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Review of 3D image data calibration for heterogeneity correction in proton therapy treatment planning

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Abstract

Correct modelling of the interaction parameters of patient tissues is of vital importance in proton therapy treatment planning because of the large dose gradients associated with the Bragg peak. Different 3D imaging techniques yield different information regarding these interaction parameters. Given the rapidly expanding interest in proton therapy, this review is written to make readers aware of the current challenges in accounting for tissue heterogeneities and the imaging systems that are proposed to tackle these challenges. A summary of the interaction parameters of interest in proton therapy and the current and developmental 3D imaging techniques used in proton therapy treatment planning is given. The different methods to translate the imaging data to the interaction parameters of interest are reviewed and a summary of the implementations in several commercial treatment planning systems is presented.

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References

  1. Papanikolaou N, Battista JJ, Boyer AL, Kappas C, Klein E, Mackie TR, Sharpe M, Van Dyk J (2004) Tissue inhomogeneity corrections for megavoltage photon beams. AAPM Task Gr 65:1–142

    Google Scholar 

  2. Robinson DM (2008) Inhomogeneity correction and the analytic anisotropic algorithm (AAA). J Appl Clin Med Phys 9(2):1–12

    Google Scholar 

  3. Verhaegen F, Devic S (2005) Sensitivity study for CT image use in Monte Carlo treatment planning. Phys Med Biol 50(5):937

    Article  PubMed  Google Scholar 

  4. Schaffner B, Pedroni E (1998) The precision of proton range calculations in proton radiotherapy treatment planning: experimental verification of the relation between CT-HU and proton stopping power. Phys Med Biol 43(6):1579

    Article  CAS  PubMed  Google Scholar 

  5. Paganetti H (2012) Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys Med Biol 57(11):R99

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Newhauser WD, Zhang R (2015) The physics of proton therapy. Phys Med Biol 60(8):R155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tavernier S (2010) Experimental techniques in nuclear and particle physics. Springer Science & Business Media, Heidelberg

    Book  Google Scholar 

  8. Chen GT, Singh RP, Castro JR, Lyman JT, Quivey JM (1979) Treatment planning for heavy ion radiotherapy. Int J Radiat Oncol Biol Phys 5(10):1809–1819

    Article  CAS  PubMed  Google Scholar 

  9. Schneider U, Pemler P, Besserer J, Pedroni E, Lomax A, Kaser-Hotz B (2005) Patient specific optimization of the relation between CT-Hounsfield units and proton stopping power with proton radiography. Med Phys 32(1):195–199

    Article  PubMed  Google Scholar 

  10. Kanematsu N, Inaniwa T, Koba Y (2012) Relationship between electron density and effective densities of body tissues for stopping, scattering, and nuclear interactions of proton and ion beams. Med Phys 39(2):1016–1020

    Article  CAS  PubMed  Google Scholar 

  11. Jiang H, Seco J, Paganetti H (2007) Effects of Hounsfield number conversion on CT based proton Monte Carlo dose calculations. Med Phys 34(4):1439–1449

    Article  PubMed  PubMed Central  Google Scholar 

  12. Schneider U, Pedroni E, Lomax A (1996) The calibration of CT Hounsfield units for radiotherapy treatment planning. Phys Med Biol 41(1):111

    Article  CAS  PubMed  Google Scholar 

  13. Yang M, Virshup G, Clayton J, Zhu X, Mohan R, Dong L (2010) Theoretical variance analysis of single-and dual-energy computed tomography methods for calculating proton stopping power ratios of biological tissues. Phys Med Biol 55(5):1343

    Article  CAS  PubMed  Google Scholar 

  14. DeMarco J, Solberg T, Smathers JB (1998) A CT-based Monte Carlo simulation tool for dosimetry planning and analysis. Med Phys 25(1):1–11

    Article  CAS  PubMed  Google Scholar 

  15. Du Plessis F, Willemse C, Lötter M, Goedhals L (1998) The indirect use of CT numbers to establish material properties needed for Monte Carlo calculation of dose distributions in patients. Med Phys 25(7):1195–1201

    Article  PubMed  Google Scholar 

  16. Bazalova M, Carrier J-F, Beaulieu L, Verhaegen F (2008) Dual-energy CT-based material extraction for tissue segmentation in Monte Carlo dose calculations. Phys Med Biol 53(9):2439

    Article  PubMed  Google Scholar 

  17. Landry G, Parodi K, Wildberger JE, Verhaegen F (2013) Deriving concentrations of oxygen and carbon in human tissues using single-and dual-energy CT for ion therapy applications. Phys Med Biol 58(15):5029

