1 23
Skeletal Radiology
Journal of the International
Skeletal Society A Journal
of Radiology, Pathology and
Orthopedics

ISSN 0364-2348

Skeletal Radiol
DOI 10.1007/
s00256-011-1223-2
Measuring femoral lesions despite CT
metal artefacts: a cadaveric study
Daniel F. Malan, Charl P. Botha, Gert
Kraaij, Raoul M. S. Joemai, Huub
J. L. van der Heide, Rob G. H. H. Nelissen
& Edward R. Valstar

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SCIENTIFIC ARTICLE
Measuring femoral lesions despite CT metal artefacts:
a cadaveric study
Daniel F. Malan & Charl P. Botha & Gert Kraaij &
Raoul M. S. Joemai & Huub J. L. van der Heide &
Rob G. H. H. Nelissen & Edward R. Valstar
Received: 13 April 2011 /Revised: 12 June 2011 /Accepted: 13 June 2011
# The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract
Objective Computed tomography is the modality of choice
for measuring osteolysis but suffers from metal-induced
artefacts obscuring periprosthetic tissues. Previous papers on
metal artefact reduction (MAR) show qualitative improve-
ments, but their algorithms have not found acceptance for
clinical applications. We investigated to what extent metal
artefacts interfere with the segmentation of lesions adjacent to
a metal femoral implant and whether metal artefact reduction
improves the manual segmentation of such lesions.
Materials and methods We manually created 27 peripros-
thetic lesions in 10 human cadaver femora. We filled the
lesions with a fibrotic interface tissue substitute. Each
femur was fitted with a polished tapered cobalt-chrome
prosthesis and imaged twice—once with the metal, and
once with a substitute resin prosthesis inserted. Metal-
affected CTs were processed using standard back-projection
as well as projection interpolation (PI) MAR. Two
experienced users segmented all lesions and compared
segmentation accuracy.
Results We achieved accurate delineation of periprosthetic
lesions in the metal-free images. The presence of a metal
implant led us to underestimate lesion volume and
introduced geometrical errors in segmentation boundaries.
This study was funded by the Netherlands Organization for Scientific
Research (NWO) and the Stichting STW.
All work was performed at Leiden University Medical Center, Leiden,
The Netherlands.
D. F. Malan (*) : G. Kraaij : H. J. L. van der Heide :
R. G. H. H. Nelissen : E. R. Valstar
Department of Orthopaedics, Leiden University Medical Center,
J11-R Albinusdreef 2,
2333ZA Leiden, The Netherlands
e-mail: fmalan@medvis.org
G. Kraaij
e-mail: g.kraaij@lumc.nl
H. J. L. van der Heide
e-mail: H.J.L.van_der_Heide@lumc.nl
R. G. H. H. Nelissen
e-mail: R.G.H.H.Nelissen@lumc.nl
E. R. Valstar
e-mail: e.r.valstar@lumc.nl
D. F. Malan : C. P. Botha
Department of Mediamatics, EEMCS,
Delft University of Technology,
P.O. Box 5031, 2600GA Delft, The Netherlands
C. P. Botha
e-mail: c.p.botha@tudelft.nl
C. P. Botha : R. M. S. Joemai
Department of Radiology, Leiden University Medical Center,
K4, Albinusdreef 2,
2333ZA Leiden, The Netherlands
R. M. S. Joemai
e-mail: R.M.S.Joemai@lumc.nl
E. R. Valstar
Department of Biomechanical Engineering,
Delft University of Technology,
Delft, The Netherlands
Skeletal Radiol
DOI 10.1007/s00256-011-1223-2

Although PI MAR reduced streak artefacts, it led to greater
underestimation of lesion volume and greater geometrical
errors than without its application.
Conclusion CT metal artefacts impair image segmentation.
PI MAR can improve subjective image appearance but
causes loss of detail and lower image contrast adjacent to
prostheses. Our experiments showed that PI MAR is
counterproductive for manual segmentation of peripros-
thetic lesions and should be used with care.
Keywords Computed tomography.Metal artifact
reduction . Osteolysis . Segmentation . Beam hardening
Introduction
Aseptic loosening caused by osteolysis is one of the
foremost problems limiting the survival of hip prostheses
[1]. Plain radiographs (Fig. 1) are the default modality for
evaluating osteolysis [2] but tend to underestimate lesion
volume [1]. Claus et al. [3] even refers to “the lack of any
relationship between the two-dimensional lesion size and
the actual three-dimensional lesion volume.”
