Download Intrinsic respiratory gating in small-animal CT

Eur Radiol (2008) 18: 1375–1384
DOI 10.1007/s00330-008-0903-3
Soenke H. Bartling
Julien Dinkel
Wolfram Stiller
Michael Grasruck
Ijad Madisch
Hans-Ulrich Kauczor
Wolfhard Semmler
Rajiv Gupta
Fabian Kiessling
Received: 21 September 2007
Revised: 1 December 2007
Accepted: 19 January 2008
Published online: 23 April 2008
# European Society of Radiology 2008
Soenke H. Bartling and Julien Dinkel
contributed equally.
S. H. Bartling . F. Kiessling
Junior Group Molecular Imaging,
German Cancer Research Center
(DKFZ),
Heidelberg, Germany
S. H. Bartling . W. Stiller .
W. Semmler . F. Kiessling
Department of Medical Physics in
Radiology, German Cancer Research
Center (DKFZ),
Heidelberg, Germany
J. Dinkel . H.-U. Kauczor
Department of Radiology, German
Cancer Research Center (DKFZ),
Heidelberg, Germany
M. Grasruck
Siemens Medical Solutions,
Forchheim, Germany
EXPERIME NTAL
Intrinsic respiratory gating in small-animal CT
I. Madisch . R. Gupta
Department of Radiology,
Massachusetts General Hospital,
Boston, MA, USA
S. H. Bartling (*)
Junior Group Molecular Imaging,
Department Medical Physics in
Radiology, German Cancer Research
Center (DKFZ),
Im Neuenheimer Feld 280,
69120 Heidelberg, Germany
e-mail: [email protected]
Tel.: +49-622-1422686
Fax: +49-622-1422572
Abstract Gating in small-animal CT
imaging can compensate artefacts
caused by physiological motion during
scanning. However, all published gating approaches for small animals rely
on additional hardware to derive the
gating signals. In contrast, in this study
a novel method of intrinsic respiratory
gating of rodents was developed and
tested for mice (n=5), rats (n=5) and
rabbits (n=2) in a flat-panel cone-beam
CT system. In a consensus read image
quality was compared with that of nongated and retrospective extrinsically
Introduction
With the increasing utilization of small-animal CT in
preclinical and basic research, effective methods to
compensate for physiological motion during scanning
have been sought. Prospective [1–5] as well as retrospective [6, 7] methods for respiratory and cardiac gating
have been described, and their benefit for structural lung
imaging has been shown [2, 6, 7]. Furthermore, gating
gated scans performed using a pneumatic cushion. In comparison to nongated images, image quality improved
significantly using intrinsic and extrinsic gating. Delineation of diaphragm
and lung structure improved in all
animals. Image quality of intrinsically
gated CT was judged to be equivalent to
extrinsically gated ones. Additionally
4D datasets were calculated using both
gating methods. Values for expiratory,
inspiratory and tidal lung volumes
determined with the two gating methods were comparable and correlated
well with values known from the
literature. We could show that intrinsic
respiratory gating in rodents makes
additional gating hardware and preparatory efforts superfluous. This
method improves image quality and
allows derivation of functional data.
Therefore it bears the potential to find
wide applications in small-animal CT
imaging.
Keywords CT .
Small-animal imaging .
Flat-panel detector . Intrinsic gating
enables reconstruction of a 4D time series that can be used
to calculate functional parameters such as respiratory tidal
volume and cardiac ejection fraction [6–8].
Regardless of whether retrospective or prospective
gating is used, all gating methods that are currently in
use depend on an extrinsic sensor to derive a gating
reference signal. Such a sensor could be a respiratory
cushion that is placed below the chest of the small animal
[1, 6, 7] or an optical system to deduce the breathing
1376
movements [9, 10]. For cardiac gating, EKG electrodes are
routinely used.
Intrinsic methods extract the gating information from the
acquired projection data directly, obviating the need for
additional hardware or preparatory efforts. Intrinsic methods have been described for human cardiac [11, 12] as well
as respiratory gated CT imaging [13, 14]. However,
intrinsic gating methods for small animals have not yet
been described.
Human and small-animal CT instrument characteristics
and data acquisition parameters vary substantially. To the
best of our knowledge, this paper describes the first
intrinsic respiratory gating method for small-animal imaging that takes into account the characteristics of smallanimal cone-beam CT instruments such as a relatively large
z-coverage, relatively slow data acquisition, continuous
volume acquisition and non-spiral scanning. In this
research we successfully demonstrated that, using intrinsic
respiratory gating in small animals, it is possible to achieve
the same image quality as that from the established
extrinsic gating using external gating hardware. It is also
possible to reconstruct 4D data sets, thus enabling derivation of functional parameters of respiration.
tion translates into a minimal detectable feature size of
200 μm.
