Characteristics of inverse gamma histograms

Abstract

This work explores the characteristics of the inverse gamma histogram and its potential use as part of the patient specific quality assurance (PSQA) program for volumetric modulated arc therapy (VMAT). ArcCheck measured dose files and TPS predicted dose files were imported and analysed using the in-house inverse gamma code developed in the Python package. Inverse gamma with fixed distance-to-agreement of 2 mm were calculated for 23 VMAT arcs. Dose difference histograms were plotted for six arbitrarily selected arcs with the 95th and 90th percentile values calculated. Dose difference histograms enabled visualisation of the dose difference distribution information. The 95th and 90th percentile values are equivalent to the dose difference criteria where the gamma pass rate is 95% and 90% respectively. These values can be used as a guide to assess plan acceptability, especially for plans that failed the initial gamma evaluation. The inverse gamma histograms are demonstrated to be a useful tool for plan evaluation in addition to the traditional gamma evaluation method. It contains dose difference or distance-to-agreement distribution information, which could be clinically useful for plan evaluation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. 1.

    Schreibmann E, Dhabaan A, Elder E, Fox T (2009) Patient-specific quality assurance method for VMAT treatment delivery. Med Phys 36(10):4530–4535

    Article  Google Scholar 

  2. 2.

    Cozzi L, Dinshaw KA, Shrivastava SK et al (2008) A treatment planning study comparing volumetric arc modulation with RapidArc and fixed field IMRT for cervix uteri radiotherapy. Radiother Oncol 89(2):180–191

    Article  Google Scholar 

  3. 3.

    Fogliata A, Clivio A, Nicolini G, Vanetti E, Cozzi L (2008) Intensity modulation with photons for benign intracranial tumours: a planning comparison of volumetric single arc, helical arc and fixed gantry techniques. Radiother Oncol 89(3):254–262

    Article  Google Scholar 

  4. 4.

    Stasi M, Bresciani S, Miranti A, Maggio A, Sapino V, Gabriele P (2012) Pretreatment patient-specific IMRT quality assurance: a correlation study between gamma index and patient clinical dose volume histogram. Med Phys 39(12):7626–7634

    CAS  Article  Google Scholar 

  5. 5.

    Nelms BE, Zhen H, Tomé WA (2011) Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors. Med Phys 38(2):1037–1044

    Article  Google Scholar 

  6. 6.

    Grégoire V, Mackie T (2011) State of the art on dose prescription, reporting and recording in Intensity-Modulated Radiation Therapy (ICRU report No. 83). Cancer/Radiothérapie. 15(6–7):555–559

    Article  Google Scholar 

  7. 7.

    Low DA, Harms WB, Mutic S, Purdy JA (1998) A technique for the quantitative evaluation of dose distributions. Med Phys 25(5):656–661

    CAS  Article  Google Scholar 

  8. 8.

    Miften M, Olch A, Mihailidis D et al (2018) Tolerance limits and methodologies for IMRT measurement-based verification QA: recommendations of AAPM Task Group No. 218. Med Phys. 45(4):e53–e83

    Article  Google Scholar 

  9. 9.

    Jiang SB, Sharp GC, Neicu T, Berbeco RI, Flampouri S, Bortfeld T (2006) On dose distribution comparison. Phys Med Biol 51(4):759

    Article  Google Scholar 

  10. 10.

    Depuydt T, Van Esch A, Huyskens DP (2002) A quantitative evaluation of IMRT dose distributions: refinement and clinical assessment of the gamma evaluation. Radiother Oncol 62(3):309–319

    Article  Google Scholar 

  11. 11.

    Zhen H, Nelms BE, Tomé WA (2011) Moving from gamma passing rates to patient DVH-based QA metrics in pretreatment dose QA. Med Phys 38(10):5477–5489

    Article  Google Scholar 

  12. 12.

    Kruse JJ (2010) On the insensitivity of single field planar dosimetry to IMRT inaccuracies. Med Phys 37(6Part 1):2516–2524

    Article  Google Scholar 

  13. 13.

