- Open Access
Implementation of iterative metal artifact reduction in the pre-planning-procedure of three-dimensional physical modeling
© The Author(s) 2017
Received: 5 January 2017
Accepted: 14 March 2017
Published: 31 March 2017
To assess the impact of metal artifact reduction techniques in 3D printing by evaluating image quality and segmentation time in both phantom and patient studies with dental restorations and/or other metal implants. An acrylic denture apparatus (Kilgore Typodent, Kilgore International, Coldwater, MI) was set in a 20 cm water phantom and scanned on a single-source CT scanner with gantry tilting capacity (SOMATOM Edge, Siemens Healthcare, Forchheim, Germany) under 5 scenerios: (1) Baseline acquisition at 120 kV with no gantry tilt, no jaw spacer, (2) acquisition at 140 kV, (3) acquisition with a gantry tilt at 15°, (4) acquisition with a non-radiopaque jaw spacer and (5) acquisition with a jaw spacer and a gantry tilt at 15°. All acquisitions were reconstructed both with and without a dedicated iterative metal artifact reduction algorithm (MAR). Patients referred for a head-and-neck exam were included into the study. Acquisitions were performed on the same scanner with 120 kV and the images were reconstructed with and without iterative MAR. Segmentation was performed on a dedicated workstation (Materialise Interactive Medical Image Control Systems; Materialise NV, Leuven, Belgium) to quantify volume of metal artifact and segmentation time.
In the phantom study, the use of gantry tilt, jaw spacer and increased tube voltage showed no benefit in time or artifact volume reduction. However the jaw spacer allowed easier separation of the upper and lower jaw and a better display of the teeth. The use of dedicated iterative MAR significantly reduced the metal artifact volume and processing time. Same observations were made for the four patients included into the study.
The use of dedicated iterative MAR and jaw spacer substantially reduced metal artifacts in the head-and-neck CT acquisitions, hence allowing a faster 3D segmentation workflow.
The introduction of three-dimensional physical modeling is evolving as an important tool in the medical field, especially in pre-operative planning of complex surgery procedures, medical training or patient education [1–9]. However printing of the final medical three dimensional model is preceded by a number of laborious steps involving computer assisted segmentation of the structures of interest, as described elsewhere in great details . Metal artifacts commonly seen in computed tomography (CT) images, induced by dental hardware or orthopedic prosthesis, hamper the diagnostic evaluation of radiological images, especially affecting the palatine and root of the tongue in cranio-maxillo-facial and head-and-neck imaging [11–13]. Besides the effect on intracorporal anatomical structures, those artifacts also generate extracorporeal artifact-structures, disturbing the fabrication of the 3D-model, adding additional time-consuming segmentation steps to eliminate those structures. In order to overcome these artifacts, acquisitions and post-processing techniques, such as higher x-ray tube voltage, gantry / head tilting, dual-energy or dedicated metal artifact reduction algorithms have been proposed and implemented in diagnostic CT exams [12, 14–17]. However, the impact of these techniques on segmentation of data for 3D printing has not been thoroughly investigated.
The purpose of this study is to assess the impact of metal artifact reduction techniques in 3D printing by evaluating image quality and segmentation time in both phantom and patient studies with dental restorations and/or other metal implants.
Scanner description and data acquisition
Baseline imaging parameters used for the phantom and patient studies
128 x 0.6
128 x 0.6
Tube potential [kVp]
Rotation time [s]
Quality reference mAS
Reconstruction slice thickness [mm] / increment [mm]
0.6 / 0.6
J40 (iterative reconstruction with a strength of 3)
J40 (iterative reconstruction with a strength of 3)
Baseline acquisition with imaging parameters as standard clinical protocols (Table 1)
Increased tube voltage from 120 to 140 kV while maintaining the remaining baseline acquisition parameters the same.
Tilting the gantry by 15° while maintaining the remaining baseline acquisition parameters the same.
