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RESEARCH ARTICLE
Clinical evaluation of the iterative metal artefact reduction
algorithm for post-operative CT examination after
maxillofacial surgery
1
Arsany Hakim,
1
Johannes Slotboom,
2
Olivier Lieger,
2
Fabian Schlittler,
3
Roland Giger,
4
Chantal Michel,
1
Roland Wiest and
1
Franca Wagner
1
Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital University of Bern, Bern,
Switzerland;
2
Department of Craniomaxillofacial Surgery, Bern University Hospital, Inselspital University of Bern, Bern,
Switzerland;
3
Department of Otorhinolaryngology—Head and Neck Surgery, Bern University Hospital, Inselspital University of
Bern, Bern, Switzerland;
4
University Hospital of Child and Adolescent Psychiatry and Psychotherapy, Bern University Hospital,
University of Bern, Bern, Switzerland
Objectives: Metal artefacts present challenges to both radiologists and clinicians during post-
operative imaging. Such artefacts reduce the diagnostic effectiveness of CT scans and mask
findings that could be vital for patient management. Thus, a powerful artefact reduction tool is
necessary when imaging patients with metal implants. Our aim was to test the recently introduced
iterative metal artefact reduction (iMAR) algorithm in patients with maxillofacial implants.
Methods: Images from 17 patients with diverse maxillofacial metal implants who had
undergone CT scans were qualitatively and quantitatively analyzed before and after metal
artefact reduction with iMAR.
Results: After iMAR application, images exhibited decreased artefacts and improved image
quality, leading to detection of lesions that were previously masked by artefacts. The
application of iMAR did not affect image quality in regions distant from the metal implants.
Conclusions: The application of iMAR to CT examinations of patients with maxillofacial
metal implants leads to artefact reduction, improvement of image quality and increased
diagnostic utility. Routine implementation of iMAR during imaging of patients with metal
hardware implants could add diagnostic value to their CT examinations.
Dentomaxillofacial Radiology (2017) 46, 20160355. doi: 10.1259/dmfr.20160355
Cite this article as: Hakim A, Slotboom J, Lieger O, Schlittler F, Giger R, Michel C, et al.
Clinical evaluation of the iterative metal artefact reduction algorithm for post-operative CT
examination after maxillofacial surgery. Dentomaxillofac Radiol 2017; 46: 20160355.
Keywords: artefacts; CT; post-operative; metal; osteosynthesis; dental
Introduction
Metal hardware, such as prostheses, wires, pins, re-
construction plates and screws, are routinely used in
maxillofacial surgery and patients who undergo such
surgery commonly require post-operative imaging for
follow-up or may undergo imaging for the investigation
of other unrelated conditions. However, the presence of
metal during a CT scan causes beam hardening,
streaking, non-linear partial volume effects, noise,
scatter and aliasing, which are collectively known as
metal artefacts.
1,2
These artefacts significantly degrade
image quality, restrict the evaluation of operative re-
construction and may mask post-operative pathologies,
such as haemorrhage, infections or other incidental
pathologies in nearby or distant regions (for example,
tumours). Failure to note such findings may lead
to serious complications and may impair patient
management.
Correspondence to: Mr Arsany Hakim. E-mail: arsany_hakim@yahoo.com
Received 31 August 2016; revised 15 October 2016; accepted 11 January 2017
Dentomaxillofacial Radiology (2017) 46, 20160355
ª2017 The Authors. Published by the British Institute of Radiology
birpublications.org/dmfr
Some metal artefacts can be reduced by changing the
scan parameters (such as milliampere second, kilo-
voltage or slice thickness) or by using a dedicated kernel
and adding filters; however, the results of these mod-
ifications are not satisfactory.
1
Other new methods,
such as dual-source scanners, require specialized
equipment and reduce only some types of artefacts.
1,3
For these reasons, CT vendors have been trying to
implement new algorithms to reduce these artefacts.
The modern algorithms for metal artefact reduction
may differ between manufacturers but utilize the same
principle, which is iterative reconstruction. In addition,
many of these algorithms use “sinogram in-painting”,
which considers CT values affected by metal to be
completely useless and ignores them. This deletion leads
to loss of data in regions near the metal, or generates
new artefacts. Owing to these drawbacks, other in-
terpolation techniques are used to replace the corrupted
data.
4,5
The iterative metal artefact reduction (iMAR)
algorithm was recently introduced by Siemens Health-
care and is based on two previously developed algo-
rithms, namely frequency split metal artefact reduction
(FSMAR) and normalized metal artefact reduction
(NMAR).