Each projection image acquired during gantry rotation is
time stamped and is labelled with the angle of acquisition.
Gantry rotation times can be varied from 2 s to 19 s in steps
of 1 s, the maximum total CT data acquisition time being
80 s. The tomographic image reconstruction is based on a
modified Feldkamp algorithm [16].
For each animal examined, a total data acquisition time
of 80 s was used with a gantry rotation time of 5 s. This
resulted in a projection data set over 16 full rotations. A
tube voltage of 80 kV and a tube current of 50 mA with
continuous radiation were selected. Both extrinsic as well
as intrinsic motion-gated reconstruction was performed for
each animal. The reconstruction field of view was 4.5 cm
transaxially with a reconstruction matrix of 512×512 pixels
and an axial slice spacing 0.2 mm resulting in a voxel size
of 0.08×0.08×0.2 mm3. A sharp reconstruction kernel
(H80s) was used for image reconstruction.
All gating algorithms contained a procedure to obtain a
gating reference signal. Essentially, both gating methods
compared herein differ only in the way the reference signal
is derived.
Materials and methods
Derivation of a gating
reference signal for extrinsic gating
Flat-panel-based volume CT instruments
and data acquisition
A prototype CT instrument (Siemens Medical Solutions,
Forchheim, Germany) was used for gating experiments.
The technical CT setup is identical to that described in [7,
15] where extrinsic gating was originally tested [7]. Its
main features are a flat-panel detector and a modified X-ray
tube, both mounted on a multi-slice CT gantry. Taking the
geometry of the CT system setup into consideration, the
instrument’s total field of view is 25×25×18 cm3. For rat
and mouse CT imaging, the active detector area was limited
to 192 lines in z-direction and 1,024 rows in x-y-direction
to increase the frame rate. The detector was read out in a
2×2 binning mode, meaning that four neighbouring pixels
were averaged. This resulted in a decreased field of view of
25×25×4.5 cm3, which was still big enough to cover the
entire thorax and diaphragm of a rat. The resulting frame
rate was 100 frames per second (fps), which translates into
an exposure time of 10 ms per projection.
Rabbits were examined without reduction of the active CT
detector area, because the lung did not fit in the reduced field
of view that was employed for rats and mice. The detector
read out frame rate was therefore reduced to 30 fps. All other
parameters were the same for rabbit CT imaging.
The spatial resolution of the CT system, as computed by
examining a tungsten wire phantom, is 24 lp/cm at 10%
modulation transfer function. This isotropic spatial resolu-
The aim of this research is to show that the proposed
intrinsic gating method leads to the same results as an
established extrinsic gating method. Therefore the extrinsic
respiratory gating was performed as described previously
[6, 7], including the extrinsic method of gating reference
signal derivation: A commercial small-animal monitoring
unit (1025L and Signal Breakout Module, SA Instruments,
Stony Brook, NY) was used to track the respiration
movements using a pneumatic cushion. The pneumatic
cushion was placed beneath the thorax of the small animal
in prone position. Commercially available software was
used to derive a respiratory gating reference signal.
Derivation of a gating reference signal
for intrinsic gating
In comparison to extrinsic gating, the gating reference
signal in intrinsic gating was derived from the image data
alone. The key innovation described in this paper is the
method used for deriving a gating reference signal from the
raw projection data.
