Advertisement

Dose coefficients of percentile-specific computational phantoms for photon external exposures

  • Yeon Soo Yeom
  • Haegin Han
  • Chansoo Choi
  • Bangho Shin
  • Chan Hyeong KimEmail author
  • Choonsik Lee
Original Article
  • 38 Downloads

Abstract

The use of dose coefficients (DCs) based on the reference phantoms recommended by the International Commission on Radiological Protection (ICRP) with a fixed body size may produce errors to the estimated organ/tissue doses to be used, for example, for epidemiologic studies depending on the body size of cohort members. A set of percentile-specific computational phantoms that represent 10th, 50th, and 90th percentile standing heights and body masses in adult male and female Caucasian populations were recently developed by modifying the mesh-type ICRP reference computational phantoms (MRCPs). In the present study, these percentile-specific phantoms were used to calculate a comprehensive dataset of body-size-dependent DCs for photon external exposures by performing Monte Carlo dose calculations with the Geant4 code. The dataset includes the DCs of absorbed doses for 29 individual organs/tissues from 0.01 to 104 MeV photon energy, in the antero-posterior, postero-anterior, right lateral, left lateral, rotational, and isotropic geometries. The body-size-dependent DCs were compared with the DCs of the MRCPs in the reference body size, showing that the DCs of the MRCPs are generally similar to those of the 50th percentile standing height and body mass phantoms over the entire photon energy region except for low energies (≤ 0.03 MeV); the differences are mostly less than 10%. In contrast, there are significant differences in the DCs between the MRCPs and the 10th and 90th percentile standing height and body mass phantoms (i.e., H10M10 and H90M90). At energies of less than about 10 MeV, the MRCPs tended to under- and over-estimate the organ/tissue doses of the H10M10 and H90M90 phantoms, respectively. This tendency was revised at higher energies. The DCs of the percentile-specific phantoms were also compared with the previously published values of another phantom sets with similar body sizes, showing significant differences particularly at energies below about 0.1 MeV, which is mainly due to the different locations and depths of organs/tissues between the different phantom libraries. The DCs established in the present study should be useful to improve the dosimetric accuracy in the reconstructions of organ/tissue doses for individuals in risk assessment for epidemiologic investigations taking body sizes into account.

Keywords

Body size Computational phantom Photon Dose coefficient Monte Carlo 

Notes

Acknowledgements

This work was funded by the intramural program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. This work was also supported by the Nuclear Safety Research Development (NSR&D) Program through the Korea Foundation of Nuclear Safety (KOFONS) funded by the Nuclear Safety and Security Commission (NSSC) and by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning through the National Research Foundation of Korea (Project nos.: 1705006, 2016R1D1A1A09916337). One of the authors (Yeon Soo Yeom) was supported by a grant of the Korean Health Technology R&D Project through the Korean Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (Project no: H18C2257). Two of the authors (Chansoo Choi and Haegin Han) were supported by the Global PhD Fellowship program (Project nos.: NRF-2017H1A2A1046391, NRF-2018H1A2A1059767). The calculations in this work were performed on the National Institutes of Health’s High-Performance Computing Biowulf cluster (http://hpc.nih.gov).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

411_2019_818_MOESM1_ESM.xlsx (1.4 mb)
Supplementary material 1 (XLSX 1411 kb)

