Extraction of the respiratory signal from sequential thorax
Cone-Beam X-ray images
Lambert Zijp, Jan-Jakob Sonke, Marcel van Herk
The Netherlands Cancer Institute / Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
Abstract
Cone-Beam CT (CBCT) scans of the thorax and upper abdomen contain considerable artifacts caused by respiratory motion. These
artifacts can be prevented in CBCT if only those X-ray images that are obtained during the same breathing phase are used for
subsequent reconstruction. In this paper, a method is presented to determine the breathing signal from the X-ray images themselves,
thus obliterating the use of respiration sensors and the need to synchronize breathing signal measurements with image acquisition.
The method is fast, robust and fully automatic. The principal idea in the method is to enhance diaphragm like features in the
individual X-ray images, project these features on the cranio-caudal axis, combine all successive 1D projections to a 2D image, and
to extract from this image features that change position along the cranio-caudal axis.
Keywords
Cone-Beam CT, 4D radiotherapy, X-ray, projections, thorax, diaphragm, reconstruction artifacts, breathing cycle
Introduction
A prerequisite for high quality CT reconstructions is that
enough angular projections are made and that the scanned
object is stationary [1,2,3]. In medical imaging the latter
condition is not always met. Especially CT scans of the thorax
and upper abdomen contain huge artifacts due to breathing
motion within the patient.
In spiral CT scanners, an X-ray source and a 1D detector rotate
with high speed around the patient, and the patient moves
slowly through the scanner. This gives rise to a sequence of
sharp 2D axial reconstructions. In areas where breathing
motion is large, the 3D combination of these images contains
spatial distortions (Fig 1 left).
In Cone-Beam CT (CBCT) scanners, an X-ray source and a 2D
detector rotate slowly around a stationary patient and typically
collect 300-1000 images [4]. The detector produces sharp 2D
conical projections, but the 3D reconstruction will be blurred in
areas with large breathing motion (Fig 1 right).
acquisition result in 3D reconstructions of diagnostic quality,
but impair the patients comfort, may increase the scanning
time, and may be too a-typical of the patients behavior for
satisfactory use in radiotherapy.
Another strategy is to let the patient breathe freely, and select
for 3D reconstruction only those images that were made in the
same phase of the breathing cycle. Following this scheme, and
having acquired a sufficient amount of images, permits a 4D
reconstruction [6,7,8].
The breathing signal can be determined by measurement of
temperature differences between inhaled and exhaled air near
the patients nose, by measurement of changes in the
circumference of the thorax using stretch sensors, or using
reflective markers on the patients chest. These types of
measurements need to be synchronized with the exact time at
which individual slices were acquired in the spiral CT, or at
which X-ray images were taken in a CBCT scanner.
This paper presents a method to extract the breathing signal
from the individual X-ray images in a CBCT acquisition
sequence. A thus obtained breathing signal is synchronized by
definition, and eliminates the need for additional equipment.
Material and methods
Figure 1: Breathing artifacts in the lower lung: distortion in
conventional CT (left) and blurring in CBCT (right).
For both CT scanning techniques, methods have been proposed
to prevent these breathing artifacts from occurring. Breath
holding during the whole scanning period or operator
controlled breathing [5] combined with interrupted image
The CBCT scanner used was an Elekta Synergy Research
Platform, consisting of a conventional 120kV X-ray source and
an a-Si detector, both mounted on the gantry of an Elekta
SL20i linac. The source-detector distance was 153.6 cm; the
detector has a field of view of 25.6cm by 25.6 cm at the
isocenter plane; radiation exposure is about 50μGy per image,
given in an X-ray pulse of 20msec. During a 360 degree
rotation around the patient (which takes 2-4 minutes), 330 to
660 images with a 512x512 resolution, were acquired.
Algorithm
All X-ray transmission images underwent a few processing
steps in order to highlight the diaphragm. This processing can
be done during acquisition of subsequent images. The
diaphragm is the most prominent anatomical structure
exhibiting breathing motion, and its position is assumed to be a
good measure for the breathing phase.
First, the logarithm of the images was taken (Fig 2 left); the
pixel values then become proportional to the radiological
thickness. The position of the diaphragm is characterized by a
steep cranio-caudal (CC) transition from light to dark.