Insertion of a jaw spacer (Bite Block, Heraeus Kulzer, South Bend, IN), made out of a non-radiopaque material, between the jaw bones (Fig. 1e-f) and acquisition with baseline parameters.
Inserting a jaw spacer and acquiring in tilted gantry position of 15° with baseline parameters.
All images were reconstructed using an iterative reconstruction algorithm (ADMIRE, Siemens Healthcare, Forchheim, Germany) and a medium sharp kernel (J40). In addition, all images were reconstructed using a dedicated iterative metal artifact reduction algorithm (MAR) (iMAR®, Siemens Healthcare, Forchheim, Germany).
The retrospective patient study was approved by the local institutional review board and was compliant with the Health Insurance Portability and Accountability Act (HIPAA). Informed consent was waivered. Four patients referred for a head and neck acquisition with subsequent 3D-modelling were identified in the institutional database and included into the study. Scan parameters applied are described in Table 1. All images were reconstructed with and without dedicated MAR algorithm.
Qualitative and quantitative evaluation
Qualitative visual evaluation of the obtained images regarding metal artifacts was performed on a dedicated workstation (snygo.via VB10, Siemens Healthcare, Forchheim, Germany) by a radiologist with >4 years of radiological experience (R.P.M.), focusing on the amount of metal artifacts and how they affect surrounding anatomies.
Quantitative evaluation of the metal artifacts for all above mentioned scenarios was performed using dedicated medical 3D-printing software (Materialise Interactive Medical Image Control Systems; Materialise NV, Leuven, Belgium) by one radiologist with >4 years (R.P.M) of radiological experience. Segmentation of the metal artifact and skull was performed using the split mask tool (Materialise Interactive Medical Image Control Systems; Materialise NV, Leuven, Belgium). The segmented model, including the segmented artifact, was then loaded into 3-matic software (Materialise Interactive Medical Image Control Systems; Materialise NV, Leuven, Belgium), where the volume of the isolated rendered metal artifact was isolated and calculated.
Tube voltage and iterative based metal artifact reduction technique – scenarios 1 & 2
Gantry tilt – scenario 3
Jaw spacer – scenario 4
Combinations of gantry tilt and jaw spacer – scenario 5
Artifact Volume and segmentation time for 4 patients referred for head-and-neck acquisition and reconstructed with and without iterative metal artifact reconstruction. Only one patient was acquired with a jaw spacer, whereas none of the acquisitions were performed with a tilted gantry
Artifact Volume [ml]
Segmentation time [s]
In this study we demonstrated the use of various metal artifact reduction techniques in order to enhance the anatomical 3D segmentation hampered by heavy metal artifacts. In addition we showed that using dedicated iterative metal artifact reduction algorithm is the most promising technique to reduce the metal artifact volume and hence reducing segmentation time. Those findings are important, since a majority of head-and-neck CT acquisitions are associated with metal artifacts due to dental restorations. This results in laborious 3D segmentation of the craniomaxillofacial bones and separation of the mandible from the maxilla which is needed when 3D printing clinical cases. Previous studies evaluating metal artifact reduction techniques mainly focused on the diagnostic performance and our observations made in the phantom and patient studies agreed with the findings in the present literature [15, 17–19].
Bannas et al. showed an incremental value of gantry tilt on the reader’s sensitivity of detecting oral tumors as gantry tilt redistributed the metal artifacts to other locations outside of the slices where the tumor was . Our phantom data support the visual results, however quantitatively the segmented artifact volume was not different between both gantry states. Artifact segmentation time of the data in non-tilted position was shorter as the artifact did not have to be removed from as many slices. This is mainly due to the concentration of artifact in a smaller and more condensed volume, resulting in a faster and simplified containment. In clinical practice, if the sole purpose of the CT exams is for 3D modeling, patients should be scanned without gantry tilt, as demonstrated by our phantom data. However, if the scan is for both 3D printing and other diagnostic tasks (e.g. tumor detection) where gantry tilt benefits the diagnosis, the patients should be scanned with gantry tilt. In this case, the benefit of accurate tumor detection outweighs the drawback of increased processing time for 3D modeling. Dedicated MAR technique should be used, which has been shown to have a positive impact for the clinical evaluation and 3D-segmentation.