    Article  PubMed  Google Scholar 

  18. Johns HE, Cunningham JR (1974) The physics of radiology. Charles C Thomas, Springfield, p 457

    Google Scholar 

  19. Gottschalk B, Koehler A, Schneider R, Sisterson J, Wagner M (1993) Multiple Coulomb scattering of 160 MeV protons. Nucl Instrum Methods Phys Res Sect B 74(4):467–490

    Article  Google Scholar 

  20. Fermi E (1940) The ionization loss of energy in gases and in condensed materials. Phys Rev 57(6):485

    Article  CAS  Google Scholar 

  21. Eyges L (1948) Multiple scattering with energy loss. Phys Rev 74(10):1534

    Article  CAS  Google Scholar 

  22. Kanematsu N (2009) Semi-empirical formulation of multiple scattering for the Gaussian beam model of heavy charged particles stopping in tissue-like matter. Phys Med Biol 54(5):N67

    Article  PubMed  Google Scholar 

  23. Gottschalk B (2010) On the scattering power of radiotherapy protons. Med Phys 37(1):352–367. doi:10.1118/13264177

    Article  CAS  PubMed  Google Scholar 

  24. Ibbott GS (1985) Radiation dosimetry: electron beams with energies between 1 and 50 MeV (ICRU report No. 35). Med Phys 12(6):813–813

    Article  Google Scholar 

  25. Fippel M, Soukup M (2004) A Monte Carlo dose calculation algorithm for proton therapy. Med Phys 31(8):2263–2273

    Article  PubMed  Google Scholar 

  26. ICRU (2000) Nuclear data for neutron and proton radiotherapy and for radiation protection dose. International commission on radiation units and measurements report 63

  27. ICRU (1989) Tissue substitutes in radiation dosimetry and measurement. International commission on radiation units and measurements report 44

  28. Knopf A-C, Lomax A (2013) In vivo proton range verification: a review. Phys Med Biol 58(15):R131

    Article  PubMed  Google Scholar 

  29. Schneider W, Bortfeld T, Schlegel W (2000) Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. Phys Med Biol 45(2):459

    Article  CAS  PubMed  Google Scholar 

  30. Rutherford R, Pullan B, Isherwood I (1976) Measurement of effective atomic number and electron density using an EMI scanner. Neuroradiology 11(1):15–21

    Article  CAS  PubMed  Google Scholar 

  31. Fornaro J, Leschka S, Hibbeln D, Butler A, Anderson N, Pache G, Scheffel H, Wildermuth S, Alkadhi H, Stolzmann P (2011) Dual-and multi-energy CT: approach to functional imaging. Insights Imaging 2(2):149–159

    Article  PubMed  PubMed Central  Google Scholar 

  32. Torikoshi M, Tsunoo T, Sasaki M, Endo M, Noda Y, Ohno Y, Kohno T, Hyodo K, Uesugi K, Yagi N (2003) Electron density measurement with dual-energy X-ray CT using synchrotron radiation. Phys Med Biol 48(5):673

    Article  PubMed  Google Scholar 

  33. Johns H, Cunningham J (1983) The physics of radiology, 4th edn. Thomas, Springfield

    Google Scholar 

  34. Hünemohr N, Paganetti H, Greilich S, Jäkel O, Seco J (2014) Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy. Med Phys 41(6):061714

    Article  PubMed  PubMed Central  Google Scholar 

  35. Landry G, Seco J, Gaudreault M, Verhaegen F (2013) Deriving effective atomic numbers from DECT based on a parameterization of the ratio of high and low linear attenuation coefficients. Phys Med Biol 58(19):6851

    Article  CAS  PubMed  Google Scholar 

  36. Cormack AM (1963) Representation of a function by its line integrals, with some radiological applications. J Appl Phys 34(9):2722–2727

    Article  Google Scholar 

  37. Goitein M (1972) Three-dimensional density reconstruction from a series of two-dimensional projections. Nucl Instrum Methods 101(3):509–518

    Article  Google Scholar 

  38. Schulte R, Bashkirov V, Li T, Liang Z, Mueller K, Heimann J, Johnson LR, Keeney B, Sadrozinski HF, Seiden A (2004) Conceptual design of a proton computed tomography system for applications in proton radiation therapy. Nucl Sci IEEE Trans 51(3):866–872

    Article  CAS  Google Scholar 

  39. NICADD (2015) http://www.niu.edu/nicadd/research/medical/

  40. Cormack A, Koehler A (1976) Quantitative proton tomography: preliminary experiments. Phys Med Biol 21(4):560

    Article  CAS  PubMed  Google Scholar 

  41. Hanson KM (1979) Proton computed tomography. Nucl Sci IEEE Trans 26(1):1635–1640

    Article  Google Scholar 

  42. Hanson K, Bradbury J, Cannon T, Hutson R, Laubacher D, Macek R, Paciotti M, Taylor C (1981) Computed tomography using proton energy loss. Phys Med Biol 26(6):965