Applying a CC derivative filter emphasizes this feature (Fig 2
right).
Figure 2: The logarithm of a CBCT X-ray image (left), and
its CC derivative (right).
The prominent vertical bar in the middle of the logarithmic
image, is one of the two carbon fiber reinforced table-support
arms entering the field of view. Vertical features like these
disappear when using a CC derivative filter. A threshold was
first applied to the logarithmic image to discern patient from air,
and next to its CC derivative to isolate diaphragm-like
transitions. The results are combined to mask interesting
regions in a gradient filtered image (Fig 3 left).
The enhanced X-ray images are projected on the CC axis, and
all the 1D projections are then combined and depicted as a 2D
image: the ‘Amsterdam Shroud’ (Fig 4). In this image, the
columns are the 1D CC projections of successive enhanced
images. Note that regions of high intensity are relatively
stationary in time, except for the diaphragm region, which
clearly manifests itself as a more or less periodic signal in the
caudal part of the shroud.
Figure 4: The ‘Amsterdam Shroud’. Each column
corresponds to a single projection image.
In order to automatically detect this region, the temporal
derivative (i.e. horizontal derivative) of the shroud is
calculated. This operation enhances features that change in
time, and lets stationary features disappear. The absolute value
of the temporal derivative (Fig 5) is then again projected on the
CC axis, resulting in a 1D signal exhibiting a very prominent
peak indicating the region of the diaphragm.
Figure 3: The enhanced X-ray image (left) and its projection
on CC axis (right).
In the resulting enhanced X-ray image, the presence of many
other features like upper arms, patient to air transitions, and
ribs, makes isolation of the diaphragm a formidable task.
The method presented here does not try to isolate the
diaphragm, but uses the fact that the diaphragm is the only
feature that moves considerably!
Figure 5: The absolute of the temporal derivative of the
‘Amsterdam Shroud.’.
This region then, is cut out of the ‘Amsterdam Shroud’ and
subjected to the final processing step. Each is column aligned
to the next column, in such a way that the root mean square of
all pixel value differences is minimized. The number of pixels
that one column had to be shifted cranially or caudally for
optimal fit is the respiration signal. In figure 6 the number of
pixel shifts needed for each optimal pair-wise alignment are
shown cumulatively. After removal of the low frequency
components using a high-pass filter, the resulting signal is
ready for 4D reconstruction purposes.
Figure 6: The extracted breathing signal
Figure 6: A sagittal (left) and a coronal (right) slice of a
scan. The upper two are slices through one of the phase bins
of a 4D CBCT reconstruction. The lower two are slices
through a conventional 3D CBCT reconstruction.
Results and discussion
Conclusion
Processing of all X-ray images can be done during image
acquisition. Processing of the ‘Amsterdam Shroud’ takes a few
seconds.
The method has been tested on all available CBCT scans made
in our institute, where the diaphragm was inside the field of
view: one mamma patient, three lung patients, two upper
abdomen patients, and one rabbit (kindly provided by the PMH
in Toronto); 24 scans in total. The values of the few thresholds
used for the first CBCT scan, did not need any alteration in
order to work for subsequent scans. These ‘one size fits all’
parameters indicate robustness of the method. In all cases, a
clear periodic signal was extracted.
The signals were visually verified in two ways.
Firstly by inspection of created movies, where the frames
consisted of successive raw X-ray images in which vertical
bars reflecting the extracted signal were inserted. These movies
showed good correspondence between the moving bar and the
position of the diaphragm.
Secondly, the 4D reconstructions using phase bins of the
extracted signals were inspected. Two 2D examples of a 4D
reconstruction are shown in figure 7. The sharpness of the
upper two slices indicate that the projections used to make the
reconstruction belong more or less to the same breathing phase.
Comparison of the extracted breathing cycle with thermometer
measurements was not done, because any possible
discrepancies between the two may very well be attributed to
the thermometer being less fit for the purpose.
A fast, robust and fully automatic method has been developed
for determining the breathing phase of successive CBCT X-ray
images of the thorax. The method has been successfully
applied for respiration correlated CBCT reconstruction.
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