A region of interest (ROI) that covers the diaphragm and
adjacent structures on all projections was defined for each
individual CT raw data set (Fig. 1). Its position was the
same in all projections and was defined in absolute detector
coordinates. The ROI extension in x-y direction was broad
1377
enough so that the ROI covered the lateral extension of
the animal fully on all projections. Within this ROI, the
center of mass (COM) in z-direction (P) was calculated
from the raw projection data. All raw projection values
were gain calibrated, and the value of defective detector
elements was estimated using interpolation [15]. For
calculating the COM, each line sum of projection values
(mz) was multiplied with a weighting factor (z) that
depends on z-position of the particular line. Weighted
projection values from all lines were summed and
divided by the total sum of projection values from the
ROI (M) as shown in:
P¼
X
1X
mz Z; with M ¼
mz
M z
z
(1)
An example of a curve that shows the z-position of the
COM as a function of angular projection position is shown
in (Fig. 2a). The z-position of the COM depends mainly on
two factors: (a) the angular position of the imaging chain
and (b) the position of the diaphragm in the selected ROI
reflecting the phase of respiration. The variations due to the
angular position of the gantry have a fixed periodicity of
500 projections reflecting the number of projections in one
gantry rotation around the animal. The phase of respiration
has a more irregular period and reflects breathing excursion
of the diaphragm along z-axis. In order to derive a gating
reference signal, the influence of angular position on the
resulting curve should be minimized while maximizing the
effect of breathing excursions. This is accomplished by
baseline correction described below. The COM of each
projection position is normalized to the mean of the zposition values at this projection position for all acquired
rotations to decrease the influence of (a). The resulting
curve is shown in Fig. 2b. As can be seen, by explicitly
reducing the angular position dependent oscillations, the
dependence on the effect position of the diaphragm can be
increased. Local maxima of this curve were used as gating
reference points as marked in Fig. 2b.
Fig. 1 An example of a manually selected region of interest (ROI)
placed over the diaphragm and adjacent structures. Note that the
ROI box has been made deliberately larger in x-y direction and
extends beyond the margins of the animal. This is to ensure that the
Motion-gated rebinning and CT reconstruction
As described above, gating reference points were derived for
every respiratory cycle for extrinsic gating by analysing the
compression of the respiratory cushion and for intrinsic
gating by analysing the parameter P derived from raw
projections. All other steps, such as retrospective binning of
projections from several rotations according to their phase,
and volumetric reconstruction using these projection sets,
were identical in both gating methods. In essence, this leaves
the derivation of the gating signal the only difference
between the new intrinsic and established extrinsic gating
method. This assures that potential differences in retrospective binning, interpolation and reconstruction algorithms
cannot cause a difference in the two methods compared.
This is also the reason why all other steps of the gating
algorithm beside the derivation of the gating reference signal
are nearly identical to the already published procedure [7]
and are therefore only briefly summarized below.
The starting point of each respiratory cycle (0% point) was
defined to correspond to the gating reference point of every
motion cycle. In order to reconstruct a given phase of the
respiratory cycle, the projections that were acquired within a
certain time frame around that respiratory phase were selected
for image reconstruction. Time frames were defined by start
and end points, given as the percentage of the cycle length.
The selected projections from the re-binning step,
representing projections pertaining to a given phase of
the respiratory cycle, were then interpolated to yield a new
360° projection data set consisting of 600 evenly
distributed projections. If two or more selected projections
were found to be at identical positions-recall that the
angular position of each projection is recorded during
different rotations-they were averaged to improve the
signal-to-noise ratio of the interpolated projection. If no
projections were found for a selected angular position,
interpolation from the closest neighbouring projections
was performed. Interpolation was weighted with respect to
angular distance.
whole body is within the ROI in all projections. The selection of
ROI is the only manual interaction necessary for the proposed
intrinsic gating algorithm
1378
Fig. 2 The z-position of the
center of mass (COM), calculated from an ROI that included
the diaphragm of a mouse, is
plotted against the projection
number. Each rotation consisted
of 500 projections (i.e., the
marks along the X axis constitute two consecutive rotations).
The z-position of the COM is
influenced by the projection
number-with a periodicity of
500-and the respiratory motion
of the diaphragm. The latter is
visible as long spikes with less
regular periodicity. Normalization with respect to projection
number can accentuate the influence of the respiratory motion
so that a gating signal can be
reliably derived using the local
maxima, shown as circles in the
above example. For the purpose
of illustration, the z-position is
given without (a) and with (b)
normalization with respect to the
projection number. As can be
seen, without this normalization
step one respiration cycle would
have been missed (arrow), jeopardizing the gating results.
(AU = arbitrary unit)
A motion-compensated, “still” image was reconstructed
to illustrate one motion-free phase of the respiratory cycle.
Optimal gating intervals used for computing this still image
depended on the animal being imaged; these were
empirically derived through experimentation. In rats and
mice, a reconstruction phase interval of 20% to 80%
yielded the best. This is because in mice and rats the most
intense motion occurs either before 20% or after 80% of the
respiratory phase. For rabbits, a reconstruction phase
interval of 50% to 100% was selected because little
perceptible motion was found to occur beyond the midpoint of the respiratory cycle. In general, the reconstruction
phase interval should be chosen to cover the duration of
least motion in the respiratory cycle. For comparison, nongated reconstruction was also performed. Here all projections
from all rotations were used for image reconstruction.