6–8
NMAR minimizes new artefacts generated
in the corrected image during interpolation by removing
the structures with high contrast from the sinogram
prior to the interpolation process and reinserting them
afterwards.
4,6
FSMAR preserves both the natural image
impression and the valid edge information of the un-
corrected image, which has the drawback of reinserting
high-frequency streak artefacts into the corrected image.
These artefacts are reduced following each iteration so
that the final image quality depends on the number of
iterations. Other factors affecting image quality include
the threshold used for metal segmentation and prior
image calculation, as well as the filter parameters of the
frequency split operation. These parameters are user
selectable and vendor adjusted according to the metal
implant type (for example: neuro, dental, prosthesis or
pacemaker).
6,9
The process of iMAR has been discussed
in detail by Axente et al
6
and Wuest et al.
9
Previous studies on iMAR and its prototypes
revealed its utility in reducing streak artefacts.
10–13
However, there are concerns that these newly in-
troduced algorithms may not provide accurate attenu-
ation values near metallic hardware and that this may
impair diagnostic values by smoothing the image in
attempts to reduce noise. This study was designed to
address these concerns and to evaluate the performance
of iMAR in patients with maxillofacial implants after
reconstructive surgery or metallic implants, such as
fixed overdentures.
Methods and materials
This retrospective study was conducted according to the
guidelines of the Cantonal Ethics Committee and was
performed in accordance with the guidelines of the
Declaration of Helsinki. All CT examinations were
clinically indicated, and no CT scan was performed for
the purpose of this study alone.
Study population
A total of 17 (12 male and 5 female) patients who un-
derwent CT for diverse indications at the Institute of
Diagnostic and Interventional Neuroradiology, Insel-
spital, Bern University Hospital and University of Bern,
Bern, Switzerland, were retrospectively analyzed. The
inclusion criterion was the presence of metal hardware
in the maxillofacial region. The age of the patients
ranged from 19 to 89 years (mean 63 years).
Metal hardware
The 17 patients had diverse types of metal hardware. In
14 (82.4%) patients, multiple metal implants in different
locations were present. Among our patient sample,
approximately 32 metallic hardware implants (ranging
from 2 cm 30.5 cm to 9 cm 31 cm in diameter) were
examined, in addition to amalgam tooth fillings and
dental superstructures.
Among the 17 patients, 13 (76.5%) patients had metal
hardware in the maxilla and 11 (64.7%) patients had
metal hardware in the mandible. 2 (11.8%) patients
presented with maxillary internal fixation (plate and
screws) and 8 (47.1%) patients presented with mandib-
ular internal fixation. Most of the patients (70.58%) had
multiple amalgam tooth fillings and/or dental pins.
1 (5.9%) patient also presented with bilateral orbital
mesh implants. Figure 1 illustrates examples of the types
of metal hardware analyzed in our study. Our patients
had diverse types of metal ranging from silver amalgam,
gold–platinum or silver–palladium to titanium.
Clinical indication
For 5 (29.4%) of the 17 patients, the indication for CT
was a suspected infection in the oral cavity or mandible.
4 (80%) of these 5 patients presented to our emergency
department with pain, soft-tissue swelling and limited
mouth opening. The fifth patient presented with clinical
signs of an infection after receiving a tongue piercing. 4
(23.5%) of the 17 patients in the study were under
bisphosphonate therapy. These patients complained of
pain when moving the mouth and when speaking, and
CT scanning was performed to exclude osteonecrosis
and pathological fracture of the mandible. 3 (17.6%) of
the 17 patients were referred for a routine post-operative
CT scan of the facial bones following severe trauma and
complex maxillofacial reconstruction. For 2 (11.8%)
patients, a muscle free flap transfer after tumour exci-
sion (of the head and neck) was planned, and a CT scan
of the extracranial vessels was performed for pre-
operative delineation of the vascular supply. For 2
(11.8%) patients, stroke work-up (CT brain scan and
CT angiography) was performed in response to neuro-
logical symptoms. The last patient (5.9%) complained
of recurrent nasal polyps and was referred for a CT scan
for pre-operative planning.
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CT image acquisition
All examinations were performed in the supine position
using a 128-slice CT scanner (SOMATOM
®
Definition
Edge; Siemens Healthcare, Erlangen, Germany). 6
(35%) patients underwent non-enhanced CT; 9 (53%)
patients underwent contrast-enhanced CT of the facial
bones; and 2 (12%) patients underwent non-enhanced
CT/contrast-enhanced CT of the brain and CT angi-
ography of the intracranial and extracranial vessels.
