Radiological Physics and Technology

, Volume 11, Issue 2, pp 174–183 | Cite as

Effect of region extraction and assigned mass-density values on the accuracy of dose calculation with magnetic resonance-based volumetric arc therapy planning

  • Keisuke Usui
  • Keisuke Sasai
  • Koichi Ogawa


This study aimed to verify the validity of generating treatment plans for volumetric arc therapy (VMAT) for prostate cancer using magnetic resonance (MR) imaging with a dose calculation algorithm in Acuros XB (Eclipse version 13.6; Varian Medical Systems, Palo Alto, CA, USA) based on deterministically solving the linear Boltzmann transport equations. Four different classes were applied to prostate MR images: MRW (all water equivalent); MRW+B (water and bone); MRS+B (soft tissue and bone); and MRS+B+G (soft tissue, bone, and rectal gas). Each of these regions was assigned a mass density for calculating doses. The assigned mass-density values were then altered in three ways. Using initial planning and optimization parameters, MR-based VMAT plans were generated and compared with corresponding forward-calculated computed tomography-based plans for doses to the target volumes and organs at risk using dose-volume histograms and γ analyses. In the MRW plans, the mean doses for TVs were overestimated by approximately 1.3%. The MRW+B plans revealed reduced differences within 0.5%. Further segmentation (MRS+B) did not result in substantial improvement. Dose deviations affected by the changes in the mass densities assigned to soft tissue were as small as approximately 1.0%, whereas larger deviations were revealed in bone and rectal gas, especially those with > 5% error. Assignment of accurate mass-density values acquired from MR images is needed for MR-based radiation treatment planning. Multiple MR sequences should be acquired for segmentation and mass-density conversion purposes. Segmented MR-based VMAT planning is feasible with a density assignment method using Acuros XB.


Dose calculation MR image VMAT Boltzmann transport equations Segmentation 



This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript.

Ethics approval

All procedures involving human participants were performed in accordance with the ethics committee of the authors’ institution, and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. This study did not involve animals.

Informed consent

Informed consent was obtained from all participants included in this study.