In order to visualize different phases of respiration and to
compute functional respiratory data, a 4D time series
of volumetric data was computed. For deriving these
temporally varying images for rats and mice, the respiratory
cycle was additionally divided in the following six
phases: (70–80%), (80–90%), (90–100%), (0–10%), (10–
20%) and (20–30%). In rabbits the 0% to 50% interval was
additionally split in five 10% long phases. Recall that the
most intense motion in rabbits occurs during the first half of
the respiratory cycle.
Animals, contrast media
All animal experiments were approved by the German
(mice and rats) and American (rabbits) Governmental
Review Committees on Animal Care.
Six C3H/HeN wild-type mice (20 g), six healthy
Copenhagen rats (250 g) and two New Zealand white
rabbits (4 kg) were scanned. Four of the rodents (two rats and
two mice) were additionally scanned after intravascular
contrast administration to improve the conspicuity of lung
1379
vessels. For this purpose, 2.5-ml Fenestra-VC (ART
Advanced Research Technologies, Saint-Laurent, CA)-a
blood pool contrast agent with 50 mg iodine/ml-was injected
into the tail vein of the rats, 5 min prior to the data acquisition
[1, 17]. The mice were given 0.5 ml of the same contrast
agent. The rodents were anaesthetized by continuous
inhalation of 3% Sevoflurane (Sevorane, Abbot, Maidenhead, UK) in oxygen during preparation, the injection of
contrast media and data acquisition. Rabbits were anaesthetized by intraperitoneal injection of 1 mg/kg acepromazine,
40 mg/kg ketamine and 6 mg/kg xylazine. The pneumatic
cushion was attached to the animals to record the respiratory
movements. The animals were free-breathing.
Post-processing
The reconstructed imaged datasets were supplemented
by a DICOM3-header to enable importation into standard
post-processing software. InSpace (Siemens Medical
Solutions, Forchheim, Germany) was used for analysis
and generation of appropriate reformations from both 3D and
4D datasets. The Medical Imaging Interaction Toolkit
(German Cancer Research Center, Heidelberg, Germany)
[18] was used to semi-automatically segment the lung
volumes.
Evaluation and data analysis
Image quality of non-gated, intrinsically and extrinsically
gated datasets was compared with respect to the following
score table:
A. Delineation of the diaphragm:
B. Rib delineation:
0 points no clear delineation, severe motion artefacts
1 point some blurring, contours predominantly assessable
2 points clear delineation, no motion artefacts
C. Tracheobronchial tract:
0 points trachea and main bronchi not assessable
1 point trachea and main bronchi assessable, but no
segmental bronchi can be delineated
2 points trachea, main bronchi and segmental bronchi
clearly visualized
D. Delineation of central vessels within the mediastinum:
0 points no clear delineation, severe motion artefacts
1 point some blurring, contours predominantly assessable
2 points clear delineation (n, no motion artefacts
Three readers (SHB, FK, JD) decided scores in
consensus. Sum scores were calculated for each criterion
and each animal. In addition, mean total sum scores
(± standard deviation) of the non-gated, extrinsically and
intrinsically gated datasets in mice, rats and rabbits were
calculated. Differences in the achieved total sum scores
were statistically compared among all three groups using a
non-parametric Friedmann test. Differences between two
groups were analyzed using the non-parametric Wilcoxon
signed rank test. A p-value of <0.05 was considered to
indicate significant differences.
For the determination of respiratory tidal volume the
time points of maximum expiration and inspiration were
selected. Because it was not known whether the time points
of maximum and minimum lung volumes lie within the
same reconstruction phase for each animal, and for each
gating method, the phases of maximum inspiration and
expiration were selected by visual inspection. The lung
volumes at these time points were segmented. Respiratory
tidal volume was calculated as the difference between both
lung volumes.
Results
Comparison of image quality
When comparing the mean sum scores of non-gated and
respiratory-gated scans significant differences between
groups were found by the Friedmann test (p=0.003).
Using extrinsic gating image quality only improved
significantly in mice (p=0.05). In rats improvement of
image quality after extrinsic gating hardly failed significance as compared to non-gated scans, although the overall
sum score was higher. Intrinsic gating resulted in a
significantly image quality improvement for mice
(p=0.03) and rats (p=0.03). However, statistical testing
revealed no significant differences in image quality
between extrinsically and intrinsically gated images. Also
in the two rabbits examined higher sum scores for image
quality were found for each gating method.