The use of a jaw spacer has been recommended for maxillofacial CT-acquisitions in order to separate both jaws . Our data support the recommendations without having any effects on the segmented artifact volumes. Clinically many of our 3D printed craniomaxillofacial cases are for tumor resection or congenital facial reconstruction and the surgeon often requests the mandible be printed separately from the maxilla for surgical planning. This requires two separate volumes to be created which is hampered by overlapping artifact which is greatly reduced with the jaw spacer. A separate benefit of the spacer was more realistic appearance to the teeth after segmentation. The most time and labor effective artifact reduction method in both phantom and patient model is the dedicated iterative metal artifact reduction . Our study hence adds to the current literature stating that the use of this technique enhances the 3D-segmentation workflow. As demonstrated in a previous study, the MAR algorithm can occasionally deteriorate the bone contours if the parameter settings were not optimal . Therefore, caution should be taken using MAR algorithm and bone contour should be carefully checked based on professional expertise during the segmentation process.
Our study has a number of limitations. First, our patient sample size is very small, since the number of patients referred for pre-operative 3D-printing was limited. Secondly, the phantom used was made of acrylic, which has lower attenuation than bone and consistent CT number unlike the bone which is more variable. In this study, we lowered the CT number threshold in the segmentation process to accommodate this difference between the material properties. This procedure, however, was not necessary in the human subject studies. As seen in our patient study, substantial improvement of image quality was achieved with real bones. Another limitation is that the model after thresholding and manually alteration was used as the reference model when artifact volume was calculated. Since this process was done separately for images reconstructed with and without MAR, it is possible there are slight differences in the artifact volume based on manual artifact removal. Building a common reference model with other techniques, such as 3D scanning, may provide a more accurate estimation of artifact volume. However, we expect the difference would be small as the radiologist tried to maintain the same anatomy after artifact removal and carefully reviewed the final data before calculating artifact volume.
In conclusion, the use of metal artifact reduction techniques, especially iterative metal artifact reduction algorithm, shortens the time of segmentation for 3D printing and provides a more accurate mandible and maxilla in areas affected by metal artifacts.
The authors would like to thank Thomas Vrieze for assistance in phantom data acquisition, and Kris Nunez for assistance in manuscript preparation.
No funding was used in completing this study.
Availability of data and materials
Data is available by contacting the corresponding author.
RM: Study design, Data Acquisition, Data Analysis, Drafting of manuscript, Critical revision of the manuscript. JM: Study design, Data Acquisition, Data Analysis, Critical revision of the manuscript. JM: Study design, Critical revision of the manuscript. AA: Study design, Data Acquisition, Critical revision of the manuscript. AH: Study design, Critical revision of the manuscript. JK: Study design, Data Acquisition, Critical revision of the manuscript. JF: Study design, Critical revision of the manuscript. CM: Study design, Critical revision of the manuscript. SL: Study design, Data Acquisition, Data Analysis, Drafting of manuscript, Critical revision of the manuscript. All authors read and approved the final manuscript.
Dr. Halaweish is an employee of Siemens Healthineers. Dr. McCollough receives industry grant support from Siemens Healthineers, unrelated to this work.