In recent years CT has gained popularity for quantifying
periprosthetic osteolysis. There seems to be consensus that
CT has superior sensitivity and measurement accuracy for
the detection and measurement of osteolysis compared to
traditional radiographs [4–8]. Unfortunately CT images
suffer from metal-induced artefacts in the vicinity of metal
prostheses [9, 10]. These most notably arise due to beam
hardening and photon starvation [11, 12].
Although there still exists no general solution for
removing metal-induced artefacts [13], several approaches
have been offered. Glover and Pelc [14] and Kalender et al.
[15] first proposed replacing metal sinogram projections
with interpolations of adjacent data—referred to here as
projection interpolation (PI) metal artefact reduction
(MAR). To our knowledge all MAR techniques that have
found clinical application are based on PI. A notable
example is the algorithm [10] implemented on the Siemens
SOMATOM from 1987 to 1990, and which is still
undergoing further development [9]. Commercial software
such as ScanIP (Simpleware, Exeter, U.K.) offers PI as an
image preprocessing tool. These MAR techniques can lead
to lowering of detail and cause unpredictable secondary
artefacts, such as that described by Mahnken et al. as a
“ground glass like fan-shaped artifact” [16].
In comparison, non-PI algorithms are computationally
expensive and remain confined to academic papers [10, 17–
20]. “Extended CT scale” techniques [21] have been made
redundant by the 16-bit quantization used in modern
scanners such as the Toshiba scanner used in this study.
The aim of this study was to examine the extent to which
the presence of a metal hip prosthesis, and the subsequent
application of PI MAR, affect the segmentation of
periprosthetic fibrous lesions. Does the presence of metal
decrease the manual segmentation performance of such
lesions? Does MAR improve the manual segmentation
compared to the metal-degraded CT images? To answer
these questions we first compare the segmented lesion
volumes to ground truthed volumes obtained by filling each
lesion with water. Second, we compare the segmentation
boundaries between scans acquired under optimal metal-
free scanning to those found in metal-degraded images,
both before and after the application of MAR. Contrast and
image intensity gradients are measured across segmentation
boundaries to help explain the results. This enables us to
either recommend or warn against MAR as a preprocessing
step in assessing periprosthetic lesions.
Materials and methods
Figure 2 shows a flow chart of the complete experimental
work flow. Ten human femora were retrieved post-
mortem from seven donors. These comprised three
female and seven male femora with a mean age of
80.7 years (range 67–98). Dual energy X-ray absorpti-
ometry (DXA) measurements performed prior to prepa-
ration yielded a median T-score of −0.7 (average −1.4)
within a range of −4.9 to +1.1. AT-score of −1 or higher
is considered normal, whereas clinical osteoporosis is
defined by a T-score of −2.5 or lower [22]. All femora
were preserved in formalin and surrounding soft-tissue
Fig. 1 In a clinical radiograph, radiolucent lines adjacent to the
prosthesis indicate osteolysis (arrows)
Skeletal Radiol

removed. We fitted each femur with a polished tapered
cobalt-chrome Exeter size 42-2 stem (Stryker, Limerick,
Ireland). For fixation we used radiopaque contrast-
enhanced bone cement (Palacos, Biomet, Warsaw, IN,
USA). The prostheses were implanted under supervision
of an experienced orthopedic surgeon (H.J.L.vdH.) while
using standard cemented implantation protocol.
To enable scanning each femur with and without the
metal prosthesis, we required removable prostheses. After
each prosthesis was cemented it was mechanically removed
from the femur, leaving the remaining cement mantle and
femur intact (Figs. 3 and 4). This was possible due to the
Exeter prosthesis’s smooth polished surface and tapered
shape.
Each femur was axially bisected so as to intersect the
cement mantle. We subsequently created lesions both
proximally and distally from the sawn-through interface
and at varying locations along the circumference (Fig. 4)
using a rotary burr (Dremel). In total, 27 cavities were
created having a mean volume of 2.4 ml (range 1.1–
5.0 ml). We measured lesion volumes by using a 0.2 ml-
graduated syringe to fill each cavity with water. The lesions
were then drained and filled with a fibrous tissue substitute.