References

  1. Akhavanallaf A, Xie T, Zaidi H (2019) Development of a library of adult computational phantoms based on anthropometric indexes. IEEE Trans Radiat Plasma Med Sci 3:65–75.  https://doi.org/10.1109/TRPMS.2018.2816072 CrossRefGoogle Scholar
  2. Allison J, Amako K, Apostolakis J, Arce P, Asai M, Aso T, Bagli E, Bagulya A, Banerjee S, Beck BR, Bogdanov AG, Brandt D, Brown JMC, Burkhardt H, Canal P, Cano-Ott D, Chauvie S, Cho K, Cirrone GAP, Cooperman G, Cortés-Giraldo MA, Cosmo G, Cuttone G, Depaola G, Desorgher L, Dong X, Dotti A, Elvira VD, Folger G, Francis Z, Galoyan A, Garnier L, Gayer M, Genser KL, Grichine VM, Guatelli S, Guèye P, Gumplinger P, Howard AS, Hřivnáčová I, Hwang S, Incerti S, Ivanchenko A, Ivanchenko VN, Jones FW, Jun SY, Kaitaniemi P, Karakatsanis N, Karamitros M, Kelsey M, Kimura A, Koi T, Kurashige H, Lechner A, Lee SB, Longo F, Maire M, Mancusi D, Mantero A, Mendoza E, Morgan B, Murakami K, Nikitina T, Pandola L, Paprocki P, Perl J, Petrović I, Pia MG, Pokorski W, Quesada JM, Raine M, Reis MA, Ribon A, Ristić Fira A, Romano F, Russo G, Santin G, Sasaki T, Sawkey D, Shin JI, Strakovski II, Taborda A, Tanaka S, Tomé B, Toshito T, Tran HN, Truscott PR, Urban L, Uzhinski V, Verbeke JM, Verderi M, Wendt BL, Wenzel H, Wright DH, Yamashita T, Yarba J, Yoshida H (2016) Recent developments in Geant4. Nucl Instr Meth Phys Res A 835:186–225.  https://doi.org/10.1016/j.nima.2016.06.125 ADSCrossRefGoogle Scholar
  3. Broggio D, Beurrier J, Bremaud M, Desbrée A, Farah J, Huet C, Franck D (2011) Construction of an extended library of adult male 3D models: rationale and results. Phys Med Biol 56:7659–7692.  https://doi.org/10.1088/0031-9155/56/23/020 CrossRefGoogle Scholar
  4. Cassola VF, Milian FM, Kramer R, de Oliveira Lira CA, Khoury HJ (2011) Standing adult human phantoms based on 10th, 50th and 90th mass and height percentiles of male and female Caucasian populations. Phys Med Biol 56:3749–3772.  https://doi.org/10.1088/0031-9155/56/13/002 CrossRefGoogle Scholar
  5. Chang LA, Borrego D, Lee C (2018) Body-weight dependent dose coefficients for adults exposed to idealised external photon fields. J Radiol Prot 38:1441–1453.  https://doi.org/10.1088/1361-6498/aae66e CrossRefGoogle Scholar
  6. Chen Y, Qiu R, Li C, Wu Z, Li J (2016) Construction of Chinese adult male phantom library and its application in the virtual calibration ofin vivomeasurement. Phys Med Biol 61:2124–2144.  https://doi.org/10.1088/0031-9155/61/5/2124 ADSCrossRefGoogle Scholar
  7. Choi Y, Shil Cha E, Jin Bang Y, Ko S, Ha M, Lee C, Jin Lee W (2018) Estimation of organ doses among diagnostic medical radiation workers in South Korea. Radiat Prot Dosimetry 179:142–150.  https://doi.org/10.1093/rpd/ncx239 CrossRefGoogle Scholar
  8. Geyer AM, O’Reilly S, Lee C, Long DJ, Bolch WE (2014) The UF/NCI family of hybrid computational phantoms representing the current US population of male and female children, adolescents, and adults—application to CT dosimetry. Phys Med Biol 59:5225–5242.  https://doi.org/10.1088/0031-9155/59/18/5225 CrossRefGoogle Scholar
  9. Gordon CC, Blackwell CL, Bradtmiller B, Parham JL, Barrientos P, Paquette SP, Corner BD, Carson J, Venezia JC, Rockwell BM, Mucher M, Kristensen S (2014) 2012 anthropometric survey of U.S. army personnel: methods and summary statistics. Army Natick Soldier Research Development and Engineering Center MA, NatickGoogle Scholar
  10. ICRP (2007) The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publ 103 Ann ICRP 37:1–332Google Scholar
  11. ICRP (2009) Adult reference computational phantoms. ICRP Publ 110 Ann ICRP 39:1–166Google Scholar
  12. ICRP (2010) Conversion coefficients for radiological protection quantities for external radiation exposures. ICRP Publ 116 Ann ICRP 40:1–258Google Scholar
