# Effect of Edge-Preserving

Authors: Miwa Okumura; Takamasa Ota; Kazuhisa Kainuma; James W. Sayre; Michael McNitt-Gray; Kazuhiro Katada
Journal: International Journal of Biomedical Imaging
 (2008)

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## Abstract

Objective. For the
multislice CT (MSCT) systems with a larger
number of detector rows, it is essential to
employ dose-reduction techniques. As reported in
previous studies, edge-preserving adaptive image
filters, which selectively eliminate only the
noise elements that are increased when the
radiation dose is reduced without affecting the
sharpness of images, have been developed. In the
present study, we employed receiver operating
characteristic (ROC) analysis to assess the
effects of the quantum denoising system (QDS),
which is an edge-preserving adaptive filter that we
have developed, on low-contrast resolution, and
to evaluate to what degree the radiation dose
can be reduced while maintaining acceptable
low-contrast resolution.
Materials and Methods. The low-contrast phantoms (Catphan 412) were scanned at various tube current settings, and ROC analysis was then performed for the groups of images obtained with/without the use of QDS at each tube current to determine whether or not a target could be identified. The tube current settings for which the area under the ROC curve (Az value) was approximately 0.7 were determined for both groups of images with/without the use of QDS. Then, the radiation dose reduction ratio when QDS was used was calculated by converting the determined tube current to the radiation dose.
Results. The use of the QDS edge-preserving adaptive image filter allowed the radiation dose to be reduced by up to 38%.
Conclusion. The QDS was found to be useful for reducing the radiation dose without affecting the low-contrast resolution in MSCT studies.


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## Body

## 1. Introduction

Compared
with conventional X-ray CT
systems, the time required for scanning various anatomical regions with various
slice thicknesses has been markedly reduced due to the introduction of
multislice CT (MSCT) systems with a larger number of detector rows. However,
since it is now possible to scan a wide range with a thin slice thickness
during a single breath-hold, there has been an increasing concern regarding the
higher radiation doses in a wide range of clinical applications. In addition,
due to the high-speed scanning capabilities of the latest MSCT scanners that
allow the same range to be scanned repeatedly at short intervals, further
efforts in the area of image processing must be made to reduce the radiation
dose in order to fully exploit the benefits of MSCT. As the dose is reduced,
image noise is increased. A lowpass filter processing can reduce image noise, but
the edges of objects become less clear and the sharpness of images is reduced.
To address these issues, the development of edge-preserving adaptive filters,
which selectively eliminate only the noise elements that are increased when the
radiation dose is reduced without affecting edge intensity, has been reported [1–4].We
have developed the quantum denoising system (QDS), which is one type of
edge-preserving adaptive filter, and have assessed its physical characteristics
[5]. QDS is an adaptive filter that extracts the three-dimensional edge
intensity for each pixel and adjusts the amount of smoothing according to the
edge intensity. Specifically, the amount of smoothing is increased in uniform
regions with little edge content in order to reduce image noise and the amount
of smoothing is reduced in regions near stronger edges so that the edges are
maintained. In a previous study [5], we reported the physical characteristics
of QDS, for example, relationships between edge intensity and frequency
response (modulation transfer function; MTF), and noise characteristics (noise
power spectrum; NPS). It was shown that the MTF curve varied smoothly according
to the edge intensity and that the noise reduction characteristics obtained in
QDS-processed images were comparable to those obtained in Gaussian
filter-processed images.In
the present study, we evaluated the effectiveness of the QDS using receiver
operating characteristic (ROC) analysis [6–12] from viewpoint of radiation dose
reduction. ROC analysis was employed for the objective evaluation of QDS in
order to evaluate how far the dose can be reduced with QDS while maintaining an
acceptable low-contrast resolution.