By visual inspection the diaphragm was blurred and had
double contour from motion-induced shadowing or ghosting in non-gated datasets. In contrast, it was sharply
delineated in the gated datasets (Figs. 3, 4 and 5). Lung
structures such as bronchi and vessels were also sharper
and better delineated in the gated datasets (Figs. 3, 4 and 5).
Motion artefacts, most pronounced around bony structures
of the rib cage, were considerably diminished in the gated
datasets (Fig. 4). The difference between gated and nongated datasets was most pronounced in rabbit scans,
probably because of greater excursion of the thoracic cage.
While there was obviously a perceptible difference
between the non-gated and gated scans, there was no
significant difference in image quality between the two
different types of gating schemes tested when comparing
1380
Intrinsic gating schemes in multi-detector CT where the
gating signal is derived from the slice-by-slice projection
data have been implemented and shown to work for human
subjects [11–14]. MDCT machines, however, offer less
favorable conditions for intrinsic gating than small-animal
imagers that are based on flat-panel detectors. As a
consequence algorithms developed for MDCT machines
are specifically adapted for human respiratory physiology
that differs significantly from that of small animals.
However, these intrinsic gating algorithms tended to be
complex and difficult to implement. Perhaps not surprisingly, none of them are being used in routine clinical
practice so far.
The main result of this research was to show that in small
animals an intrinsic gating signal derived solely from the
projection data using a relatively simple and easy to
implement algorithm can be used to improve image quality
to the same extent as extrinsic gating derived from external
hardware. We also show the feasibility of high-fidelity 4D
datasets computed using intrinsic gating. The functional
parameters such as respiratory tidal volume, derived from
the intrinsically gated 4D datasets, have the same level of
fidelity as those from extrinsic gating. These values also
compare favourably with the values quoted in the literature,
which were derived using other means.
The ratings and statistics performed in this study resulted
in image quality improvements between gated and nongated CT acquisitions-regardless which gating method was
used. No significant difference in image quality was found
between the two gating methods. The obvious similarity of
respiratory functional parameters derived from both gating
methods further support our conclusion that intrinsic as
well as extrinsic gating not only both improve image
quality, but also do that to a very similar extent.
In the following paragraphs, we briefly outline the main
differences between the intrinsic gating schemes described
in the literature and that proposed here.
Fig. 3 Non-gated (a, d), extrinsically gated (b, e) and intrinsically
gated (c, f) coronal images of a mouse thorax enhanced with a
blood-pool contrast media (same windowing). Magnified views of a
region of interest, as marked in the coronal reformations in the upper
row, are given in the lower row. Both extrinsic and intrinsic gating
improved the sharpness and definition of vessels (small arrows) and
bronchi to a similar extent. The diaphragm, which was blurred and
had a ghost contour in the non-gated images (long arrows in d), was
sharper and without the ghost contour in the gated datasets (long
arrows in e, f)
the mean sum scores in mice, rats and rabbits. The scoring
details are listed for each animal and criterion in Table 1.
Functional lung imaging
The rodents exhibited a gasping type of respiration with
minimal changes in respiratory motion and ventilation
frequency once a steady-state of narcosis was reached.
Respiration rate varied from 20 to 35 min−1 in mice and 20 to
39 min−1 in rats. In rabbits, the expiratory phase was almost
as long as the inspiratory phase. Their respiratory rate ranged
from 25–40 min−1. In the 4D time series, the respiratory
excursions of both the diaphragm and the rib cage could be
visualized. Points of maximum inspiration and expiration
could be determined in all cases and for both gating methods.
Mean expiratory and inspiratory lung volumes
revealed by semi-automated segmentation are shown
in Table 2. No outlier was found. The lung volumes
obtained from the intrinsic and extrinsic datasets were
within standard deviation of each other (Table 2). A
literature search for the approximate values of lung
volumes and the respiratory tidal volumes was conducted. This revealed that the values quoted in the
literature correlated well with values computed using
gated images (Table 2). Minor differences in the values
may derive from different animal sizes, narcosis states,
the physiological conditions under which the animals
were tested and measurement error.
Discussion
1381
Fig. 4 Non-gated (a), extrinsically gated (b) and intrinsically gated
(c) coronal images of a rat thorax after administration of blood-pool
contrast media. Extrinsic as well as intrinsic gating showed
comparable image quality. Using both methods, motion artefacts
around the ribs (black arrow) and double contour along the
diaphragm from motion-induced shadowing (long white arrow)
were diminished, and the visualization of the basal bronchi (short
white arrows) was improved as compared with the non-gated images
In multi-slice spiral CT only parts of the volume are
examined during one revolution around the patient. Since
the detector width in MDCT machines is mostly 2 to 4 cm,
the projection data are severely limited in Z extent.