    Carrasco P, Jornet N, Latorre A, Eudaldo T, Ruiz A, Ribas M (2012) 3D DVH-based metric analysis versus per-beam planar analysis in IMRT pretreatment verification. Med Phys 39(8):5040–5049

    Article  Google Scholar 

  14. 14.

    Heilemann G, Poppe B, Laub W (2013) On the sensitivity of common gamma-index evaluation methods to MLC misalignments in Rapidarc quality assurance. Med Phys 40(3):031702

    CAS  Article  Google Scholar 

  15. 15.

    Nelms BE, Chan MF, Jarry G et al (2013) Evaluating IMRT and VMAT dose accuracy: practical examples of failure to detect systematic errors when applying a commonly used metric and action levels. Med Phys 40(11):111722

    Article  Google Scholar 

  16. 16.

    Kry SF, Molineu A, Kerns JR et al (2014) Institutional patient-specific IMRT QA does not predict unacceptable plan delivery. Int J Radiat Oncol Biol Phys 90(5):1195–1201

    Article  Google Scholar 

  17. 17.

    Stojadinovic S, Ouyang L, Gu X, Pompoš A, Bao Q, Solberg TD (2015) Breaking bad IMRT QA practice. J Appl Clin Med Phys. 16(3):154–165

    Article  Google Scholar 

  18. 18.

    Childress NL, Rosen II (2003) The design and testing of novel clinical parameters for dose comparison. Int J Radiat Oncol* Biol* Phys 56(5):1464–1479

    Article  Google Scholar 

  19. 19.

    Sumida I, Yamaguchi H, Kizaki H et al (2015) Novel radiobiological gamma index for evaluation of 3-dimensional predicted dose distribution. Int J Radiat Oncol* Biol* Phys 92(4):779–786

    Article  Google Scholar 

  20. 20.

    Liting Y, Kairn T, Trapp J, Crowe SB (2019) A modified gamma analysis method for dose distribution comparisons. J Appl Clin Med Phys. https://doi.org/10.1002/acm2.12606

    Article  Google Scholar 

  21. 21.

    Low DA, Dempsey JF (2003) Evaluation of the gamma dose distribution comparison method. Med Phys 30(9):2455–2464

    Article  Google Scholar 

  22. 22.

    Spezi E, Lewis DG (2006) Gamma histograms for radiotherapy plan evaluation. Radiother Oncol 79(2):224–230

    Article  Google Scholar 

  23. 23.

    Al Sa’d M, Graham J, Liney G, Moore CJ (2013) Quantitative comparison of 3D and 2.5 D gamma analysis: introducing gamma angle histograms. Phys Med Biol 58(8):2597

    Article  Google Scholar 

  24. 24.

    Hisey K, Morales-Paliza M, Ding G (2013) SU-E-T-168: a feasibility study to use gamma-histogram analysis in assisting quality assurance criteria for evaluating volumetric modulated arc therapy treatment plans. Med Phys 40(6Part13):242–242

    Article  Google Scholar 

  25. 25.

    Li H, Dong L, Zhang L, Yang JN, Gillin MT, Zhu XR (2011) Toward a better understanding of the gamma index: investigation of parameters with a surface-based distance method. Med Phys 38(12):6730–6741

    Article  Google Scholar 

  26. 26.

    Oliphant TE. A guide to NumPy, vol 1. Trelgol Publishing USA; 2006

  27. 27.

    Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng. 9(3):90–95

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Liting Yu.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interests.

Ethical approval

No human participants were involved in the procedures performed in this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, L., Kairn, T., Trapp, J.V. et al. Characteristics of inverse gamma histograms. Phys Eng Sci Med 43, 659–664 (2020). https://doi.org/10.1007/s13246-020-00873-4

Download citation

Keywords

  • PSQA
  • Gamma evaluation
  • Inverse gamma
  • Histogram