Dose and Risk Characterization in CT

  • Cameron Kofler
  • Edmond Olguin
  • Andres Abadia
  • Wesley E. BolchEmail author


Computed tomography is an indispensable modality for obtaining critical diagnostic information regarding healthy and diseased tissues of the patient. It is widely accepted that the medical benefit of computed tomography imaging vastly exceeds any harm incurred to the patient following x-ray exposure. Nevertheless, CT imaging exams should be medical justified, and the CT imaging protocols applied should be optimized to balance image quality and patient dose. In this chapter, we review various forms of patient dose metrics and discuss methods by which individual organ doses to patients of both genders, of all ages, and of varying body morphometries may be assessed using computational human phantoms and Monte Carlo radiation transport simulation. These values of individual organ dose may then be used to report nominal values of risk through either radiation detriment, as determined by the quantity effective dose, or the lifetime attributable risk (LAR) of cancer. The former averages population-based risks across both sexes, all ages, and several populations, while the latter retains these individual features of the risk profile. The radiation epidemiological models that underpin these risk estimates are based on dose-response data obtained from long-term follow-up of exposed populations and thus may only be applied, at best, at the population level – such as cohorts of young female patients or those of older male patients – and never at the level of an individual patient. These population-based nominal risks, however, are needed for CT imaging protocol optimization. The chapter concludes with a numerical example of organ doses and risks – both radiation detriment and cancer incidence/mortality – for an age-dependent series of reference patients undergoing 18FDG PE/CT imaging.


Computed tomography Organ dose Computational human phantom Monte Carlo radiation transport Cancer risk 