Image reconstruction according to our protocol
includes a soft-tissue window (kernel J45s) and a bone
window (kernel J70h). Both images are routinely
reconstructed using the standard integrated sinogram-
affirmed iterative reconstruction (SAFIRE) algorithm.
Iterative metal artefact reduction (artefact reduction
algorithm)
For the purpose of our study, the soft-tissue window
was reconstructed using the iMAR algorithm. iMAR
was developed by Siemens Healthcare based on the
previous algorithms FSMAR and NMAR. The iMAR
process has been discussed in detail by Axente et al
6
and
Wuest et al.
9
iMAR is an add-on tool to the scanning
software that is implemented similarly to the standard
SAFIRE algorithm without any need for operator
training. Additional reconstruction with iMAR takes
from a few seconds to a few minutes (according to the
number of images to be reconstructed) and does not
delay other reconstructions of the standard images
being transferred to the picture archiving and commu-
nication system. Post-processing with iMAR can be
performed even after the examination is finished as long
as the source images remain in the scanning computer.
The images before post-processing are referred to as
the uncorrected data set. The iMAR images after post-
processing are referred to as the corrected data set.
Image evaluation and measurements
A certified reporting workstation (Sectra IDS7, Link-
¨
oping, Sweden) with a certified monitor (DIN V 6868-
57 and QA guideline) was used for the analysis. The
evaluation of both data sets, i.e. uncorrected and cor-
rected with iMAR, was performed independently by
two experienced neuroradiologists (AH and FW).
Window settings could be adjusted by the examiners.
The axial soft-tissue reconstruction kernel was utilized
with the soft tissue and bone windows. To avoid a par-
tial volume effect, all analyses were performed on thin
slices (0.6–1 mm).
Qualitative image analysis
Three criteria (artefacts, image quality and diagnostic
utility) were defined. The first two criteria were assessed
on a four-point scale in six defined regions of the image
(three on each side, R1 to R3 and L1 to L3 at the level
of the oropharynx) (Figure 2). The six defined regions
were symmetrically separated with a vertical line from
the symphysis menti to the centre of the tip of the dens
Figure 1 An example of metal hardware: CT topograms from eight patients with diverse implants ranging from plates with screws, dental fillings
and dental pins to eye mesh.
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axis and the middle of the posterior cervical arch. A
horizontal line connected both posterior maxillary
ridges, and a second line was placed immediately ven-
tral to the atlas.
These lines separated three regions on each side into
the anterior, middle and posterior regions. The anterior
regions (R1 and L1) included the oral cavity and sub-
mandibular spaces. The middle regions (R2 and L2)
included the parapharyngeal spaces, masticator spaces
and pharyngeal mucosal spaces. The posterior regions
(R3 and L3) included the perivertebral spaces and
posterior cervical spaces.
Artefacts
The severity of artefacts was evaluated using a four-
point scale (Table 1). The data set corrected with iMAR
was also checked for the likelihood of newly developed
artefacts.
Image quality
Image quality was defined as how precisely the CT
images represented a maxillofacial pathology and was
based on contrast, resolution and brightness perceived
in the images while considering detection, verification
and staging of a possible pathology. We also used
a four-point rating scale for evaluation (Table 1).
Diagnostic utility
The capability of detecting hidden lesions or potential
findings that were masked by the artefacts and identi-
fied or better detected after reconstruction with iMAR
was considered as well as the diagnostic confidence in
image interpretation.
Quantitative CT image analysis
To assess the effect of iMAR on the images of the areas
without metal, Hounsfield units (HU) of two defined
structures located above the metal region were mea-
sured and compared in the corrected and uncorrected
data set. These structures were the right and left vitreous
bodies and the right and left cerebellar hemispheres.
Statistics
For a pairwise comparison of corrected and uncorrected
data set, the Wilcoxon signed-rank test was performed
in SPSS
®
v. 23.0 (IBM Corp., New York, NY; formerly
SPSS Inc., Chicago, IL) to determine statistical signifi-
cance. A Wilcoxon test was applied to the rating criteria
of the artefacts and image quality in the six defined
regions. A p-value ,0.05 was considered to be
significant.
Results
Qualitative CT image analysis
Artefacts: The uncorrected data set exhibited extensive
streaking artefacts that affected the assessment of the
anatomical structures in regions R1, R2, L1 and L2
with limited pathology evaluation.