    Article  CAS  PubMed  Google Scholar 

  43. Hanson K, Bradbury J, Koeppe R, Macek R, Machen D, Morgado R, Paciotti M, Sandford S, Steward V (1982) Proton computed tomography of human specimens. Phys Med Biol 27(1):25

    Article  CAS  PubMed  Google Scholar 

  44. Zygmanski P, Gall KP, Rabin MS, Rosenthal SJ (2000) The measurement of proton stopping power using proton-cone-beam computed tomography. Phys Med Biol 45(2):511

    Article  CAS  PubMed  Google Scholar 

  45. Schneider U, Pedroni E (1994) Multiple Coulomb scattering and spatial resolution in proton radiography. Med Phys 21(11):1657–1663

    Article  CAS  PubMed  Google Scholar 

  46. Williams D (2004) The most likely path of an energetic charged particle through a uniform medium. Phys Med Biol 49(13):2899

    Article  CAS  PubMed  Google Scholar 

  47. Schulte R, Penfold S, Tafas J, Schubert K (2008) A maximum likelihood proton path formalism for application in proton computed tomography. Med Phys 35(11):4849–4856

    Article  CAS  PubMed  Google Scholar 

  48. Bethe H (1930) Zur theorie des durchgangs schneller korpuskularstrahlen durch materie. Ann Phys 397(3):325–400

    Article  Google Scholar 

  49. Leo W (1988) Techniques for nuclear and particle physics experiments. Nucl Instrum Methods Phys Res 834:290

    Google Scholar 

  50. Li T, Liang Z, Singanallur JV, Satogata TJ, Williams DC, Schulte RW (2006) Reconstruction for proton computed tomography by tracing proton trajectories: a Monte Carlo study. Med Phys 33(3):699–706

    Article  PubMed  PubMed Central  Google Scholar 

  51. Penfold S, Schulte RW, Censor Y, Rosenfeld AB (2010) Total variation superiorization schemes in proton computed tomography image reconstruction. Med Phys 37(11):5887–5895

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rit S, Dedes G, Freud N, Sarrut D, Létang JM (2013) Filtered backprojection proton CT reconstruction along most likely paths. Med Phys 40(3):031103

    Article  PubMed  Google Scholar 

  53. Poludniowski G, Allinson N, Evans P (2014) Proton computed tomography reconstruction using a backprojection-then-filtering approach. Phys Med Biol 59(24):7905

    Article  CAS  PubMed  Google Scholar 

  54. Penfold S, Censor Y (2015) Techniques in iterative proton CT image reconstruction. Sens Imaging 16(1):1–21

    Article  Google Scholar 

  55. Schulte RW, Bashkirov V, Klock MCL, Li T, Wroe AJ, Evseev I, Williams DC, Satogata T (2005) Density resolution of proton computed tomography. Med Phys 32(4):1035–1046

    Article  PubMed  Google Scholar 

  56. ICRU (1992) Photon, electron, proton, and neutron interaction data for body tissues. International commission on radiation units and measurements report 46

  57. ICRP (1975) Reference man: anatomical, physiological and metabolic characteristics. ICRP Publication 23

  58. Jackson DF, Hawkes D (1981) X-ray attenuation coefficients of elements and mixtures. Phys Rep 70(3):169–233

    Article  CAS  Google Scholar 

  59. Janni JF (1982) Proton range-energy tables, 1 keV–10 GeV, energy loss, range, path length, time-of-flight, straggling, multiple scattering, and nuclear interaction probability. Part I. For 63 compounds. At Data Nucl Data Tables 27:147

    Article  CAS  Google Scholar 

  60. Berger M, Inokuti M, Andersen H, Bichsel H (1993) Stopping powers and ranges for protons and alpha particles. ICRU report 49

  61. Doolan P, Testa M, Sharp G, Bentefour E, Royle G, Lu H (2015) Patient-specific stopping power calibration for proton therapy planning based on single-detector proton radiography. Phy Med Biol 60(5):1901

    Article  CAS  Google Scholar 

  62. Bourque AE, Carrier J-F, Bouchard H (2014) A stoichiometric calibration method for dual energy computed tomography. Phys Med Biol 59(8):2059

    Article  PubMed  Google Scholar 

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Acknowledgments

The authors would like to acknowledge J. Pollard for her constructive comments in reviewing this article.

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Correspondence to Jiahua Zhu.

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Zhu, J., Penfold, S.N. Review of 3D image data calibration for heterogeneity correction in proton therapy treatment planning. Australas Phys Eng Sci Med 39, 379–390 (2016). https://doi.org/10.1007/s13246-016-0447-9

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