## 2. Materials and Methods

### 2.1. QDS Algorithm

The quantum
denoising system (QDS) is a three-dimensional edge-preserving adaptive filter that
has been developed [5] which aims to reduce noise while maintaining the spatial
resolution in the XY plane without increasing the effective slice thickness in
the Z direction. First, smoothing processing is performed on the input image
using a lowpass filter to reduce high-frequency noise elements, while
sharpening processing is performed on the input image using a highpass filter
to enhance fine structures. The smoothed image and the sharpened image are
defined as Smooth(x,y,z) and Sharp(x,y,z), respectively. The edge
elements that should be maintained are expressed by the following formula for
each pixel: (1)Edge(x,y,z)=|Sharp(x,y,z)−Smooth(x,y,z)|. The
edge elements are then converted to values between 0 and 1 based on the edge
sensitivity curve and used to calculate the appropriate blending ratio for Smooth(x,y,z) and Sharp(x,y,z). The edge sensitivity curve w(Edge(x,y,z)) is
expressed by the following formula using a sigmoid function:(2)w(Edge(x,y,z))=((e(1/α)⋅(Edge(x,y,z)−β)−e(−1/α)⋅(Edge(x,y,z)−β))(e(1/α)⋅(Edge(x,y,z)−β)+e(−1/α)⋅(Edge(x,y,z)−β))+1)2. In
this formula, α is a parameter indicating the gradient of the rising slope of
the curve and β is a parameter indicating the threshold value of the target
edge intensity. Both are determined based on the CT number distribution for
each anatomical region. As shown in Figure 1, the blending ratio w(Edge(x,y,z)) is close to 0 when the edges in the image are soft and only noise elements are
present, while it is close to 1 when the edges in the image are sharp and many
fine structures are present. By defining the blending formula for Smooth(x,y,z) and Sharp(x,y,z) obtained by QDS as Smooth(x,y,z)⋅(1−w(Edge(x,y,z))+Sharp(x,y,z)⋅w(Edge(x,y,z)),
noise elements are reduced by increasing the blending ratio of the smoothed
image in areas with low edge intensity, while fine structures are maintained by
increasing the blending ratio of the sharpened image in areas with high edge
intensity.Figure 1
Quantum denoising system (QDS) algorithm. The results of smoothing filtering
and sharpening filtering are blended together based on the edge intensity.


### 2.2. ROC Analysis

A
64-slice CT scanner (Aquilion; Toshiba Medical Systems Corporation, Otawara, Tochigi, Japan)
was used to scan low-contrast phantoms (Catphan 412, CTP263 module; Phantom
Laboratory, NY, USA).
Four types (0.1%, 0.3%, 0.5%, and 1.0%) of low-contrast targets of eight sizes
(2 mm, 3 mm, 4 mm, 5 mm, 7 mm, 9 mm, 12 mm, and 15 mm in diameter) are embedded
in the CTP263 module. Of these 32 targets, four 0.3% low-contrast targets with
diameters of 2 mm, 3 mm, 4 mm, and 5 mm were used for assessment. The
appearance of the CTP263 module and the locations of the targets are shown in
Figure2. The scan conditions were 120 kV, 1 s/rot., and nonhelical scanning.
The tube current was adjusted to 12 settings (320 mA, 300 mA, 250 mA, 200 mA,
180 mA, 160 mA, 140 mA, 120 mA, 110 mA, 100 mA, 90 mA, and 80 mA) and 40 images
were obtained at each tube current. The scan field of view (FOV) was 240 mm.
The image reconstruction conditions were as follows. The standard abdominal
function (FC13) was used as the reconstruction kernel, and 8-mm-slice images
were obtained by stacking four 2-mm-slice images. The reconstruction FOV was
200 mm based on the external dimensions of the Catphan phantom.Figure 2
Appearance of the low-contrast phantom and locations of targets. Four types
of targets (enclosed in thick lines) were used for assessment.The
40 images obtained at each tube current were then QDS-processed to generate
QDS-processed images. In addition, limited regions including only one target
and regions including only noise elements were extracted from each image to
generate target-positive and target-negative images. The number of images for
image interpretation was 80 images for each set of conditions (target size,
tube current, and with/without QDS). To reduce the number of images for image
interpretation, the pilot study was conducted to initially determine the
appropriate three tube current settings for each target size. By performing
this pilot study, the total number of images for image interpretation was
reduced to 1920 images (80 images × 4 target sizes × 3 tube current settings ×
with/without QDS). For image interpretation, 80 images in the image group with
the same conditions (same target size, same tube current, and with/without QDS)
were presented in a randomized order to each observer, and image interpretation
was repeated for the number of conditions that were present. Examples of images
extracted for image interpretation are shown in Figure3.Figure 3
Examples of images extracted for image interpretation. The top row shows
images containing targets and the bottom row shows images containing only noise
elements. It should be noted that these images are examples intended to aid
understanding, not images that were actually used for image interpretation.Image
interpretation was performed by five radiologists with 10 to 15 years of
clinical experience, and the rating of the confidence level was performed by
marking the obtained confidence level on a continuous scale bar using the
continuous confidence rating method [11]. It should be noted that before the
main experiment, the 40 extracted images were provided to each observer in
order to practice image interpretation. These images were representative images
for which the Az value for all observers was expected to be approximately 0.7
and were not used for the main experiment.The
data for the rated confidence levels was input into the ROC analysis program
(ROCKIT) developed by Metz at the University of Chicago, and the binormal ROC curves and
parameters were estimated. The latest version of the ROCKIT program, which
includes DBM MRMC analysis, can be obtained fromhttp://www- radiology.uchicago.edu/krl/KRL_ROC/software_index.htm (accessed March
1, 2008).ROC
analysis was performed to determine the tube current settings for which the
mean Az value just exceeds 0.7 from the three tube current settings selected by
the pilot study for each image group with/without the use of QDS. If the mean
Az value was lower than 0.6 or greater than 0.8 for a certain target size, the
image interpretation experiment was performed again after shifting the tube
current one level higher/lower for only that target size. The threshold value
(Az = 0.7) was selected so that it would be rather difficult to determine
whether or not a tumor could be identified in clinical practice [9]. MRMC
analysis was used to test differences between the mean Az values for different
tube currents, and the 95% confidence intervals and P-values were then
calculated.