In the corrected data set, we noted a considerable
reduction in the amount of artefacts (Figure 3). In the
anterior regions, i.e. R1 and L1, the artefacts in the
corrected data set were reduced by 2 points (from very
severe to moderate artefacts) 20 times in the 34 regions
of R1 and R2 (58.8%). The artefacts were reduced by
1 point 12 times (35.3%) (6 times from very severe to
severe artefacts and 6 times from severe artefacts to
moderate artefacts). In only two instances (5.9%), the
artefacts did not significantly improve (once on the right
and once on the left side).
In the middle regions (R2 and L2), the artefacts were
reduced by 3 points (from very severe to none or very
mild artefacts) twice (5.9%), by 2 points (7 times from
very severe to moderate artefacts and 14 times from
severe artefacts to none or very mild artefacts) 21 times
(61.8%) and by 1 point (9 times from moderate to none
Figure 2 The six defined regions for artefact and image quality
analysis.
Table 1 Scores for artefact and image quality analysis
Score Artefact Image quality
0 Very severe: very strong artefacts where
anatomy is not visualized
Very poor
1 Severe: strong artefacts and limited
evaluation of anatomical structures
Poor
2 Moderate: artefacts present but assessment
of anatomical structures is not impaired
Fair
3 None or very mild Good/excellent
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or very mild and twice from severe to moderate) 11
times (32.5%).
In the posterior regions (R3 and L3), the artefacts in
the corrected data set were reduced by 2 points (from
severe to none or very mild) 6 times (17.6%) and by 1
point (from moderate to none or very mild) 19 times
(55.9%). The artefact score remained the same (none or
very mild) seven times (20.6%).
With iMAR reconstruction, the artefact reduction in
the six regions was statistically significant with a p-value
of 0.000 for regions R1, L1, R2 and L2 and 0.001 for
regions R3 and L3. Two examples of images from our
study are shown in Figures 4 and 5.
For 4 (23.5%) of the 17 patients, blurring around
the metal hardware in bone window was detected in the
corrected images. This blurring was not observed in the
uncorrected data set (Figure 7).
Image quality: The uncorrected data set was of poor
quality, especially in the ventral and middle regions.
Figure 3 Artefact scores before (uncorrected) and after (corrected) application of iterative metal artefact reduction (iMAR) displayed on a 100%
stacked column chart revealing score improvements due to artefact reduction using iMAR. The value 0 denotes a very poor condition; the value 3
denotes a good to excellent condition.
Figure 4 Images from the CT examination of a 72-year-old male after neck dissection and resection of a squamous cell carcinoma of the maxilla.
CT slice at the level of the oropharynx before (a) and after (b) reconstruction with iterative metal artefact reduction (iMAR). The image before
iMAR was severely distorted with artefacts owing to metal hardware in the maxilla, which affected the visualization of the anatomy, especially in
the regions next to the metal implants. After the application of iMAR, a considerable reduction of artefacts and improvement in image quality was
observed with visualization of the anatomy, especially next to the metal hardware. It can be noted that there is no change of quality in the regions
less affected by artefacts, for example paraspinal regions.
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In the corrected data set, image quality was improved
(Figure 6). Image quality in the anterior regions (R1
and L1) was improved by 2 points (from very poor to
fair, 7 times on the right side and 8 times on the left side)
15 times (44.1%) and by 1 point (12 times from very
poor to poor, 6 times from poor to fair and once from
fair to good/excellent) 19 times (55.9%).
In the middle regions (R2 and L2), image quality
improved by 3 points (from very poor to good/excellent)
twice (5.9%), by 2 points (7 times, very poor to fair and
14 times from poor to good/excellent) 21 times (61.8%)
and by 1 point (8 times from fair to good/excellent
and once from poor to fair) 9 times (26.5%). In two
instances (5.9%), the image quality did not change
(good/excellent).
In the posterior regions (R3 and L3), the image
quality improved by 2 points (from poor to good/
excellent) 6 times (26.5%) and by 1 point (from fair to
good/excellent) 18 times (52.9%). In 10 instances
(29.4%), the quality remained the same (good/excellent).
Two examples with improvement of image quality from
our study are shown in Figures 4 and 5.
With iMAR reconstruction, the improvement in
image quality in the six regions was statistically signif-
icant, with a p-value of 0.000 for regions R1, L1, R2
and L2 and 0.001 for regions R3 and L3.
