MR-based CT metal artifact reduction for head-and-neck photon, electron, and proton radiotherapy

Jonathan Scharff Nielsen*, Koen Van Leemput, Jens Morgenthaler Edmund

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

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Purpose: We investigated the impact on computed tomography (CT) image quality and photon, electron, and proton head-and-neck (H&N) radiotherapy (RT) dose calculations of three CT metal artifact reduction (MAR) approaches: A CT-based algorithm (oMAR Philips Healthcare), manual water override, and our recently presented, Magnetic Resonance (MR)-based kerMAR algorithm. We considered the following three hypotheses: I: Manual water override improves MAR over the CT- and MR-based alternatives; II: The automatic algorithms (oMAR and kerMAR) improve MAR over the uncorrected CT; III: kerMAR improves MAR over oMAR. Methods: We included a veal shank phantom with/without six metal inserts and nine H&N RT patients with dental implants. We quantified the MAR capabilities by the reduction of outliers in the CT value distribution in regions of interest, and the change in particle range and photon depth at maximum dose. Results: Water override provided apparent image improvements in the soft tissue region but insignificantly or negatively influenced the dose calculations. We however found significant improvements in image quality and particle range impact, compared to the uncorrected CT, when using oMAR and kerMAR. kerMAR in turn provided superior improvements in terms of high intensity streak suppression compared to oMAR, again with associated impacts on the particle range estimates. Conclusion: We found no benefits of the water override compared to the rest, and tentatively reject hypothesis I. We however found improvements in the automatic algorithms, and thus support for hypothesis II, and found the MR-based kerMAR to improve upon oMAR, supporting hypothesis III.
Original languageEnglish
JournalMedical Physics
Issue number10
Pages (from-to)4314-4323
Publication statusPublished - 2019


  • Bayesian modelling
  • Computed tomography
  • CT metal artifact reduction
  • Proton therapy
  • Radiotherapy


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