## 2.1. QDS Algorithm

The quantum
denoising system (QDS) is a three-dimensional edge-preserving adaptive filter that
has been developed [5] which aims to reduce noise while maintaining the spatial
resolution in the XY plane without increasing the effective slice thickness in
the Z direction. First, smoothing processing is performed on the input image
using a lowpass filter to reduce high-frequency noise elements, while
sharpening processing is performed on the input image using a highpass filter
to enhance fine structures. The smoothed image and the sharpened image are
defined as Smooth(x,y,z) and Sharp(x,y,z), respectively. The edge
elements that should be maintained are expressed by the following formula for
each pixel: (1)Edge(x,y,z)=|Sharp(x,y,z)−Smooth(x,y,z)|. The
edge elements are then converted to values between 0 and 1 based on the edge
sensitivity curve and used to calculate the appropriate blending ratio for Smooth(x,y,z) and Sharp(x,y,z). The edge sensitivity curve w(Edge(x,y,z)) is
expressed by the following formula using a sigmoid function:(2)w(Edge(x,y,z))=((e(1/α)⋅(Edge(x,y,z)−β)−e(−1/α)⋅(Edge(x,y,z)−β))(e(1/α)⋅(Edge(x,y,z)−β)+e(−1/α)⋅(Edge(x,y,z)−β))+1)2. In
this formula, α is a parameter indicating the gradient of the rising slope of
the curve and β is a parameter indicating the threshold value of the target
edge intensity. Both are determined based on the CT number distribution for
each anatomical region. As shown in Figure 1, the blending ratio w(Edge(x,y,z)) is close to 0 when the edges in the image are soft and only noise elements are
present, while it is close to 1 when the edges in the image are sharp and many
fine structures are present. By defining the blending formula for Smooth(x,y,z) and Sharp(x,y,z) obtained by QDS as Smooth(x,y,z)⋅(1−w(Edge(x,y,z))+Sharp(x,y,z)⋅w(Edge(x,y,z)),
noise elements are reduced by increasing the blending ratio of the smoothed
image in areas with low edge intensity, while fine structures are maintained by
increasing the blending ratio of the sharpened image in areas with high edge
intensity.Figure 1
Quantum denoising system (QDS) algorithm. The results of smoothing filtering
and sharpening filtering are blended together based on the edge intensity.