Previous studies used water [23], lean beef mince [8,
24], or an unspecified “soft-tissue equivalent” material to
fill artificially created lesions [3]. In this study we
specifically chose radiologically compatible tissue to
represent the fibrotic zones. On four occasions, real
periprosthetic fibrotic tissue was retrieved during hip
implant revision surgery and its CT opacity measured ex
Fig. 3 The removable Exeter prosthesis is shown partly dislodged
from one of the test femora
Fig. 2 Flow chart of the experi-
ments performed in this study
Skeletal Radiol

vivo. These tissues had a mean opacity of 72 Hounsfield
Units (HU) with standard deviation of 10 HU. This differs
substantially from water (mean 0 HU) and our measure-
ments for lean beef mince (mean 50 HU). After evaluating
several commercially available alternatives we chose
chicken liver, which was considered sufficiently similar
with a mean opacity of 77 HU and standard deviation of 6
HU.
During scanning each femur required an inserted
prosthesis to hold the two bisected halves in place. When
the metal prosthesis was removed we used a mould-cast
resin substitute. The resin had a measured CT opacity of
150 HU, placing it above the opacity of soft tissues and
blood (∼50 HU), but less than bone (> 300 HU) and much
less than metal (>3,000 HU) [25]. The resin prosthesis’s
low radiopacity did not significantly contribute to beam
hardening, the main source of metal-induced artefacts, and
therefore enabled us to acquire optimal images for CT
ground truthing.
Scans were performed on a helical CT scanner (Aquilion
16, Toshiba Medical Systems, Japan) at 135 kVp using a
200 mA tube current. The in-slice voxel spacing was 0.44×
0.44 mm with a slice thickness of 0.5 mm. Following the
advice of Lee et al. [12] and Douglas-Akinwande et al.
[26], we chose a standard smooth reconstruction filter (FC
12) to minimize metal artefacts.
For MAR we used the recent sinogram-interpolation
method of Veldkamp et al. [27]. This algorithm has a lot in
common with the original method of Kalender et al. [15]
but uses raw sinogram data to interpolate metal traces.
Adding a fraction of the original metal signal to the
interpolation has a similar role as the nonzero “confidence
parameter” of Oehler et al. [28] and makes the implant
visible in the final reconstruction.
Each of the 27 fibrotic lesions was independently and
manually segmented by each of two experienced users (F.
M. and G.K.) using MITK, an interactive segmentation
software tool [29]. F.M. and G.K. independently segmented
the resin prosthesis volumes as well as the metal prosthesis
volumes with and without application of MAR. F.M. and G.
K. segmented the volumes sequentially and in randomized
order, with 2 weeks separating their segmentation work.
The volumes of the segmented lesions were compared to
the physically measured ground-truthed fluid volumes. The
metal-affected and MAR image segmentations were regis-
tered to their metal-free counterparts using a 3D iterative
closest point (ICP) algorithm, correcting for translational
and/or rotational offsets between scans. Geometric devia-
tion in each segmented metal or MAR volume was
compared to the corresponding metal-free resin prosthesis
volume. To avoid interobserver bias when comparing
segmentations performed with metal, MAR, or resin
volumes, we always compared pairwise segmentations of
the same lesion on a per-user basis. Measurements by F.M.
and G.K. were treated as separate and not averaged.
The residual shape difference between each segmenta-
tion pair was computed by their Hausdorff distance, mean
Hausdorff distance, and Dice coefficient. The Hausdorff
distance is defined as the global maximum of all the
minimum distances between two surfaces. The mean
Hausdorff distance is the mean minimum distance between
the two surfaces. The Dice coefficient is a ratio between the
volumes enclosed by the two surfaces, defined by c ¼
2 A\Bj j
Aj jþ Bj j and has a value in the range [0,1] where 1 represents
complete overlap between volumes and 0 represents
completely disjoint volumes. A perfectly matched segmen-
tation pair would have a zero Hausdorff distance and a Dice
coefficient of one, whereas a bad match will have a high
Hausdorff distance and Dice coefficient approaching zero.