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- Trace AP, et al. Radiology’s Emerging Role in 3-D Printing Applications in Health Care. J Am Coll Radiol. 2016;13(7):856–62. e4.View ArticlePubMedGoogle Scholar
- Mitsouras D, et al. Medical 3D Printing for the Radiologist. Radiographics. 2015;35(7):1965–88.View ArticlePubMedPubMed CentralGoogle Scholar
- Lim KH, et al. Use of 3D printed models in medical education: A randomized control trial comparing 3D prints versus cadaveric materials for learning external cardiac anatomy. Anat Sci Educ. 2016;9(3):213–21.View ArticlePubMedGoogle Scholar
- Adams JW. 3D Printing in Anatomical and Palaeontological Teaching and Research. FASEB J. 2016;30(1 Supplement):92.2.Google Scholar
- Michalski MH, Ross JS. The shape of things to come: 3D printing in medicine. JAMA. 2014;312(21):2213–4.View ArticlePubMedGoogle Scholar
- Marro A, Bandukwala T, Mak W. Three-Dimensional Printing and Medical Imaging: A Review of the Methods and Applications. Curr Probl Diagn Radiol. 2016;45(1):2–9.View ArticlePubMedGoogle Scholar
- Morrison RJ, et al. Mitigation of tracheobronchomalacia with 3D-printed personalized medical devices in pediatric patients. Sci Transl Med. 2015;7(285):285ra64.View ArticlePubMedPubMed CentralGoogle Scholar
- Zopf DA, et al. Bioresorbable airway splint created with a three-dimensional printer. N Engl J Med. 2013;368(21):2043–5.View ArticlePubMedGoogle Scholar
- Leng S, et al. Construction of realistic phantoms from patient images and a commercial three-dimensional printer. J Med Imaging. 2016;3(3):033501.View ArticleGoogle Scholar
- Matsumoto JS, et al. Three-dimensional Physical Modeling: Applications and Experience at Mayo Clinic. Radiographics. 2015;35(7):1989–2006.View ArticlePubMedGoogle Scholar
- Lee MJ, et al. Overcoming artifacts from metallic orthopedic implants at high-field-strength MR imaging and multi-detector CT. Radiographics. 2007;27(3):791–803.View ArticlePubMedGoogle Scholar
- Weiss J., et al., Impact of iterative metal artifact reduction on diagnostic image quality in patients with dental hardware. Acta Radiol, 2016. In Press.Google Scholar
- Gong XY, et al. Clinical evaluation of the normalized metal artefact reduction algorithm caused by dental fillings in CT. Dentomaxillofac Radiol. 2013;42(4):20120105.View ArticlePubMedPubMed CentralGoogle Scholar
- Higashigaito K, et al. Metal Artifact Reduction in Pelvic Computed Tomography With Hip Prostheses: Comparison of Virtual Monoenergetic Extrapolations From Dual-Energy Computed Tomography and an Iterative Metal Artifact Reduction Algorithm in a Phantom Study. Invest Radiol. 2015;50(12):828–34.View ArticlePubMedGoogle Scholar
- Bannas P, et al. Diagnostic accuracy of state-of-the-art MDCT scanners without gantry tilt in patients with oral and oropharyngeal cancer. Eur J Radiol. 2012;81(12):3947–52.View ArticlePubMedGoogle Scholar
- Kotsenas AL, et al. CT Metal Artifact Reduction in the Spine: Can an Iterative Reconstruction Technique Improve Visualization? AJNR Am J Neuroradiol. 2015;36(11):2184–90.View ArticlePubMedGoogle Scholar
- Bamberg F, et al. Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol. 2011;21(7):1424–9.View ArticlePubMedGoogle Scholar
- Mahnken AH, et al. A new algorithm for metal artifact reduction in computed tomography: in vitro and in vivo evaluation after total hip replacement. Investig Radiol. 2003;38(12):769–75.View ArticleGoogle Scholar
- Meinel FG, et al. Metal artifact reduction by dual-energy computed tomography using energetic extrapolation: a systematically optimized protocol. Invest Radiol. 2012;47(7):406–14.View ArticlePubMedGoogle Scholar
- Hughes CW, et al. The custom-made titanium orbital floor prosthesis in reconstruction for orbital floor fractures. Br J Oral Maxillofac Surg. 2003;41(1):50–3.View ArticlePubMedGoogle Scholar
- Subhas N, et al. Iterative metal artifact reduction: evaluation and optimization of technique. Skelet Radiol. 2014;43(12):1729–35.View ArticleGoogle Scholar