However the COM along x-y axis can be used to derive
a cardiac gating signal [11]. This is in contrast to the
method presented here where the COM is quite sensitive to
movements of diaphragm and other structures that
predominantly move along the subject’s z-axis.
Most methods for human CT were developed for cardiac
gating because a normal thorax scan usually does not
require respiratory gating. It is possible to adapt the
proposed method based on COM for cardiac gating.
Cardiac motion-which entails Z shortening, helical screw
motion, oblique oscillatory undulations and in-plane (i.e.,
XY) contraction-is much more complex than diaphragmatic movement. Therefore, for this application more
complex post-processing steps will be required, and the
algorithmic complexity may rival that in human CT using
intrinsic gating [11].
The high scan speed of modern human CT machines
ensures that chest scanning can be concluded in one breath
hold. Respiratory gating has been most intensively
investigated in the domain of CT applications in radiation
oncology. One of the proposed schemes in this field is very
similar to the intrinsic gating approach proposed in this
paper [12]. Sonke et al. used projection data from a large
cone-beam flat-panel CT system integrated with a linear
accelerator. They used the projections to derive a 1D signal
along the z-axis that correlated well with the movement of
the diaphragm in the z-direction. In contrast to our
approach, this method assumes that respiratory phases
correlate with the extent of the 1D signal.
Iterative reconstruction algorithms for derivation of
intrinsic gating signal have also been described [19].
These techniques, however, tend to be computationally
intensive and are not (yet) used routinely.
The proposed method can probably be further improved.
However, the similarity of images from intrinsic and
extrinsic gating suggests that the quality of intrinsic gating
signal is already near optimal. Therefore, any algorithmic
improvements would most likely result in small, secondorder improvements in the final image quality. There is also
a limit to which additional improvements add to the image
quality. This is because the positional reproducibility of
thoracic structures from one respiratory cycle to the next is
of the order of 100 μm [20] in rodents. A spatial resolution
exceeding 100 μm is therefore not possible by collecting
projections over multiple respiratory cycles. The influence
of rotation time, total scan length and dose to the image
quality can be assessed and optimised in the future.
Since the proposed intrinsic gating method does not rely
on any special features of the CT machine used, it can be
implemented in any small-animal cone-beam CT systems.
Fig. 5 Non-gated (a), extrinsically gated (b) and intrinsically
gated (c) coronal images from a
rabbit thorax. Without gating,
lung structures, e.g., vessels
(white arrow), diaphragm (black
arrow) as well as lung parenchyma, were blurred. As compared to non-gated scans, the
improvement in the image
quality was comparable regardless of which gating method was
used
1382
Table 1 Results of the multireader analysis of image quality without and with extrinsic and intrinsic gating, respectively
Criterion
Non-gated
Extrinsic gating
Intrinsic gating
Mice (n=6) with score Sum score Mice (n=6) with score Sum score Mice (n=6) with score Sum score
0
Diaphragm assessability
Rib delineation
Tracheobronchial tract
Delineation of central vessels
Mean sum score/ animal ± SD
Diaphragm assessability
Rib delineation
Tracheobronchial tract
Delineation of central vessels
Mean sum score/ animal ± SD
Diaphragm assessability
Rib delineation
Tracheobronchial tract
Delineation of central vessels
Mean sum score/ animal ± SD
1
2
0
5
1
2
4
0
1
5
0
0
5
1
3.8±1.3 (max. 8)
4
2