  1. 1.
    ICRP. ICRP Publication 102: managing patient dose in multi-dector computed tomography (MDCT). Ann ICRP. 2007;37(1):1–79.CrossRefGoogle Scholar
  2. 2.
    AAPM. AAPM Report No. 204 – Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. American Association of Physicists in Medicine: college Park; 2011.Google Scholar
  3. 3.
    AAPM. AAPM Report No. 220 – Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT. American Association of Physicists in Medicine: college Park; 2014.Google Scholar
  4. 4.
    BEIR. Health risks from exposure to low levels of ionizing radiation: BEIR VII – phase 2. Washington, DC: National Research Council; 2005.Google Scholar
  5. 5.
    UNSCEAR. In: U.N.S.C.o.t.E.o.A. Radiation, editor. Effects of Ionizing Radiation - Annex A: Epidemiological Studies of Radiation and Cancer. New York: United Nations; 2006.Google Scholar
  6. 6.
    Pawel D, Puskin J. U.S. Environmental Protection Agency radiogenic risk models and projections for the U.S. population. Health Phys. 2012;102(6):646–56.CrossRefGoogle Scholar
  7. 7.
    Hintenlang DE, Moloney W, Winslow J. Physical phantoms for experimental radiation dosimetry. In: Xu XG, Eckerman KF, editors. Handbook of anatomical models for radiation dosimetry. London: Taylor & Francis Group; 2009.Google Scholar
  8. 8.
    Cristy M. Mathematical phantoms representing children of various ages for use in estimates of internal dose. Oak Ridge: Oak Ridge National Laboratory; 1980.Google Scholar
  9. 9.
    Han EY, Bolch WE, Eckerman KF. Revisions to the ORNL series of adult and pediatric computational phantoms for use with the MIRD schema. Health Phys. 2006;90(4):337–56.CrossRefGoogle Scholar
  10. 10.
    ICRP. ICRP Publication 110: adult reference computational phantoms. Ann ICRP. 2009;39(2):1–165.CrossRefGoogle Scholar
  11. 11.
    Kim C, et al. The reference phantoms: voxel vs. polygon. Ann ICRP. 2016;45:188–201.CrossRefGoogle Scholar
  12. 12.
    Furuta T, et al. Implementation of tetrahedral-mesh geometry in Monte Carlo radiation transport code PHITS. Phys Med Biol. 2017;62(12):4798–810.CrossRefGoogle Scholar
  13. 13.
    ICRP ICRP publication 23: report on the task group on reference man. In: Annals of the ICRP. Oxford: International Commission on Radiological Protection: Pergamon Press; 1975. p. 1–480.Google Scholar
  14. 14.
    ICRP. ICRP publication 89: basic anatomical and physiological data for use in radiological protection – reference values. Ann ICRP. 2002;32(3–4):1–277.Google Scholar
  15. 15.
    Segars WP, et al. Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization. Med Phys. 2013;40(4):043701.CrossRefGoogle Scholar
  16. 16.
    Geyer AM, et al. 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. 2014;59(18):5225–42.CrossRefGoogle Scholar
  17. 17.
    Johnson P, et al. Hybrid patient-dependent phantoms covering statistical distributions of body morphometry in the US adult and pediatric population. Proc IEEE. 2009;97(12):2060–75.CrossRefGoogle Scholar
  18. 18.
    Sands MM, et al. Comparison of methods for individualized astronaut organ dosimetry: morphometry-based phantom library versus body contour autoscaling of a reference phantom. Life Sci Space Res (Amst). 2017;15:23–31.CrossRefGoogle Scholar
  19. 19.
    DeMarco JJ, et al. A Monte Carlo based method to estimate radiation dose from multidetector CT (MDCT): cylindrical and anthropomorphic phantoms. Phys Med Biol. 2005;50(17):3989–4004.CrossRefGoogle Scholar
  20. 20.
    Li X, et al. Patient-specific radiation dose and cancer risk estimation in CT: part I. development and validation of a Monte Carlo program. Med Phys. 2011;38(1):397–407.CrossRefGoogle Scholar
  21. 21.
    Li X, et al. Patient-specific radiation dose and cancer risk estimation in CT: part II. Application to patients. Med Phys. 2011;38(1):408–19.CrossRefGoogle Scholar
  22. 22.
    Khatonabadi M, et al. The feasibility of a regional CTDIvol to estimate organ dose from tube current modulated CT exams. Med Phys. 2013;40(5):051903.CrossRefGoogle Scholar
  23. 23.
    Stepusin EJ, et al. Physical validation of a Monte Carlo-based phantom-derived approach to computed tomography organ dosimetry under tube current modulation. Med Phys. 2017;44:5423–32.CrossRefGoogle Scholar
  24. 24.
    Stepusin EJ, et al. Assessment of different patient-to-phantom matching criteria applied to a Monte Carlo-based CT organ dose library. Med Phys. 2017;44:5498–508.CrossRefGoogle Scholar
  25. 25.
    Turner AC, et al. A method to generate equivalent energy spectra and filtration models based on measurement for multidetector CT Monte Carlo dosimetry simulations. Med Phys. 2009;36(6):2154–64.CrossRefGoogle Scholar
  26. 26.
    NCRP. NCRP commentary no. 27: implications of recent epidemiological studies for the linear non-threshold model and radiation protection. Bethesda: National Council on Radiation Protection and Measurement; 2018. p. 1–210.Google Scholar
  27. 27.
    ICRP. ICRP publication 103: recommendations of the international commission on radiological protection. Ann ICRP. 2007;37(2–4):1–332.Google Scholar
  28. 28.
    de Gonzalez AB, et al. RadRAT: a radiation risk assessment tool for lifetime cancer risk projection. J Radiol Prot. 2012;32(3):205–22.CrossRefGoogle Scholar
  29. 29.
    Lee C, et al. The UF family of reference hybrid phantoms for computational radiation dosimetry. Phys Med Biol. 2010;55(2):339–63.CrossRefGoogle Scholar
  30. 30.
    ICRP. ICRP publication 128: radiation dose to patients from radiopharmaceuticals – a compendium of current information related to frequently used substances. Ann ICRP. 2015;44(2S):1–321.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Cameron Kofler
    • 1
  • Edmond Olguin
    • 1
  • Andres Abadia
    • 1
  • Wesley E. Bolch
    • 2
    Email author
  1. 1.Medical Physics Graduate ProgramUniversity of FloridaGainesvilleUSA
  2. 2.Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA

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