## 2.2. ROC Analysis

A
64-slice CT scanner (Aquilion; Toshiba Medical Systems Corporation, Otawara, Tochigi, Japan)
was used to scan low-contrast phantoms (Catphan 412, CTP263 module; Phantom
Laboratory, NY, USA).
Four types (0.1%, 0.3%, 0.5%, and 1.0%) of low-contrast targets of eight sizes
(2 mm, 3 mm, 4 mm, 5 mm, 7 mm, 9 mm, 12 mm, and 15 mm in diameter) are embedded
in the CTP263 module. Of these 32 targets, four 0.3% low-contrast targets with
diameters of 2 mm, 3 mm, 4 mm, and 5 mm were used for assessment. The
appearance of the CTP263 module and the locations of the targets are shown in
Figure2. The scan conditions were 120 kV, 1 s/rot., and nonhelical scanning.
The tube current was adjusted to 12 settings (320 mA, 300 mA, 250 mA, 200 mA,
180 mA, 160 mA, 140 mA, 120 mA, 110 mA, 100 mA, 90 mA, and 80 mA) and 40 images
were obtained at each tube current. The scan field of view (FOV) was 240 mm.
The image reconstruction conditions were as follows. The standard abdominal
function (FC13) was used as the reconstruction kernel, and 8-mm-slice images
were obtained by stacking four 2-mm-slice images. The reconstruction FOV was
200 mm based on the external dimensions of the Catphan phantom.Figure 2
Appearance of the low-contrast phantom and locations of targets. Four types
of targets (enclosed in thick lines) were used for assessment.The
40 images obtained at each tube current were then QDS-processed to generate
QDS-processed images. In addition, limited regions including only one target
and regions including only noise elements were extracted from each image to
generate target-positive and target-negative images. The number of images for
image interpretation was 80 images for each set of conditions (target size,
tube current, and with/without QDS). To reduce the number of images for image
interpretation, the pilot study was conducted to initially determine the
appropriate three tube current settings for each target size. By performing
this pilot study, the total number of images for image interpretation was
reduced to 1920 images (80 images × 4 target sizes × 3 tube current settings ×
with/without QDS). For image interpretation, 80 images in the image group with
the same conditions (same target size, same tube current, and with/without QDS)
were presented in a randomized order to each observer, and image interpretation
was repeated for the number of conditions that were present. Examples of images
extracted for image interpretation are shown in Figure3.Figure 3
Examples of images extracted for image interpretation. The top row shows
images containing targets and the bottom row shows images containing only noise
elements. It should be noted that these images are examples intended to aid
understanding, not images that were actually used for image interpretation.Image
interpretation was performed by five radiologists with 10 to 15 years of
clinical experience, and the rating of the confidence level was performed by
marking the obtained confidence level on a continuous scale bar using the
continuous confidence rating method [11]. It should be noted that before the
main experiment, the 40 extracted images were provided to each observer in
order to practice image interpretation. These images were representative images
for which the Az value for all observers was expected to be approximately 0.7
and were not used for the main experiment.The
data for the rated confidence levels was input into the ROC analysis program
(ROCKIT) developed by Metz at the University of Chicago, and the binormal ROC curves and
parameters were estimated. The latest version of the ROCKIT program, which
includes DBM MRMC analysis, can be obtained fromhttp://www- radiology.uchicago.edu/krl/KRL_ROC/software_index.htm (accessed March
1, 2008).ROC
analysis was performed to determine the tube current settings for which the
mean Az value just exceeds 0.7 from the three tube current settings selected by
the pilot study for each image group with/without the use of QDS. If the mean
Az value was lower than 0.6 or greater than 0.8 for a certain target size, the
image interpretation experiment was performed again after shifting the tube
current one level higher/lower for only that target size. The threshold value
(Az = 0.7) was selected so that it would be rather difficult to determine
whether or not a tumor could be identified in clinical practice [9]. MRMC
analysis was used to test differences between the mean Az values for different
tube currents, and the 95% confidence intervals and P-values were then
calculated.