0
0
4
2
0
1
5
4
2
0
3.8±1.16 (max. 8)
2
0
0
1
1
0
0
2
0
0
2
0
2.5±0.7 (max. 8)
0
7
4
5
7
2
8
11
2
0
1
2
2
1
2
0
0
0
0
3
0
2
0
4
6.5±1.3
0
1
0
1
0
1
0
0
7.5±0.83
0
0
0
0
0
0
0
0
8.0±0.0
6
3
4
2
12
9
10
8
5
5
5
6
11
11
11
12
2
2
2
2
4
4
4
4
1
2
0
0
0
2
0
2
0
4
6.7±1.2
0
1
0
2
0
1
0
1
7.2±1.17
0
0
0
0
0
0
0
0
8.0±0.0
6
4
4
2
12
10
10
8
5
4
5
5
11
10
11
11
2
2
2
2
4
4
4
4
The number of scans that were assigned to score 0, 1 or 2 is indicated for each criterion. In addition the mean total sum scores of six mice,
six rats and two rabbits considering all criteria are given. Extrinsic as well as intrinsic respiratory gating causes a general improvement of
image quality in all animals (p<0.05 in mice and rats; p=0.05 in rabbits). The differences between extrinsic and intrinsic gating were not
significant (SD: standard deviation)
Table 2 Lung volumes of mice (n=6), rats (n=6) and rabbits (n=2) after semi-automated segmentation from extrinsically and intrinsically
gated datasets (SD: standard deviation)
Mice (mean ± SD) [μl]
Extrinsic gating
Expiratory volume
583.7±54.1
Inspiratory volume
798.5±52.8
Tidal volume
214.8±21.9
Intrinsic gating
Expiratory volume
567.3±71.7
Inspiratory volume
791.8±93.8
Tidal volume
224.5±59.1
Difference: intrinsic vs. extrinsic gating
Expiratory volume
16.3±40.4
Inspiratory volume
6.7±65.2
Tidal volume
−9.7±40.9
Reference values from literature
Expiratory volume
400 (*) 430(7)
Inspiratory volume
640 (7)
Tidal volume
260 (*) 210 (7)
Rat (mean ± SD) [ml]
Rabbit (mean ± SD) [ml]
4.33±0.10
5.84±0.14
1.56±0.21
53.2±2.0
67.0±3.8
15.1±1.8
4.28±0.05
5.62±0.13
1.34±0.16
52.6±1.0
69.5±4.6
16.9±5.7
0.04±0.10
0.22±0.23
0.17±0.29
0.6±3.1
−2.5±0.8
−3.1±3.8
3.9 [21] 4.6 (7)
6.17 (7)
0.6–2.0 [21] 1.52 (7)
76.4 [22]
10.8 [23]
The differences between intrinsic and extrinsic gating are also tabulated. All differences are within 1 SD of the values being compared.
Volumes correlate well with reference values from the literature. (*Mouse Phenome Project, The Jackson Laboratory, Bar Harbor, ME)
1383
A prerequisite would be access to some basic scan
parameters such raw projection data. Even existing systems
can be easily adapted with this gating algorithm making
hardware changes superfluous.
Currently, selection of the ROI is the only manual step in
this algorithm. An automated method for the ROI selection,
which would make the process fully user independent, would
facilitate the workflow.
In summary, this paper demonstrates a method for
intrinsic gating from projection data that is suitable for flatpanel or cone-beam small-animal CT instruments. The
proposed algorithm is easy to implement and makes
external gating hardware completely superfluous in smallanimal CT imaging. Since the external gating hardware
typically has to interface with the CT hardware in order to
phase-stamp each projection, a secondary benefit of the
proposed scheme is that it makes gating fully independent
from the CT hardware. This means that gating can be
performed as a post-processing step, without any changes
to the existing CT hardware. Our experience to date shows
that this method is quite robust and reliable as far as
changes in the examination environment are concerned. It
also enables acquisition of 4D functional respiratory data.
Finally, it may even be possible to derive a gating signal
from several animals that are examined by CT simultaneously, allowing a higher throughput of animals. We
expect that all these salient features will lead to widespread
adoption of this gating method in small-animal CT.
Acknowledgment The work was supported by the trans-regional
grant “Vascular Differentiation and Remodelling” of the German
Research Foundation (DFG). We thank Karin Leotta for her excellent
technical assistance during data acquisition and post processing.
