Literature DB >> 30076784

Evaluation of two commercial CT metal artifact reduction algorithms for use in proton radiotherapy treatment planning in the head and neck area.

Karin M Andersson1,2, Christina Vallhagen Dahlgren1, Johan Reizenstein3, Yang Cao4,5, Anders Ahnesjö6, Per Thunberg7.   

Abstract

PURPOSE: To evaluate two commercial CT metal artifact reduction (MAR) algorithms for use in proton treatment planning in the head and neck (H&N) area.
METHODS: An anthropomorphic head phantom with removable metallic implants (dental fillings or neck implant) was CT-scanned to evaluate the O-MAR (Philips) and the iMAR (Siemens) algorithms. Reference images were acquired without any metallic implants in place. Water equivalent thickness (WET) was calculated for different path directions and compared between image sets. Images were also evaluated for use in proton treatment planning for parotid, tonsil, tongue base, and neck node targets. The beams were arranged so as to not traverse any metal prior to the target, enabling evaluation of the impact on dose calculation accuracy from artifacts surrounding the metal volume. Plans were compared based on γ analysis (1 mm distance-to-agreement/1% difference in local dose) and dose volume histogram metrics for targets and organs at risk (OARs). Visual grading evaluation of 30 dental implant patient MAR images was performed by three radiation oncologists.
RESULTS: In the dental fillings images, ΔWET along a low-density streak was reduced from -17.0 to -4.3 mm with O-MAR and from -16.1 mm to -2.3 mm with iMAR, while for other directions the deviations were increased or approximately unchanged when the MAR algorithms were used. For the neck implant images, ΔWET was generally reduced with MAR but residual deviations remained (of up to -2.3 mm with O-MAR and of up to -1.5 mm with iMAR). The γ analysis comparing proton dose distributions for uncorrected/MAR plans and corresponding reference plans showed passing rates >98% of the voxels for all phantom plans. However, substantial dose differences were seen in areas of most severe artifacts (γ passing rates of down to 89% for some cases). MAR reduced the deviations in some cases, but not for all plans. For a single patient case dosimetrically evaluated, minor dose differences were seen between the uncorrected and MAR plans (γ passing rate approximately 97%). The visual grading of patient images showed that MAR significantly improved image quality (P < 0.001).
CONCLUSIONS: O-MAR and iMAR significantly improved image quality in terms of anatomical visualization for target and OAR delineation in dental implant patient images. WET calculations along several directions, all outside the metallic regions, showed that both uncorrected and MAR images contained metal artifacts which could potentially lead to unacceptable errors in proton treatment planning. ΔWET was reduced by MAR in some areas, while increased or unchanged deviations were seen for other path directions. The proton treatment plans created for the phantom images showed overall acceptable dose distributions differences when compared to the reference cases, both for the uncorrected and MAR images. However, substantial dose distribution differences in the areas of most severe artifacts were seen for some plans, which were reduced by MAR in some cases but not all. In conclusion, MAR could be beneficial to use for proton treatment planning; however, case-by-case evaluations of the metal artifact-degraded images are always recommended.
© 2018 The Authors Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Entities:  

Keywords:  computed tomography; dose calculation; metal artifacts; proton therapy; radiotherapy

Mesh:

Substances:

Year:  2018        PMID: 30076784     DOI: 10.1002/mp.13115

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  10 in total

Review 1.  Status and innovations in pre-treatment CT imaging for proton therapy.

Authors:  Patrick Wohlfahrt; Christian Richter
Journal:  Br J Radiol       Date:  2019-11-11       Impact factor: 3.039

2.  Metal implants on abdominal CT: does split-filter dual-energy CT provide additional value over iterative metal artifact reduction?

Authors:  Hildegard M Wichtmann; Kai R Laukamp; Sebastian Manneck; Konrad Appelt; Bram Stieltjes; Daniel T Boll; Matthias R Benz; Markus M Obmann
Journal:  Abdom Radiol (NY)       Date:  2022-09-30

Review 3.  Dental management in head and neck cancers: from intensity-modulated radiotherapy with photons to proton therapy.

Authors:  Sabah Falek; Rajesh Regmi; Joel Herault; Melanie Dore; Anthony Vela; Pauline Dutheil; Cyril Moignier; Pierre-Yves Marcy; Julien Drouet; Arnaud Beddok; Noah E Letwin; Joel Epstein; Upendra Parvathaneni; Juliette Thariat
Journal:  Support Care Cancer       Date:  2022-05-05       Impact factor: 3.359

4.  Comparison of quantitative measurements of four manufacturer's metal artifact reduction techniques for CT imaging with a self-made acrylic phantom.

Authors:  Ryan Chou; Hung-Yi Chi; Yi-Hung Lin; Liu-Kuo Ying; Yu-Ju Chao; Cheng-Hsun Lin
Journal:  Technol Health Care       Date:  2020       Impact factor: 1.285

5.  CT metal artifact reduction algorithms: Toward a framework for objective performance assessment.

Authors:  J Y Vaishnav; B Ghammraoui; M Leifer; R Zeng; L Jiang; K J Myers
Journal:  Med Phys       Date:  2020-06-05       Impact factor: 4.071

6.  NRG Oncology Survey of Monte Carlo Dose Calculation Use in US Proton Therapy Centers.

Authors:  Liyong Lin; Paige A Taylor; Jiajian Shen; Jatinder Saini; Minglei Kang; Charles B Simone; Jeffrey D Bradley; Zuofeng Li; Ying Xiao
Journal:  Int J Part Ther       Date:  2021-05-25

7.  Impact of metal artifact reduction algorithm on gross tumor volume delineation in tonsillar cancer: reducing the interobserver variation.

Authors:  Yoshiyuki Fukugawa; Ryo Toya; Tomohiko Matsuyama; Takahiro Watakabe; Yoshinobu Shimohigashi; Yudai Kai; Tadashi Matsumoto; Natsuo Oya
Journal:  BMC Med Imaging       Date:  2022-09-06       Impact factor: 2.795

8.  Geometric and dosimetric impact of 3D generative adversarial network-based metal artifact reduction algorithm on VMAT and IMPT for the head and neck region.

Authors:  Mitsuhiro Nakamura; Megumi Nakao; Keiho Imanishi; Hideaki Hirashima; Yusuke Tsuruta
Journal:  Radiat Oncol       Date:  2021-06-06       Impact factor: 3.481

9.  Targeting Treatment Resistance in Head and Neck Squamous Cell Carcinoma - Proof of Concept for CT Radiomics-Based Identification of Resistant Sub-Volumes.

Authors:  Marta Bogowicz; Matea Pavic; Oliver Riesterer; Tobias Finazzi; Helena Garcia Schüler; Edna Holz-Sapra; Leonie Rudofsky; Lucas Basler; Manon Spaniol; Andreas Ambrusch; Martin Hüllner; Matthias Guckenberger; Stephanie Tanadini-Lang
Journal:  Front Oncol       Date:  2021-05-27       Impact factor: 6.244

10.  Dosimetric impact of using a commercial metal artifact reduction tool in carbon ion therapy in patients with hip prostheses.

Authors:  Jingfang Zhao; Weiwei Wang; Kambiz Shahnaz; Xianwei Wu; Jingfang Mao; Ping Li; Qing Zhang
Journal:  J Appl Clin Med Phys       Date:  2021-06-23       Impact factor: 2.102

  10 in total

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