## 3. Results

The
ROC curves of five observers for images with/without the use of QDS at
different tube current settings were obtained for the 0.3% low-contrast targets
of all sizes (2, 3, 4, and 5 mm). An example of the ROC curves is shown in Figure4 for the 4 mm target. Figure 4(a) shows the ROC analysis results for images
obtained at 140 mA without the use of QDS, and Figure 4(b) shows the ROC analysis results for images
obtained at 90 mA with the use of QDS.The ROC curves of five observers for images with/without the use of QDS for
the target with a diameter of 4 mm. The low-contrast resolution for the images
obtained at 140 mA without QDS was statistically equivalent to that of the
images obtained at 90 mA with QDS.
(a)(b)For the
4 mm target images without QDS, the mean Az value was 0.682 (<0.7) at 120 mA, 0.730 (>0.7) at 140 mA, and 0.851 (≫0.7) at 160 mA, with the
mean Az value just exceeding 0.7 when the tube current was set at 140 mA. On
the other hand, the mean Az value was 0.632 (<0.7) at 80 mA, 0.759 (>0.7) at 90 mA, and 0.765 (≫0.7) at 100 mA for the images obtained with
the use of QDS, with the mean Az value exceeding 0.7 when the tube current was
set to 90 mA. These results show that the mean Az values just exceeding 0.7
were 0.730 for images obtained at 140 mA without QDS and 0.759 for images
obtained at 90 mA with QDS.Similar
results were obtained for each of the other target sizes. For the 2 mm target,
the mean Az values just exceeding 0.7 were 0.709 for images obtained at 320 mA
without QDS and 0.760 for images obtained at 200 mA with QDS. For the 3 mm
target, the mean Az values just exceeding 0.7 were 0.706 for images obtained at
180 mA without QDS and 0.704 for images obtained at 120 mA with QDS. For the 5
mm target, the mean Az values just exceeding 0.7 were 0.717 for images obtained
at 100 mA without QDS and 0.721 for images obtained at 80 mA with QDS. These
results are shown in Table1.Table 1
95% confidence intervals of the differences in average Az values andP-values obtained using MRMC analysis for low-contrast detection task for each target
size (2, 3, 4, 5 mm) using images with and without QDS filter. These results
also list the tube current used for each condition (target size, with or
without QDS filter) to obtain a mean Az value that just exceeded 0.7. In all
cases, “0” was included in the 95% confidence intervals and the P-values
were large.
Target diameterQDSTube currentMean Az valueDifference95% confidence intervalP-value2 mmWithout320 mA0.709−0.051−0.1760.0740.410With200 mA0.7603 mmWithout180 mA0.7060.002−0.0740.0780.911With120 mA0.7044 mmWithout140 mA0.730−0.029−0.1330.0750.566With90 mA0.7595 mmWithout100 mA0.717−0.004−0.0800.0720.880With80 mA0.721


### 3.1. MRMC Analysis

The MRMC
statistical analysis [12–15] was used to test whether or not there were
statistically significant differences between the images acquired with QDS and
the images acquired without QDS for different tube current settings for which
the mean Az value just exceeds 0.7. Specifically, the 95% confidence intervals
of the differences in average Az values between the images with QDS and those
without QDS were calculated and the P-values were then calculated for
the null hypothesis that there were no differences in the mean Az values. The
results are also included in Table 1. In all cases, “0” was included
in the 95% confidence intervals and the P-values were large (>.4).
It was therefore concluded that statistically significant differences could not
be detected for the mean Az values between the images with QDS and those
without QDS for different tube currents and the results shown in Table 1 were
reasonable.


### 3.2. Dose Reduction

Given
that the conditions in Table1 were found to be statistically equivalent in
terms of this low-contrast performance task, the dose reduction for each condition
(target size and tube current setting) from the use of QDS was calculated.
Table 2 shows the tube current settings, resulting CTDI100 values,
and dose reduction possible from using the QDS filter for the 0.3% low-contrast
targets at which the statistically equivalent performance values were observed
for images obtained with/without the use of QDS. These values ranged from 38%
dose reduction for the 2 mm targets to 20% for the 5 mm targets.Table 2
Comparison of the tube current necessary for each condition (target size,
with or without QDS filter) to obtain approximately equivalent mean Az values.
The last column shows the dose reduction possible with QDS for each target size
at statistically equivalent performance levels based on mean Az values.
Without QDSWith QDSTarget diameterTube currentMean Az valueExposure doseTube currentMean Az valueExposure doseDose reduction ratio2 mm320 mA0.70944.5 mGy200 mA0.76027.8 mGy38%3 mm180 mA0.70625.0 mGy120 mA0.70416.7 mGy33%4 mm140 mA0.73019.5 mGy90 mA0.75912.5 mGy36%5 mm100 mA0.71713.9 mGy80 mA0.72111.1 mGy20%


## 3.1. MRMC Analysis

The MRMC
statistical analysis [12–15] was used to test whether or not there were
statistically significant differences between the images acquired with QDS and
the images acquired without QDS for different tube current settings for which
the mean Az value just exceeds 0.7. Specifically, the 95% confidence intervals
of the differences in average Az values between the images with QDS and those
without QDS were calculated and the P-values were then calculated for
the null hypothesis that there were no differences in the mean Az values. The
results are also included in Table 1. In all cases, “0” was included
in the 95% confidence intervals and the P-values were large (>.4).
It was therefore concluded that statistically significant differences could not
be detected for the mean Az values between the images with QDS and those
without QDS for different tube currents and the results shown in Table 1 were
reasonable.