  1. 1.
    Barker JL Jr, Garden AS, Ang KK, O’Daniel JC, Wang H, Court LE, et al. Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. Int J Radiat Oncol Biol Phys. 2004;59:960–70.CrossRefPubMedGoogle Scholar
  2. 2.
    Hong TS, Tomé WA, Chappell RJ, Chinnaiyan P, Mehta MP, Harari PM. The impact of daily setup variations on head-and-neck intensity modulated radiation therapy. Int J Radiat Oncol Biol Phys. 2005;61:779–88.CrossRefPubMedGoogle Scholar
  3. 3.
    Barney BM, Lee RJ, Handrahan D, Welsh KT, Cook JT, Sause WT. Image-guided radiotherapy (IGRT) for prostate cancer comparing KV imaging of fiduciary markers with cone beam computed tomography (CBCT). Int J Rad Oncol Biol Phys. 2011;80:301–5.CrossRefGoogle Scholar
  4. 4.
    Ding GX, Duggan DM, Coffey CW, Deeley M, Hallahan DE, Cmelak A, et al. A study on adaptive IMRT treatment planning using kV cone-beam CT. Radiother Oncol. 2007;85:116–25.CrossRefPubMedGoogle Scholar
  5. 5.
    Menten MJ, Wetscherek A, Fast MF. MRI-guided lung SBRT: present and future developments. Phys Med. 2017;S1120–1797:30032–7.Google Scholar
  6. 6.
    Rasch C, Steenbakkers R, van Herk M. Target definition in prostate, head, and neck. Semin Radiat Oncol. 2005;15:136–45.CrossRefPubMedGoogle Scholar
  7. 7.
    Keall PJ, Barton M, Crozier S. The Australian magnetic resonance imaging-linac program. Semin Radiat Oncol. 2014;24:203–6.CrossRefPubMedGoogle Scholar
  8. 8.
    Jaffray DA, Carlone MC, Milosevic MF, Breen SL, Stanescu T, Rink A, et al. A facility for magnetic resonance-guided radiation therapy. Semin Radiat Oncol. 2014;24:193–5.CrossRefPubMedGoogle Scholar
  9. 9.
    Bostel T, Nicolay NH, Grossmann JG, Mohr A, Delorme S, Echner G, et al. MR-guidance—a clinical study to evaluate a shuttle-based MR-linac connection to provide MR-guided radiotherapy. Radiat Oncol. 2014;9:12–9.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Fallone BG, Murray B, Rathee S, Stanescu T, Steciw S, Vidakovic S, et al. MR images obtained during megavoltage photon irradiation from a prototype integrated linac-MR system. Med Phys. 2009;36:2084–8.CrossRefPubMedGoogle Scholar
  11. 11.
    Chin AL, Lin A, Anamalayil S, Teo BK. Feasibility and limitations of bulk density assignment in MRI for head and neck IMRT treatment planning. J Appl Clin Med Phys. 2014;15:100–11.CrossRefPubMedCentralGoogle Scholar
  12. 12.
    den Dekker AJ, Sijbers J. Data distributions in magnetic resonance images: a review. Phys Med. 2014;30:725–41.CrossRefGoogle Scholar
  13. 13.
    Korsholm ME, Waring LW, Edmund JM. A criterion for the reliable use of MRI-only radiotherapy. Radiat Oncol. 2014;9:16–22.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Korhonen J, Kapanen M, Keyriläinen J, Seppälä T, Tenhunen M. A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer. Med Phys. 2014;41:011704. Scholar
  15. 15.
    Andreasen D, Van Leemput K, Edmund JM. A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis. Med Phys. 2016;43:4742–52. Scholar
  16. 16.
    Wang D, Strugnell W, Cowin G, Doddrell DM, Slaughter R. Geometric distortion in clinical MRI systems part I: evaluation using a 3D phantom. Magn Reson Imaging. 2004;22:1211–21.CrossRefPubMedGoogle Scholar
  17. 17.
    Baldwin LN, Wachowicz K, Thomas SD, Rivest R, Fallone BG. Characterization, prediction, and correction of geometric distortion in 3 T MR images. Med Phys. 2007;34:388–99.CrossRefPubMedGoogle Scholar
  18. 18.
    Chen Z, Ma CM, Paskalev K, Li J, Yang J, Richardson T, Palacio L, Xu X, Chen L. Investigation of MR image distortion for radiotherapy treatment planning of prostate cancer. Phys Med Biol. 2006;51:1393–403.CrossRefPubMedGoogle Scholar
  19. 19.
    Prott FJ, Haverkamp U, Eich H, Resch A, Micke O, Fischedick AR, Willich N, Pötter R. Effect of distortions and asymmetry in MR images on radiotherapeutic treatment planning. Int J Cancer. 2000;90:46–50.CrossRefPubMedGoogle Scholar
  20. 20.
    Crijns SP, Bakker CJ, Seevinck PR, de Leeuw H, Laqendijk JJ, Raaymakers BW. Towards inherently distortion-free MR images for image-guided radiotherapy on an MRI accelerator. Phys Med Biol. 2012;57:1349–58.CrossRefPubMedGoogle Scholar
  21. 21.
    Kapanen M, Collan J, Beule A, Seppala T, Saarilahti K, Tenhunen M. Commissioning of MRI-only based treatment planning procedure for external beam radiotherapy of prostate. Magn Reson Med. 2013;70:127–35.CrossRefPubMedGoogle Scholar
  22. 22.
    Vassiliev ON, Wareing TA, McGhee J, Failla G, Salehpour MR, Mourtada F. Validation of a new grid-based Boltzmann equation solver for dose calculation in radiotherapy with photon beams. Phys Med Biol. 2010;55:581–98.CrossRefPubMedGoogle Scholar
  23. 23.
    Shirotani T. Attenuation coefficients of human tissues and tissue substitutes. Japan Atomic Energy Research. JAERI-Data/Code 95-002, INIS 26; 1995 [in Japanese].Google Scholar
  24. 24.
    Kapanen M, Tenhunen M. T1/T2*-weighted MRI provides clinically relevant pseudo-CT density data for the pelvic bones in MRI-only based radiotherapy treatment planning. Acta Oncol. 2013;52:612–8.CrossRefPubMedGoogle Scholar
  25. 25.
    Korhonen J, Kapanen M, Keyrilainen J, Seppala T, Tuomikoski L, Tenhunen M. Absorbed doses behind bones with MR image-based dose calculations for radiotherapy treatment planning. Med Phys. 2013;40:011701. Scholar
  26. 26.
    Dunlop A, McQuaid D, Nill S, Murray J, Poludniowski G, Hansen VN, et al. Comparison of CT number calibration techniques for CBCT-based dose calculation. Strahlenther Onkol. 2015;191:970–8.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Keereman V, Fierens Y, Broux T, De Deene Y, Lonneux M, Vandenberqhe S. MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. J Nucl Med. 2010;51:812–8.CrossRefPubMedGoogle Scholar
  28. 28.
    Johansson A, Karlsson M, Nyholm T. CT substitute derived from MRI sequences with ultrashort echo time. Med Phys. 2011;38:2708–14.CrossRefPubMedGoogle Scholar
  29. 29.
    Ldiyatullin D, Corum C, Moeller S, Prasad HS, Garwood M, Nixdorf DR. Dental magnetic resonance imaging: making the invisible visible. J Endod. 2011;37:745–52.CrossRefGoogle Scholar
  30. 30.
    Maspero M, Seevinck PR, Schubert G, Hoesl MA, van Asselen B, Vierqever MA, Laqendijk JJ, Meijer GJ, van den Berq CA. Quantification of confounding factors in MRI-based dose calculations as applied to prostate IMRT. Phys Med Biol. 2017;62:948–65.CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Radiation OncologyJuntendo UniversityTokyoJapan
  2. 2.Faculty of Science and EngineeringHosei UniversityKoganeiJapan

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