Figure 5 CT examination of an 81-year-old patient with chronic bisphosphonate-induced osteonecrosis. CT slice at the level of the oropharynx
before (a) and after (b) post-processing with iterative metal artefact reduction (iMAR). Several artefacts were present near the implanted metal
hardware in the maxilla before post-processing and were considerably reduced after the application of iMAR, which improved the quality of
the image.
Figure 6 Image quality scores before (uncorrected) and after (corrected) application of iterative metal artefact reduction (iMAR) displayed on
a 100% stacked column chart revealing quality improvement of images after post-processing with iMAR.
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Diagnostic utility: In 10 and 11 of the 17 patients (58.8%
and 64.7% patients), new findings in the corrected data
set were noted in the soft tissue and in the bone window,
respectively. These findings could not be evaluated
during the standard reconstruction without iMAR be-
cause they were masked by artefacts. Findings included
(a) focal hyperostosis around the implanted osteosyn-
thesis screws (two patients); (b) dental decay of the
mandibular central incisors due to piercings in the oral
cavity (one patient); (c) lamellar periosteal reaction at
the alveolar ridge (one patient); (d) reactive hyperostosis
in the lingual cortex of the mandibular ramus (one pa-
tient); and (e) better visualization and evaluation of the
screws of the osteosynthesis of the pins of implanted
tooth and bone cement (six patients).
Regarding the soft-tissue window, the following
findings were noted after post-processing with iMAR:
(a) hypertrophy of the pharyngeal and palatine tonsils
with tinny calcifications in two patients; (b) asymmetry
of the muscles of mastication (buccinators/masseter) in
two patients; (c) reactive swelling of the Waldeyer’s
ring and lymph nodes in the parapharyngeal space in
the context of throat infection (three patients); (d)
a simple cyst in the palatine tonsil (one patient); and
(e) fatty atrophy of the tongue (one patient) and better
delineation of a subcutaneous haematoma after
trauma of the right maxillary sinus (one patient). In
image evaluation, both examiners reported greater
confidence in the iMAR-corrected images than in the
original images.
Quantitative CT image analysis
HU measurements for neither the vitreous body nor the
cerebellum revealed any significant differences between
the uncorrected and corrected data sets (Table 2).
Discussion
In this study, we evaluated the recently introduced ar-
tefact reduction algorithm “iMAR”. iMAR was utilized
during the routine workflow for patients with metal
hardware in the maxillofacial region who had post-
operative CT examinations. These patients were un-
dergoing complex reconstructive maxillofacial surgery
and presented with metal implants in the oral cavity.
The patients underwent imaging for various clinical
reasons such as trauma, infection, stroke or post-
operative follow-up.
iMAR is an add-on tool that can be applied to images
after scanning the patient. iMAR does not change the
CT scan parameters, and no additional scans are re-
quired. The radiation dose also remains the same. From
the operator perspective, utilizing iMAR is similar to
the standard SAFIRE, which is very simple and does
not require additional training.
The iMAR algorithm significantly reduced artefacts
and improved image quality. The extent of artefact re-
duction is related to the volume and type of metal, as
well as the distance of the tissue from the metal
implants. The effect of iMAR was particularly notice-
able in the regions most affected by artefacts, such those
that abut or are near to maxillofacial implants, namely
the oral cavity, submandibular space, pharyngeal mu-
cosal space, masticator space, parotid space and retro-
pharyngeal space. Complete removal of artefacts in
regions adjacent to metal turned out to be not possible
in every patient.
Artefacts in the perivertebral and posterior cervical
spaces were also reduced, and image quality increased.
In addition, the diagnostic value of the CT scans after
iMAR increased owing to the ability of detecting
Figure 7 CT examination of a 58-year-old patient who was treated by osteosynthesis of the mandible following pathological fracture due to
osteonecrosis: the corrected images with iterative metal artefact reduction (a) are showing an osteolysis-like area around the screws (arrow). Such
artefacts were found in a small number of patients. Misinterpretation was avoided by comparing the corrected data set with the uncorrected data
set (b).
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findings that were masked by artefacts. There was no
change in the quality in regions distant from metal
implants.
Previous studies evaluated the usefulness of iMAR
and its prototypes in reducing metal artefacts. These
reports focused on hip arthroplasties,
5,10,14–18
shoulder
arthroplasties,
13
internal fixation hardware,
12
dental
hardware
14,19
and spinal hardware.