The Dice coefficient and Hausdorff distance are well suited
to evaluating differences in 3D segmentation such as in Van
der Lijn et al. [30].
For each segmentation boundary we computed the
median image gradient magnitude, as well as the Michelson
contrast between the inner and outer region defined by this
boundary. The Michelson contrast for each lesion is defined
as IoutIinIoutþIin ; where Iin and Iout represent the median image
intensities in a 1 mm wide region symmetrically located
inward and outward of the segmentation border.
Image registration, distance metrics, and contrast metrics
were computed using the Insight Segmentation and Regis-
tration Toolkit (ITK), Visualization Toolkit (VTK) and the
Python programming language. All computations were
performed on the DeVIDE image processing and visuali-
zation platform [31].
We did not assume normal distributions of the measured
differences in volume, edge gradient magnitude, Michelson
Fig. 4 Femoral lesions mechanically created anterior and posterior of
the cement mantle are shown with the prosthesis removed
Skeletal Radiol

contrast, pairwise Hausdorff distances, or Dice coefficients.
This decision was supported by the Shapiro-Wilk test for
normality, indicating that the hypothesis of normality
should be rejected for several of the measurement pairs,
as is also visually evidenced in asymmetry in several of the
measurement distributions (e.g., see Figs. 6 and 8 below).
Distributions of measurements and differences between
measurement pairs are described by nonparametric meas-
ures such as median and interquartile range. Rather than the
Student’s t-test we therefore chose the Wilcoxon signed
rank test to compare measurements of the same quantities
under metal-free, metal-containing, and MAR acquisition.
We furthermore chose not to assume linear relationships
between variables when testing for correlation, choosing
instead to use Spearman’s rank correlation coefficient,
which serves as a nonparametric analogue to Pearson’s
correlation.
Results
To answer whether the presence of metal degrades
segmentation performance, we compared segmentations
performed on the metal-free ground-truthed images to those
of metal-affected images. In metal-free image segmenta-
tions we measured volumes that were not significantly
different (P=0.65) compared to the physically measured
fluid volumes (Fig. 5), while metal-containing CT scans
tended to significantly (P=0.002) underestimate the phys-
ically measured volumes. The Hausdorff distances, mean
Hausdorff distances, and Dice coefficients of metal-affected
versus metal-free images show low dissimilarity albeit with
several outliers (Figs. 6, 7, 8). Michelson contrast across
segmentation boundaries is significantly lower (P=0.002)
than for metal-free scans ( Fig. 9). Image gradient
magnitudes on segmentation boundaries also have a lower
median value compared to metal-free images (Fig. 10),
although this difference is not significant (P=0.811).
The second question is whether PI MAR improves
segmentation performance relative to unprocessed metal-
degraded CT. Unexpectedly, we found that volumes
measured after application of PI MAR were even smaller
than those measured in the metal-affected scans (Fig. 5),
and significantly smaller than the ground-truthed volumes
(P<0.001). We see that the MAR segmentations exhibit
significantly larger geometrical deviations (P<0.001 in all
three cases) from the ground-truthed results than unpro-
cessed metal scans (Figs. 6, 7, 8). Michelson contrast across
segmentation boundaries (Fig. 9) is significantly lower than
for either resin scans (P<0.001) or unprocessed metal (P=
0.003). Image gradient magnitudes on segmentation bound-
aries (Fig. 10) are significantly reduced compared to either
marmetal
A
ve
ra
ge
H
au
sd
or
ff
Di
st
an
ce
(m
m)
4.00
3.00
2.00
1.00
.00
Fig. 7 Mean Hausdorff distances compared to “resin” ground-truthed
results show average segmentation boundary errors
marmetal
H
au
sd
or
ff
di
st
an
ce
(m
m)
20.00
15.00
10.00
5.00
.00
Fig. 6 Hausdorff distances compared to “resin” ground-truthed results
show maximum local segmentation boundary errors
MAR vs FluidMetal vs FluidResin vs Fluid
M
ea
su
re
d
Vo
lu
m
e
Di
ffe
re
nc
e
(m
l)
3.00
2.00
1.00
.00
-1.00
-2.00
-3.00
Fig. 5 Metal-free CT accurately estimates volume, whereas metal
degradation causes volume underestimation. MAR causes even further
volume underestimation
Skeletal Radiol

metal-free ground-truthed or unprocessed metal scans (P<
0.001 in both cases).