  13. Johnson PB, Bahadori AA, Eckerman KF, Lee C, Bolch WE (2011) Response functions for computing absorbed dose to skeletal tissues from photon irradiation—an update. Phys Med Biol 56:2347–2365.  https://doi.org/10.1088/0031-9155/56/8/002 CrossRefGoogle Scholar
  14. Kainz W, Neufeld E, Bolch WE, Graff CG, Kim CH, Kuster N, Lloyd B, Morrison T, Segars P, Yeom YS, Zankl M, Xu XG, Tsui BMW (2019) Advances in computational human phantoms and their applications in biomedical engineering—a topical review. IEEE Trans Radiat Plasma Med Sci 3:1–23.  https://doi.org/10.1109/TRPMS.2018.2883437 CrossRefGoogle Scholar
  15. Kim CH, Yeom YS, Nguyen TT, Han MC, Choi C, Lee H, Han H, Shin B, Lee JK, Kim HS, Zankl M, Petoussi-Henss N, Bolch WE, Lee C, Chung BS, Qiu R, Eckerman K (2018) New mesh-type phantoms and their dosimetric applications, including emergencies. Ann ICRP.  https://doi.org/10.1177/0146645318756231 CrossRefGoogle Scholar
  16. Kim S, Chang L, Mosher E, Lee C, Lee C (2019) A feasibility study to reduce misclassification error in occupational dose estimates for epidemiological studies using body size-dependent computational phantoms. IEEE Trans Radiat Plasma Med Sci 3:83–88.  https://doi.org/10.1109/TRPMS.2018.2847227 CrossRefGoogle Scholar
  17. Land CE, Kwon D, Hoffman FO, Moroz B, Drozdovitch V, Bouville A, Beck H, Luckyanov N, Weinstock RM, Simon SL (2015) Accounting for shared and unshared dosimetric uncertainties in the dose response for ultrasound-detected thyroid nodules after exposure to radioactive fallout. Radiat Res 183:159–173.  https://doi.org/10.1667/RR13794.1 ADSCrossRefGoogle Scholar
  18. Lee H, Yeom YS, Nguyen TT, Choi C, Han H, Shin B, Zhang X, Kim CH, Chung BS, Zankl M (2019) Percentile-specific computational phantoms constructed from ICRP mesh-type reference computational phantoms (MRCPs). Phys Med Biol 64:045005.  https://doi.org/10.1088/1361-6560/aafcdb CrossRefGoogle Scholar
  19. Simon SL, Bouville A, Kleinerman R, Ron E (2006) Dosimetry for epidemiological studies: learning from the past, looking to the future. Radiat Res 166:313–318.  https://doi.org/10.1667/RR3536.1 ADSCrossRefGoogle Scholar
  20. Simon SL, Preston DL, Linet MS, Miller JS, Sigurdson AJ, Alexander BH, Kwon D, Yoder RC, Bhatti P, Little MP, Rajaraman P, Melo D, Drozdovitch V, Weinstock RM, Doody MM (2014) Radiation organ doses received in a nationwide cohort of U.S. radiologic technologists: methods and findings. Radiat Res 182:507–528.  https://doi.org/10.1667/RR13542.1 ADSCrossRefGoogle Scholar
  21. Yeom YS, Han MC, Kim CH, Jeong JH (2013) Conversion of ICRP male reference phantom to polygon-surface phantom. Phys Med Biol 58:6985.  https://doi.org/10.1088/0031-9155/58/19/6985 CrossRefGoogle Scholar
  22. Yeom YS, Jeong JH, Han MC, Kim CH (2014) Tetrahedral-mesh-based computational human phantom for fast Monte Carlo dose calculations. Phys Med Biol 59:3173–3185.  https://doi.org/10.1088/0031-9155/59/12/3173 CrossRefGoogle Scholar
  23. Yeom YS, Wang ZJ, Nguyen TT, Kim HS, Choi C, Han MC, Kim CH, Lee JK, Chung BS, Zankl M, Petoussi-Henss N, Bolch WE, Lee C (2016) Development of skeletal system for mesh-type ICRP reference adult phantoms. Phys Med Biol 61:7054–7073.  https://doi.org/10.1088/0031-9155/61/19/7054 CrossRefGoogle Scholar
  24. Yeom YS, Choi C, Han H, Lee H, Shin B, Nguyen TT, Han MC, Lee C, Kim CH (2019) Dose coefficients of mesh-type ICRP reference computational phantoms for idealized external exposures of photons and electrons. Nucl Eng Technol 51:843–852.  https://doi.org/10.1016/j.net.2018.12.006 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Cancer Epidemiology and GeneticsNational Cancer Institute, National Institutes of HealthRockvilleUSA
  2. 2.Department of Nuclear EngineeringHanyang UniversitySeoulKorea

Personalised recommendations