Radiological Physics and Technology

, Volume 11, Issue 4, pp 467–472 | Cite as

Geometric distortion in magnetic resonance imaging systems assessed using an open-source plugin for scientific image analysis

  • Takahiro AoyamaEmail author
  • Hidetoshi Shimizu
  • Ikuo Shimizu
  • Atsushi Teramoto
  • Naoki Kaneda
  • Kazuhiko Nakamura
  • Masaru Nakamura
  • Takeshi Kodaira


Tumor locations are commonly delineated by referring to magnetic resonance (MR) images. However, MR images have geometric distortions that cannot be completely corrected. This study aimed to investigate quantitatively uncorrectable error [residual error (RE)] with the use of an open-source plugin for scientific image analysis. The RE values were calculated by Fiji, which was enhanced by Image J image processing software. The results obtained with the open-source plugin for scientific image analysis agreed with the results obtained with the commercially available software. Obtaining detailed geometric distortion data for each facility and device could facilitate safe treatment because the homogeneous magnetic field in MR imaging varies across devices and over time. Therefore, using an open-source plugin for scientific image analysis may be an accurate and effective technique for evaluating the RE of MR imaging systems.


Image distortion Magnetic resonance imaging Open-source plugin for scientific image analysis Residual error Stereotactic radiation therapy 



We are grateful to Mr Yoshito Ichiba of Siemens Healthineers, Japan K.K., for his useful suggestions. We thank Mr Masamiti Hojo of QualitA, Ltd., for his helpful advice. Additionally, we thank the Japan Association of Radiological Technologists. Furthermore, the authors would like to thank Enago ( for the English language review.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to declare.

Statement of human and animal rights

There is no animal or humans involved in this study.

Informed consent

There are no human subjects involved in this work.


  1. 1.
    Seute T, Leffers P, ten Velde GP, Twijnstra A. Detection of brain metastases from small cell lung cancer: consequences of changing imaging techniques (CT versus MRI). Cancer. 2008;112:1827–34.CrossRefGoogle Scholar
  2. 2.
    Halasz LM, Rockhill JK. Stereotactic radiosurgery and stereotactic radiotherapy for brain metastases. Surg Neurol Int. 2013;4:185–91.CrossRefGoogle Scholar
  3. 3.
    Wang D, Strugnell W, Cowin G, Doddrell DM, Slaughter R. Geometric distortion in clinical MRI systems: part I: evaluation using a 3D phantom. J Magn Reson. 2004;22:1211–21.Google Scholar
  4. 4.
    Watanabe Y, Lee CK, Gerbi BJ. Geometrical accuracy of a 3-tesla magnetic resonance imaging unit in Gamma Knife surgery. Spec Suppl. 2006;105:190–3.Google Scholar
  5. 5.
    Weygand J, Fuller CD, lbbott GS, Mohamed AS, Ding Y, Yang J, et al. Spatial precision in magnetic resonance imaging-guided radiation therapy: the role of geometric distortion. Int J Radiat Oncol Biol Phys. 2016;95:1304–16.CrossRefGoogle Scholar
  6. 6.
    Wang D, Strugnell W, Cowin G, Doddrell DM, Slaughter R. Geometric distortion in clinical MRI systems: part II: correction using a 3D phantom. Magn Reson Med. 2004;29:1223–32.Google Scholar
  7. 7.
    Schmidt MA, Wells EJ, Davison K, Riddell AM, Welsh L, Saran F. Stereotactic radiosurgery planning of vestibular schwannomas: is MRI at 3 T geometrically accurate? Med Phys. 2017;44:375–81.CrossRefGoogle Scholar
  8. 8.
    Weygand J, Fuller D, Ibbott GS, Mohamed AS, Ding Y, Yang J, et al. Spatial precision in magnetic resonance imaging—guided radiation therapy: the role of geometric distortion. Int J Radiat Oncol Biol Phys. 2016;95:1304–16.CrossRefGoogle Scholar
  9. 9.
    Baldwin LN, Wachowicz K, Thomas DS, Rivest R, Fallone BG. Characterization, prediction, and correction of geometric distortion in 3T MR images. Med Phys. 2007;34:388–99.CrossRefGoogle Scholar
  10. 10.
    Walker A, Liney G, Metcalfe P. MRI distortion: consideration for MRI based radiotherapy treatment planning. Australas Phs Eng Sci Med. 2014;37:103–13.CrossRefGoogle Scholar
  11. 11.
    Sun J, Dowling J, Pichler P, Menk F, Rivest-Henault D, Lambert J, et al. MRI simulation: end-to-end testing for prostate radiation therapy using geometric pelvic MRI phantoms. Int J Radiat Oncol Biol Phys. 2015;60:3097–109.Google Scholar
  12. 12.
    Fatemi A, Taghizadeh S, Yang C, Kanakamedara MR, Morris B, Vijayakumar S. Machine-specific magnetic resonance imaging quality control procedures for stereotactic radiosurgery treatment planning. Cureus. 2017;9(12):e1957.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Takemura A, Sasamoto K, Nakamura K, Kuroda T, Shoji S, Matsuura Y, et al. Comparison of image distortion between three magnetic resonance imaging systems of different magnetic field strengths for use in stereotactic irradiation of brain. Nihon Hoshasen Gijutsu Gakkaizasshi Zasshi. 2013;69:641–7.CrossRefGoogle Scholar
  14. 14.
    Wetzel SG, Johnson G, Tan AG, Cha S, Knopp EA, Lee VS, et al. Three-dimensional, T1-weighted gradient-echo imaging of the brain with a volumetric interpolated examination. Am J Neuroradiol. 2002;23:995–1002.PubMedGoogle Scholar
  15. 15.
    Kwak HS, Hwang S, Chung GH, Song JS, Choi EJ. Detection of small brain metastases at 3 T: comparing the diagnostic performances of contrast-enhanced T1-weighted SPACE, MPRAGE, and 2D FLASH imaging. Clin Imaging. 2014;39:571–5.CrossRefGoogle Scholar
  16. 16.
    Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pitzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676–82.CrossRefGoogle Scholar
  17. 17.
    Rasband WS. ImageJ US. National Institutes of Health, Bethesda, Maryland, USA. 1997–2012.
  18. 18.
    Carrasco E, Calvo MI, Espada J. DNA labeling in vivo: quantification of epidermal stem cell chromatin content in whole mouse hair follicles using Fiji image processing software. Methods Mol Biol. 2014;1094:79–88.CrossRefGoogle Scholar
  19. 19.
    NEMA-MS-2. Determination of two-dimensional geometric distortion in diagnostic magnetic resonance images: MS 2–2008 (R2014). Rosslyn: National Electrical Manufacturers Association; 2003.Google Scholar
  20. 20.
    Chudler EH. Brain facts and figures. University of Washington. 2017. Accessed 17 Oct 2017.

Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2018

Authors and Affiliations

  1. 1.Department of Radiation OncologyAichi Cancer Center HospitalNagoyaJapan
  2. 2.Graduate School of Radiological TechnologyGunma Prefectural College of Health SciencesMaebashiJapan
  3. 3.Department of RadiologyAichi Medical University HospitalNagakuteJapan
  4. 4.School of Health SciencesFujita Health UniversityToyoakeJapan

Personalised recommendations