We found no significant correlation between the lesion
size or DXA T-score and any of the measured parameters
using Spearman’s rank correlation coefficient. Barring a
statistically significant but small difference in segmentation
volume we found observations between the two indepen-
dent observers to agree well (Table 1).
Discussion
We set out to determine whether the presence of a metal
prosthesis and subsequent projection interpolation metal
artefact reduction (PI MAR) affect the segmentation of
periprosthetic lesions resembling osteolysis. We compared
segmentation volume as well as geometrical deviation
between segmentations performed with and without the
presence of metal and after application of PI MAR.
We believe that the observed trend of lowered segmen-
tation performance due to MAR is widely relevant to the
diagnosis and quantification of periprosthetic tissues from
CT. Our experimental data were obtained under optimal
scanning conditions, with all soft-tissue removed from
around the femora. In the clinical setting the image
degrading effects of metal-induced beam hardening, as well
as secondary artefacts created by MAR, are likely to present
a greater obstacle to lesion detection and quantification than
in the carefully controlled environment described in this
paper. Through inspection we believe that the threshold we
used for identifying metal prosthesis yielded a good
segmentation of the metal boundary while still excluding
all surrounding biological tissue. Using a different threshold
affects the delineation of the interpolation region, and
subsequently also the amount and the location of detail lost
to the MAR algorithm. A detail-retaining compromise
could involve decreasing the interpolation regions’ size at
the cost of artefact suppression.
We found no tendency for manual CT-based segmenta-
tion to either over- or underestimate lesion volume in the
absence of metal hardware. When a metal prosthesis was
introduced, however, lesion volume was underestimated.
This agrees with Walde et al. [8] and Leung et al. [32] who
found that CT neither consistently underestimated nor
overestimated lesion volume, and Stamenkov et al. [23]
who found that CT systematically underestimates lesion
volume in the presence of metal artefacts. We explain this
tendency by our measurements, which show that metal-
induced artefacts cause lower contrast across lesion
boundaries, which negatively influences their visibility.
Contrary to expectation we found that lesion segmenta-
tion deteriorated even further after application of PI MAR,
marmetalresin
Ed
ge
G
ra
di
en
t M
ag
ni
tu
de
( H
U
/ m
m
)
800
600
400
200
Fig. 10 The median edge gradient magnitude across each segmented
lesion’s boundary
marmetalresin
M
ic
he
ls
on
C
on
tra
st
a
cr
os
s
se
gm
en
ta
tio
n
bo
un
da
ry
.8
.7
.6
.5
.4
.3
.2
Fig. 9 The median Michelson contrast across each segmented lesion’s
boundary
marmetal
D
ic
e
Co
ef
fic
ie
nt
1.0
.9
.8
.7
.6
.5
.4
Fig. 8 Dice coefficients compared to “resin” ground-truthed results
show volumetric agreement between segmentations
Skeletal Radiol

with larger associated underestimation of lesion volume and
larger geometrical errors. PI MAR reduced image noise in
homogenous regions, but this was achieved at the cost of a
substantial loss of detail, evidenced by lowered edge
gradients and image contrast across lesion boundaries.
Kalender et al. [15] mentioned that PI MAR works best
for objects with simple near-circular geometries, while
Watzke and Kalender [10] mentioned that PI MAR is well
suited to larger implants consisting of dense metal. This
view is also echoed by Liu et al. [9] who wrote that MAR
improved image quality in scans of large prostheses,
whereas it had a negative effect on small metal objects
due to image blurring. In this regard we expected PI MAR
to be of benefit since the Exeter prosthesis chosen for this
study meets the requirements mentioned above. Except for
its smoother appearance (Fig. 11c), there is little to
recommend the application of PI MAR above the original
metal degraded image (Fig. 11b). Detail in the MAR image
is noticeably blurred—especially in regions closest to the
metal prosthesis. This is supported by a measured lowering
of edge contrast and edge gradient magnitude after
application of PI MAR (Figs. 9 and 10).