References
1. Buliev IG, Badea CT, Kolitsi Z,
Pallikarakis N (2003) Estimation of the
heart respiratory motion with applications for cone beam computed
tomography imaging: a simulation
study. IEEE Trans Inf Technol Biomed
7:404–411
2. Cody DD, Nelson CL, Bradley WM,
Wislez M, Juroske D, Price RE, Zhou
X, Bekele BN, Kurie JM (2004) Murine lung tumor measurement using
respiratory-gated micro-computed
tomography. Invest Radiol 40:263–269
3. Walters EB, Panda K, Bankson JA,
Brown E, Cody DD (2004) Improved
method of in vivo respiratory-gated
micro-CT imaging. Phys Med Biol
49:4163–4172
4. Ford NL, Nikolov HN, Norley CJ,
Thornton MM, Foster PJ, Drangova M,
Holdsworth DW (2005) Prospective respiratory-gated micro-CT of free breathing
rodents. Med Phys 32:2888–2898
5. Hu J, Haworth ST, Molthen RC,
Dawson CA (2004) Dynamic small
animal lung imaging via a postacquisition respiratory gating technique using
micro-cone beam computed tomography. Acad Radiol 11:961–970
6. Drangova M, Ford NL, Detombe SA,
Wheatley AR, Holdsworth DW (2007)
Fast retrospectively gated quantitative
four-dimensional (4D) cardiac micro
computed tomography imaging of freebreathing mice. Invest Radiol 42:85–94
7. Bartling S, Stiller W, Grasruck M,
Schmidt B, Peschke P, Semmler W,
Kiessling F (2007) Retrospective motion-gating in small animal CT of mice
and rats. Invest Radiol 42:704–714
8. Kiessling F, Greschus S, Lichy MP,
Bock M, Fink C, Vosseler S, Moll J,
Mueller MM, Fusenig NE, Traupe H,
Semmler W (2004) Volumetric computed tomography (VCT): a new technology for noninvasive, high-resolution
monitoring of tumor angiogenesis. Nat
Med 10:1133–1138
9. Zaporozhan J, Ley S, Unterhinninghofen
R, Saito Y, Fabel-Schulte M, Keller S,
Szabo G, Kauczor HU (2006) Freebreathing. -dimensional computed
tomography of the lung using prospective
respiratory gating: charge-coupled device
camera and laser sensor device in an
animal experiment. Invest Radiol
41:468–475
10. Ritchie CJ, Hsieh J, Gard MF, Godwin
JD, Kim Y, Crawford CR (1994)
Predictive respiratory gating: a new
method to reduce motion artifacts on
CT scans. Radiology 190:847–852
11. Kachelrieß M, Sennst DA, Maxlmoser
W, Kalender WA (2002) Kymogram
detection and kymogram-correlated
image reconstruction from subsecond
spiral computed tomography scans of
the heart. Med Phys 29:1489–1503
12. Manzke R, Kohler T, Nielsen T,
Hawkes D, Grass M (2004) Automatic
phase determination for retrospectively
gated cardiac CT. Med Phys 31:3345–
3362
13. Sonke JJ, Zijp L, Remeijer P, van Herk
M (2005) Respiratory correlated cone
beam CT. Med Phys 32:1176–1186
14. Zeng R, Fessler JA, Balter JM (2005)
Respiratory motion estimation from
slowly rotating x-ray projections: theory and simulation. Med Phys 32:984–
991
15. Gupta R, Grasruck M, Suess C,
Bartling SH, Schmidt B, Stierstorfer K,
Popescu S, Brady T, Flohr T (2006)
Ultra-high resolution flat-panel volume
CT: fundamental principles, design
architecture, and system characterization. Eur Radiol 30:337–343
16. Feldkamp LA, Davis LC, Kress JW
(1984) Practical cone-beam algorithm.
J Opt Soc Am 1:612–619
17. Bartling S, Stiller W, Semmler W,
Kiessling F (2007) Small animal Computed Tomography Imaging. CMIR 3:45–
59
18. Wolf I, Vetter M, Wegner I, Bottger T,
Nolden M, Schobinger M, Hastenteufel
M, Kunert T, Meinzer HP (2004) The
medical imaging interaction toolkit
(MITK): a toolkit facilitating the creation of interactive software by extending VTK and ITK. Proceedings of SPIE
5367:16–27
19. De Man B, Edic P, Basu S (2005) An
iterative algorithm for time-resolved
reconstruction of a CT scan of a beating
heart. Proceedings of Fully 3D, The 8th
international meeting on fully threedimensional image reconstruction in
radiology and nuclear medicine:
pp 356–359
1384
20. Mai W, Badea CT, Wheeler CT,
Hedlund LW, Johnson GA (2005)
Effects of breathing and cardiac motion
on spatial resolution in the microscopic
imaging of rodents. Magn Reson Med
53:858–665
21. Sharp P, La Regina M, LaRegina M
(1998) The Laboratory Rat. CRC Press,
Boca Raton, FL, USA
22. Krause MF, Hoehn T (1999) Decrease
in lung volume depends on endexpiratory pressure in a rabbit model of
airway lavage. Respiration 66:259–264
23. Zhu GF, Zhang W, Zong H, Liang Y
(2006) Effects of continuous tracheal
gas insufflation during pressure limited
ventilation on pulmonary surfactant in
rabbits with acute lung injury. Chin
Med J (Engl) 119:1415–1420