The Impact of Metal Artifact Reduction in Estimation of Dose Distribution in ISOgray Treatment Planning Software

Document Type : Original Article

Authors

1 Department of Medical Physics , Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

2 Nuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

3 Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.

Abstract

Purpose:
Some cancer patients who receive radiation therapy have metal objects in their body. There is a difference between atomic numbers of these metal objects and body tissues. This creates artifacts in images and creates large difference in CT numbers and errors in the calculations. This study was performed to evaluate the effect of correcting metal artifact on images and compare the absorbed dose estimated by ISOgray treatment planning system (TPS) and measurement.
Methods:
Homogeneous cylindrical phantom was prepared from Perspex material to simulate human body conditions. Cavities were created for Farmer dosimeter and titanium metal rods acting as prosthesis. CT images of phantom were acquired in the presence and absence of metal rods. The correction of metal artifact was performed using Metal Detection Technique (MDT) and CT control softwares. Dose calculation was done at the phantom center for 100 MU at the photon mode with the energies of 6, 10 and 15 MV at ISOgray TPS. The results were compared with Farmer dosimeter.
Results:
The results show that there is no significant difference in absorbed dose calculated for different images corrected using two softwares by ISOgray TPS. The lowest and highest errors are 0.1% and 1.09% related to the images corrected by MDT software with the slice thickness of 3.0 cm and the distance of 2.5 cm and photon energies of 6 and 15 MV, respectively.
Conclusion:
The correction of artifacts in CT images does not have a significant effect on the dose calculated by ISOgray.

Keywords

Main Subjects

References

  1. Bockisch A, Freudenberg LS, Schmidt D, Kuwert T, editors. Hybrid imaging by SPECT/CT and PET/CT: proven outcomes in cancer imaging. Seminars in nuclear medicine; 2009: Elsevier.
  2. Chapman D, Smith S, Barnett R, Bauman G, Yartsev S. Optimization of tomotherapy treatment planning for patients with bilateral hip prostheses. Radiation Oncology 2014; 9(1): 43.
  3. Glide‐Hurst C, Chen D, Zhong H, Chetty I. Changes realized from extended bit‐depth and metal artifact reduction in CT. Medical physics 2013; 40(6): 061711.
  4. Chen Y, Li Y, Guo H, Hu Y, Luo L, Yin X, et al. CT metal artifact reduction method based on improved image segmentation and sinogram in-painting. Mathematical Problems in Engineering 2012; 2012: 18.
  5. Laguda EJ. Dosimetric Evaluation of Metal Artefact Reduction using Metal Artefact Reduction (MAR) Algorithm and Dual-energy Computed Tomography (CT) Method 2016.
  6. Glover GH, Pelc NJ. An algorithm for the reduction of metal clip artifacts in CT reconstructions. Med Phys. 1981; 8(6):7 99-807.
  7. Li H, Noel C, Chen H, Harold Li H, et al. Clinical evaluation of a commercial orthopedic metal artifact reduction tool for CT simulations in radiation therapy. Medical physics 2012; 39(12): 7507-17.
  8. Huang JY, Kerns JR, Nute JL, Liu X, et al. An evaluation of three commercially available metal artifact reduction methods for CT imaging. Physics in Medicine & Biology 2015; 60(3): 1047.
  9. Jeong S, Kim SH, Hwang EJ, Shin C-i, et al. Usefulness of a metal artifact reduction algorithm for orthopedic implants in abdominal CT: phantom and clinical study results. American Journal of Roentgenology 2015; 204(2): 307-17.
  10. Joemai R, de Bruin PW, Veldkamp WJ, Geleijns J. Metal artifact reduction for CT: Development, implementation, and clinical comparison of a generic and a scanner‐specific technique. Medical physics 2012; 39(2): 1125-32.
  11. Abdoli M, Mehranian A, Ailianou A, Becker M, et al. Assessment of metal artifact reduction methods in pelvic CT. Medical physics 2016; 43(4): 1588-97.
  12. Giantsoudi D, De Man B, Verburg J, Trofimov A, et al. Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction. Physics in Medicine and Biology 2017; 62(8): R49.
  13. Roberts R. How accurate is a CT-based dose calculation on a pencil beam TPS for a patient with a metallic prosthesis? Physics in medicine and biology. 2001; 46(9): N227.
  14. Hasani, Mohammadi, Khairullah, Gholami, Soraya, Nabavi. A Study of the Precise Algorithm of the Radiation Design System in the Prognosis of the Prosthodonal Prosthodonion Using Montecarlo Simulation. Razi Medical Journal. 2016; 23 (147): 64-73. [Persian]
  15. Kwon H, Kim K, Chun Y, Wu H, Carlson J, Park J, et al. Evaluation of a commercial orthopaedic metal artefact reduction tool in radiation therapy of patients with head and neck cancer. The British journal of radiology 2015; 88(1052): 20140536.
  16. Andersson KM, Ahnesjö A, Dahlgren CV. Evaluation of a metal artifact reduction algorithm in CT studies used for proton radiotherapy treatment planning. Journal of Applied Clinical Medical Physics 2014; 15(5): 112-9.
  17. Spadea MF, Verburg J, Baroni G, Seco J. Dosimetric assessment of a novel metal artifact reduction method in CT images. Journal of applied clinical medical physics 2013; 14(1): 299-304.
  18. ReVision Radiology: CT metal artifacts reduction using the Metal Deletion Technique (MDT). Available: http://www.revisionrads.com/. Accessed 20 May; 2018.
  19. Almond PR, Biggs PJ, Coursey BM, Hanson W, et al. AAPM's TG‐51 protocol for clinical reference dosimetry of high‐energy photon and electron beams. Medical physics 1999; 26(9): 1847-70.
  20. Morsbach F, Bickelhaupt S, Wanner GA, Krauss A, et al. Reduction of metal artifacts from hip prostheses on CT images of the pelvis: value of iterative reconstructions. Radiology 2013; 268(1): 237-44.
  21. Peng C, Qiu B, Li M, Guan Y, et al. Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction. Biomedical engineering online 2017; 16(1): 1.
  22. Schneider W, Bortfeld T, Schlegel W. Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. Physics in Medicine & Biology 2000; 45(2): 459.

Journal of Paramedical Sciences & Rehabilitation
Volume 7, Issue 4
December 2018
Pages 52-61

Files

Share

How to cite

Statistics