Papers showcasing MAR algorithms [16, 17, 19]
emphasize “starburst” artefacts by choosing display win-
dows that create the impression that these artefacts
completely obliterate all image detail in their path. This
study suggests that a human operator who has to manually
delineate structures adjacent to a metal prosthesis might
obtain better segmentations from unfiltered artefact-
containing images than from images processed with PI
MAR. This contrasts with the view that MAR invariably
improves the appearance and usefulness of metal-affected
clinical scans. However, in patients with bilateral prosthe-
ses, as often seen in practice, the beam hardening shadow
connecting the two prostheses is much more pronounced
than in this single prosthesis experiment. In this scenario PI
MAR can improve the subjective appearance of radio-
graphic cross sections by equalizing the shadow regions
[10, 19]. This improvement is often confirmed by radiol-
ogists’ subjective rating [9]. For our application of
measuring periprosthetic lesions, however, there seems to
be a net loss of quantifiable image information when
applying PI MAR.
CT, in the absence of metal artefacts, is an accurate and
unbiased tool for measuring the volume and geometry of
periprosthetic lesions. When adding the presence of a metal
prosthesis the result remains usable, albeit with degraded
image quality, increased difficulty in discerning structures,
and a tendency to underestimate lesion volume. Previous
studies [9, 17] investigating the merits of MAR used
subjective rating scales to assess image quality and limited
quantitative measurements to mean CT number and
Fig. 11 Each of the three image modalities for an image slice that bisects
two fibrous-tissue lesions (arrows). A display window of −1,000 to
3,000 HU was used in all three cases. a The image quality is good with
resin prosthesis in place. b With the metal prosthesis, beam-hardening
artefacts manifest as shadows and blooming of the metal region. c After
in-painting of the metal sinogram the artefacts are reduced but at the
cost of a loss in periprosthetic detail
Parameter Median difference 1st and 3rd quartiles Significance
Volume −0.1 ml −0.3, 0.1 ml 0.007
Hausdorff distance −0.62 mm −3.04, 1.86 mm 0.400
Mean Hausdorff distance −0.009 mm −0.128, 0.137 mm 0.474
Dice coefficient −0.0028 −0.0531, 0.0448 0.453
Median Michelson edge contrast 0.0046 −0.0212, 0.0123 0.885
Median edge gradient magnitude −9 HU/mm −29, 3 HU/mm 0.228
Table 1 Interobserver differen-
ces calculated pair wise over
all lesions according to the
Wilcoxon signed rank test.
The only statistically
significant difference is
a 0.1 ml bias in
measured volume
HU Hounsfield units
Skeletal Radiol

standard deviation within certain regions of interest. A
strength of our study is its quantitative evaluation of
segmentation performance, albeit for a small set of lesions.
A limitation of this study is the inclusion of only 27
fibrotic lesions from 10 human cadaver femora, all using
the same type of metal prosthesis and scanned in the same
CT scanner. We limited ourselves to evaluating a single
software PI MAR implementation, and independent
segmentations were performed by only two operators.
Although the femora were harvested from older patients,
only two of the 10 samples had DXAT-scores suggesting
osteoporosis, whereas osteoporosis may be more common
in patient populations. Our manually created lesions
lacked the radio-dense sclerotic borders that may be found
in clinical practice [33, 34]. The current clinical signifi-
cance of MAR algorithms is low, although it remains an
active field of research. We suggest that in addition to our
general observations, validation should be performed in
any specific clinical setting whenever PI MAR is to be
considered.
Conclusion
Despite its popularity in the literature and superficial
improvements to image appearance, projection interpola-
tion metal artefact reduction (PI MAR) was detrimental to
the user-guided segmentation described in this paper. It
remains to be seen whether other image-based metal
artefact reduction techniques can improve quantitative
segmentation results of such periprosthetic lesions.
Acknowledgments The authors would like to thank Fred van
Immerseel at the LUMC Department of Anatomy for the use of their
facilities and equipment, and for their cooperation in the preparation
of the specimens. Furthermore we thank Koos Geleijns and Paul de
Bruin at the LUMC Department of Clinical Physics, who assisted with
the CT scans, and for their useful comments. Thanks goes out to Tim
J. van der Steenhoven who provided DXA bone density measurements
for the femora. This research is supported by the Dutch Technology
Foundation STW, which is the applied science division of NWO, and
the Technology Programme of the Ministry of Economic Affairs
(project number LKG 7943).
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
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