translation that was
faster than the respiratory rate. This can be observed in Table
1, when the table traveled greater than 2.4 cm between tags.
Because the detector width is only 2.4 cm, not enough
projections could be obtained to completely reconstruct the
transverse CT image. Updated software now being developed
by the vendor that slows table translation may alleviate this
problem.
(a)
(b)
Figure 5: (a) Phase-binned CT image data sets and (b) displacementbinned CT image data sets at end-inspiration.
Nonetheless, we can use this approach as an interim measure,
as true displacement-based binning, in which a displacement
tag is placed in the sinogram file at appropriate intervals, is
likely to lead to more accurate, and potentially clinical more
useful image data sets. For the time being, however, true
displacement-based binning is technically much more complex.
Such binning would probably also be able to involve the entire
respiratory signal thereby making the reconstruction of 4-D
datasets more specific and thus more clinically friendly.
Acknowledgements
The authors wish to acknowledge partial financial support for
this work from Philips Medical Systems.
References
[1] Ross C S, Hussey D H, Pennington E C, Stanford W and
Doornbos JF 1990 Analysis of movement of intrathoracic
neoplasms using ultrafast computerized tomography Int J Radiat
Oncol Biol Phys 18 671-677
[2] Korin HW, Ehman RL, Riederer SJ, Felmlee JP and Grimm RC
1992 Respiratory kinematics of the upper abdominal organs: A
quantitative study Magn Reson Med 23 172-178
[3] Davies SC, Hill AL, Holmes RB, Halliwell M and Jackson PC
1994 Ultrasound quantitation of respiratory organ motion in the
upper abdomen Br J Radiol 67 1096-1102
[4] Kubo HD and Hill BC 1996 Respiration gated radiotherapy
treatment: A technical study,” Phys Med Biol 41 83-91
[5] Mori M, Murata K, Takahashi M, Shimoyama K, Ota T, Morita
R and Sakamoto T 1994 Accurate continuous sections without
breath-holding on chest CT: Value of respiratory gating and
ultrafast CT Am J Roentgenol 12 1057-1062
[6] Balter JM, Lam KL, McGinn CJ, Lawrence TS and Ten Haken
RK 1998 Improvement of CT-based treatment-planning models
of abdominal targets using static exhale imaging Int J Radiat
Oncol Biol Phys 41 939-943
[7] Grass M, Manzke R, Neilsen T, Koken P, Proska R, Natanzon
M, Shechter G 2003 Helical cardiac cone beam reconstruction
using retrospective ECG gating Phys Med Biol 48 3069-3084
[8] Keall PJ, Starkschall G, Shukla H, Forster KM, Ortiz V, Stevens
CW, Vedam S, George R and Mohan R 2003 Acquiring 4D
thoracic CT scans using a multislice helical method Phys Med
Biol submitted for publication
[9] Kini VR, Vedam SS, Keall PJ, Patil S, Chen C and Mohan R
2003 Patient training in respiratory-gated radiotherapy Med
Dosim 28 7-11