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 articleResearchpeer-review

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Abstract

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
Volume46
Issue number10
Pages (from-to)4314-4323
ISSN0094-2405
DOIs
Publication statusPublished - 2019

Keywords

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

Cite this

@article{e59eb9dd3eff41cab5ff648efd799257,
title = "MR-based CT metal artifact reduction for head-and-neck photon, electron, and proton radiotherapy",
abstract = "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.",
keywords = "Bayesian modelling, Computed tomography, CT metal artifact reduction, Proton therapy, Radiotherapy",
author = "Nielsen, {Jonathan Scharff} and {Van Leemput}, Koen and Edmund, {Jens Morgenthaler}",
year = "2019",
doi = "10.1002/mp.13729",
language = "English",
volume = "46",
pages = "4314--4323",
journal = "Medical Physics",
issn = "0094-2405",
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}

MR-based CT metal artifact reduction for head-and-neck photon, electron, and proton radiotherapy. / Nielsen, Jonathan Scharff; Van Leemput, Koen; Edmund, Jens Morgenthaler.

In: Medical Physics, Vol. 46, No. 10, 2019, p. 4314-4323.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - MR-based CT metal artifact reduction for head-and-neck photon, electron, and proton radiotherapy

AU - Nielsen, Jonathan Scharff

AU - Van Leemput, Koen

AU - Edmund, Jens Morgenthaler

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Bayesian modelling

KW - Computed tomography

KW - CT metal artifact reduction

KW - Proton therapy

KW - Radiotherapy

U2 - 10.1002/mp.13729

DO - 10.1002/mp.13729

M3 - Journal article

VL - 46

SP - 4314

EP - 4323

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 10

ER -