## 3.2. Dose Reduction

Given
that the conditions in Table1 were found to be statistically equivalent in
terms of this low-contrast performance task, the dose reduction for each condition
(target size and tube current setting) from the use of QDS was calculated.
Table 2 shows the tube current settings, resulting CTDI100 values,
and dose reduction possible from using the QDS filter for the 0.3% low-contrast
targets at which the statistically equivalent performance values were observed
for images obtained with/without the use of QDS. These values ranged from 38%
dose reduction for the 2 mm targets to 20% for the 5 mm targets.Table 2
Comparison of the tube current necessary for each condition (target size,
with or without QDS filter) to obtain approximately equivalent mean Az values.
The last column shows the dose reduction possible with QDS for each target size
at statistically equivalent performance levels based on mean Az values.
Without QDSWith QDSTarget diameterTube currentMean Az valueExposure doseTube currentMean Az valueExposure doseDose reduction ratio2 mm320 mA0.70944.5 mGy200 mA0.76027.8 mGy38%3 mm180 mA0.70625.0 mGy120 mA0.70416.7 mGy33%4 mm140 mA0.73019.5 mGy90 mA0.75912.5 mGy36%5 mm100 mA0.71713.9 mGy80 mA0.72111.1 mGy20%


## 4. Discussion

Table2 shows the values obtained by converting the tube current (mA) to dose (CTDI100,
mGy). The results showed that for the target with a diameter of 2 mm, the
low-contrast resolution at 27.8 mGy with the use of QDS was equivalent to that
at 44.5 mGy without the use of QDS, corresponding to a dose reduction ratio of
38%. In addition, for the target with a diameter of 3 mm, the low-contrast resolution
at 16.7 mGy with the use of QDS was equivalent to that at 25.0 mGy without the
use of QDS, corresponding to a dose reduction ratio of 33%. Similarly, the dose
reduction ratio was 36% for the target with a diameter of 4 mm and 20% for the
target with a diameter of 5 mm. It should be noted that since the dose is
proportional to the tube current, the dose values shown in Table 2 were
obtained by performing proportional conversion based on the dose (CTDI100)
of 44.5 mGy at 320 mA.In
the present study, the tube current settings for which the Az values exceeded
0.7 were determined from the groups of images obtained with/without the use of
QDS, and it was assumed that the low-contrast resolution values at the dose for
the determined tube current settings were equivalent. Then, the dose reduction
ratio with the use of QDS was calculated from the tube current settings. A
previous study concerning adaptive filters using ROC analysis reported that the
Az values were improved when the adaptive filters were applied to images
obtained at the same tube current and discussed how far the dose can be reduced
[1]. We have expanded their results for the combination of various target sizes
and various tube current settings, and performed statistical analysis using
MRMC methods.One
limitation in this study was that we used a low-contrast resolution phantom
(Catphan 412) where several uniform targets are embedded in a uniform
background because the purpose of the present study was to convert the effects
of the edge-preserving adaptive filters to digital form from the viewpoint of
dose reduction. In the sense that system performance was to be evaluated
objectively, we used a Catphan phantom with high reproducibility, which is
widely employed for the evaluation of low-contrast resolution. In clinical
practice, low-contrast detectability depends on the structure of the imaging
target and surrounding anatomic (and pathologic) structures. Different organs
may have different low-contrast detectabilities even with the same condition.
Therefore, further study should investigate more complex target and background
structures that simulate specific clinical cases.The
use of the QDS edge-preserving adaptive filter allowed the dose to be reduced
by 38%, 33%, 36%, and 20% for 0.3% low-contrast targets with diameters of 2 mm,
3 mm, 4 mm, and 5 mm, respectively. It is therefore concluded that QDS is a
significant advance in the field of image processing and that it is useful for
reducing the dose in MSCT studies, in which the higher doses are an important
concern.



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*Source: 379486-2008-11-16.xml*