20,21
Our findings are
consistent with those of previous studies. Furthermore,
our findings demonstrate the utility of using iMAR in
the post-operative phase and show that additional
findings can be discovered after artefact reduction. We
also found that new artefacts can appear after applying
iMAR, as observed in a few of our patients. In these
patients, we observed blurring around the metal hard-
ware in the bone window, which had the potential to be
misinterpreted as osteolysis. This blurring probably
resulted from data loss near the metal edge, which could
not be recovered during the interpolation process. This
type of artefact was noted with one of the prototypes of
iMAR
8
but is substantially reduced in the current ver-
sion of iMAR as a result of the multiple iterations be-
fore producing the final image.
13
The presence of these
artefacts is the reason why some institutes do not utilize
iMAR, or in-painting algorithms, in their clinical rou-
tine.
22
In our patients, such misinterpretation could be
avoided by comparing the corrected and uncorrected
data sets. Thus, detecting osteolysis-like lesions with
iMAR requires careful comparison with the source
images. The added diagnostic value of iMAR, as de-
scribed previously, far outweighs this disadvantage and
should not be ignored. Further research focusing on the
diagnostic accuracy of iMAR for osseous lesions is
required.
In our experience, iMAR is valuable for the detection
of soft-tissue pathologies located next to the metal
hardware but is masked by metal artefacts in the stan-
dard reconstruction. In our institution, iMAR is rou-
tinely used not only for patients with metal implants in
the maxillofacial region but also for those with ven-
triculoperitoneal shunts, clipping and coiling of in-
tracerebral aneurysms, reimplantation of skull bones
after osteoclastic craniotomy or after spinal fusion
surgery. In all of these cases, the iMAR-corrected and
iMAR-uncorrected data sets are compared to ensure
that no information is lost.
Limitations
This was a retrospective study with a small cohort of
patients and a heterogeneous sample. The study was
performed using iMAR (Siemens Healthcare). Thus,
the results do not necessarily apply to similar algo-
rithms from other vendors. Our analysis focused
on the level of the oropharynx, which in most cases
presents with the maximum degree of artefacts.
Obtaining accurate HU for a quantitative analysis
at the level of the artefacts was challenging because
there were no constant values to compare the HU after
post-processing with iMAR (the HU values in the
original uncorrected data are already corrupted).
Thus, a quantitative analysis was performed only
outside the artefact regions.
Assessing the effect of different scanning parameters
with iMAR on image quality was beyond the scope of
this study but could represent an important consider-
ation in developing a method to reduce the radiation
dose while maintaining image quality.
Conclusions
iMAR is a powerful tool for reducing artefacts resulting
from maxillofacial metal hardware. iMAR significantly
improves image quality and enhances the evaluation of
adjacent soft-tissue structures. iMAR also increases the
observer confidence in detecting pathologies.
Acknowledgments
We thank the CT team and their leader Mrs Nadja
Feusi for their contribution to this study by post-
processing the images with iMAR.
Table 2 Comparison of Hounsfield units measured in areas, which
were not affected by artefacts, before (uncorrected) and after
(corrected) iterative metal artefact reduction application
Number Data set
Right
vitreous
body
Left
vitreous
body
Right
cerebellum
Left
cerebellum
P1 Uncorrected 11 12 51 45
Corrected 11 12 51 45
P2 Uncorrected 30 37 35 44
Corrected 30 37 34 44
P3 Uncorrected 30 26 36 35
Corrected 30 26 35 35
P4 Uncorrected 34 29 40 44
Corrected 35 29 40 44
P5 Uncorrected 25 41 42 47
Corrected 25 40 41 47
P6 Uncorrected 23 34 39 42
Corrected 23 35 39 41
P7 Uncorrected 36 40 42 41
Corrected 36 40 42 41
P8 Uncorrected 36 34 47 55
Corrected 36 34 46 54
P9 Uncorrected 40 36 27 33
Corrected 40 36 27 33
P10 Uncorrected 22 20 55 54
Corrected 21 20 55 53
P11 Uncorrected 30 34 34 36
Corrected 31 35 34 36
P12 Uncorrected 35 28 36 37
Corrected 35 28 36 35
P13 Uncorrected 15 12 59 55
Corrected 15 12 57 54
P14 Uncorrected 27 27 45 40
Corrected 27 25 44 39
P15 Uncorrected 40 43 43 39
Corrected 40 43 43 37
P16 Uncorrected 41 40 34 32
Corrected 42 39 31 32
P17 Uncorrected 33 30 31 40
Corrected 32 29 28 36
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