Skeletal Radiology

, Volume 43, Issue 12, pp 1729–1735 | Cite as

Iterative metal artifact reduction: Evaluation and optimization of technique

  • Naveen Subhas
  • Andrew N. Primak
  • Nancy A. Obuchowski
  • Amit Gupta
  • Joshua M. Polster
  • Andreas Krauss
  • Joseph P. Iannotti
Technical Report



Iterative metal artifact reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique.

Materials and methods

Eight patients with nine TSA underwent CT with standardized parameters: 140 kVp, 300 mAs, 0.6 mm collimation and slice thickness, and B30 kernel. WFBP, three 3D IMAR algorithms with different amounts of WFBP high-frequency data (IMARlo, lowest; IMARmod, moderate; IMARhi, highest), and one 2D IMAR algorithm were reconstructed. Differences in attenuation near hardware and away from hardware were measured and compared using repeated measures ANOVA. Five readers independently graded image quality; scores were compared using Friedman’s test.


Attenuation differences were smaller with all 3D IMAR techniques than with WFBP (p < 0.0063). With increasing high-frequency data, the attenuation difference increased slightly (differences not statistically significant). All readers ranked IMARmod and IMARhi more favorably than WFBP (p < 0.05), with IMARmod ranked highest for most structures. The attenuation difference was slightly higher with 2D than with 3D IMAR, with no significant reader preference for 3D over 2D.


IMAR significantly decreases metal artifact compared to WFBP both objectively and subjectively in TSA. The incorporation of a moderate amount of WFBP high-frequency data and use of a 2D reconstruction technique optimize image quality and allow for relatively short reconstruction times.


CT Technique Metallic hardware Artifact 



The authors would like to recognize and thank Sahar Shiraj, MD, Cleveland Clinic, for help with data collection, Jennifer Bullen, MS, Cleveland Clinic, for help with statistical analysis, and Megan Griffiths, Cleveland Clinic, for help with manuscript editing and submission.

Conflicts of interest

Naveen Subhas reports that he has received research support from Siemens Healthcare Solutions for research into CT metal artifact reduction. The other authors report no conflicts of interest.


  1. 1.
    Kalender WA, Hebel R, Ebersberger J. Reduction of CT artifacts caused by metallic implants. Radiology. 1987;164(2):576–7.PubMedCrossRefGoogle Scholar
  2. 2.
    Meyer E, Raupach R, Lell M, Schmidt B, Kachelriess M. Normalized metal artifact reduction (NMAR) in computed tomography. Med Phys. 2010;37(10):5482–93.PubMedCrossRefGoogle Scholar
  3. 3.
    Meyer E, Raupach R, Lell M, Schmidt B, Kachelriess M. Frequency split metal artifact reduction (FSMAR) in computed tomography. Med Phys. 2012;39(4):1904–16.PubMedCrossRefGoogle Scholar
  4. 4.
    Lell MM, Meyer E, Kuefner MA, et al. Normalized metal artifact reduction in head and neck computed tomography. Invest Radiol. 2012;47(7):415–21.PubMedCrossRefGoogle Scholar
  5. 5.
    Morsbach F, Wurnig M, Kunz DM, et al. Metal artefact reduction from dental hardware in carotid CT angiography using iterative reconstructions. Eur Radiol. 2013;23(10):2687–94.PubMedCrossRefGoogle Scholar
  6. 6.
    Morsbach F, Bickelhaupt S, Wanner GA, Krauss A, Schmidt B, Alkadhi H. Reduction of metal artifacts from hip prostheses on CT images of the pelvis: value of iterative reconstructions. Radiology. 2013;268(1):237–44.PubMedCrossRefGoogle Scholar
  7. 7.
    Bamberg F, Dierks A, Nikolaou K, Reiser MF, Becker CR, Johnson TR. Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol. 2011;21(7):1424–9.PubMedCrossRefGoogle Scholar

Copyright information

© ISS 2014

Authors and Affiliations

  • Naveen Subhas
    • 1
  • Andrew N. Primak
    • 2
  • Nancy A. Obuchowski
    • 3
  • Amit Gupta
    • 1
  • Joshua M. Polster
    • 1
  • Andreas Krauss
    • 4
  • Joseph P. Iannotti
    • 5
  1. 1.Cleveland ClinicImaging InstituteClevelandUSA
  2. 2.Siemens Medical Solutions USA Inc.MalvernUSA
  3. 3.Quantitative Health Sciences, Cleveland ClinicClevelandUSA
  4. 4.Siemens HealthcareForchheimGermany
  5. 5.Cleveland ClinicOrthopaedic & Rheumatologic InstituteClevelandUSA

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