Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. National Council on Radiation Protection and Measurements, “Ionizing radiation exposure of the population of the United States,” NCRP Report No. 160 (Bethesda, MD, 2009).
2. F. A. Mettler Jr., B. R. Thomadsen, M. Bhargavan, D. B. Gilley, J. E. Gray, J. A. Lipoti, J. McCrohan, T. T. Yoshizumi, and M. Mahesh, “Medical radiation exposure in the U.S. in 2006: Preliminary results,” Health Phys. 95, 502507 (2008).
3. FDA, “FDA Unveils Initiative to Reduce Unnecessary Radiation Exposure from Medical Imaging” (February 9, 2010). See
4. M. M. Rehani and D. P. Frush, “Patient exposure tracking: The IAEA smart card project,” Radiat. Prot. Dosim. 147, 314316 (2011).
5. R. D. Neumann and D. A. Bluemke, “Tracking radiation exposure from diagnostic imaging devices at the NIH,” J. Am. Coll. Radiol. 7, 8789 (2010).
6.AB-510 Radiation control: Health facilities and clinics: records,” 2011, see;jsessionid=7feb3605cca006f3c98a1e8162a3?bill_id=201120120AB510.
7. C. H. McCollough, S. Leng, L. Yu, D. D. Cody, J. M. Boone, and M. F. McNitt-Gray, “CT dose index and patient dose: They are not the same thing,” Radiology 259, 311316 (2011).
8. J. M. Boone, K. J. Strauss, D. D. Cody, C. McCollough, M. McNitt-Gray, and T. L. Toth, “Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations,” AAPM Report No. 204 (AAPM, College Park, MD, 2011).
9. T. B. Shope, R. M. Gagne, and G. C. Johnson, “A method for describing the doses delivered by transmission x-ray computed tomography,” Med. Phys. 8, 488495 (1981).
10. M. F. McNitt-Gray, “AAPM/RSNA physics tutorial for residents: Topics in CT. Radiation dose in CT,” Radiographics 22, 15411553 (2002).
11. C. H. McCollough, “It is time to retire the computed tomography dose index (CTDI) for CT quality assurance and dose optimization: Against the proposition,” Med. Phys. 33, 11891191 (2006).
12. J. M. Boone, “The trouble with CTDI[sub 100],” Med. Phys. 34, 13641371 (2007).
13. M. K. Kalra, M. M. Maher, T. L. Toth, B. Schmidt, B. L. Westerman, H. T. Morgan, and S. Saini, “Techniques and applications of automatic tube current modulation for CT,” Radiology 233, 649657 (2004).
14. C. H. McCollough, M. R. Bruesewitz, and J. M. Kofler Jr., “CT dose reduction and dose management tools: Overview of available options,” Radiographics 26, 503512 (2006).
15. W. A. Kalender, H. Wolf, C. Suess, M. Gies, H. Greess, and W. A. Bautz, “Dose reduction in CT by on-line tube current control: Principles and validation on phantoms and cadavers,” Eur. Radiol. 9, 323328 (1999).
16. W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements,” Med. Phys. 26, 22482253 (1999).
17. H. Greess, H. Wolf, U. Baum, M. Lell, M. Pirkl, W. Kalender, and W. A. Bautz, “Dose reduction in computed tomography by attenuation-based on-line modulation of tube current: Evaluation of six anatomical regions,” Eur. Radiol. 10, 391394 (2000).
18. E. Angel, N. Yaghmai, C. M. Jude, J. J. Demarco, C. H. Cagnon, J. G. Goldin, A. N. Primak, D. M. Stevens, D. D. Cody, C. H. McCollough, and M. F. McNitt-Gray, “Monte Carlo simulations to assess the effects of tube current modulation on breast dose for multidetector CT,” Phys. Med. Biol. 54, 497512 (2009).
19. International Electrotechnical Commission, “Medical electrical equipment, part 2-44: Particular requirements for the safety of x-ray equipment for computed tomography,” IEC Publication No. 60601-2-44 Amd.1 Ed.3 (IEC, Geneva Switzerland, 2012).
20. W. He, W. Huda, D. Magill, E. Tavrides, and H. Yao, “X-ray tube current modulation and patient doses in chest CT,” Radiat. Prot. Dosim. 143, 8187 (2011).
21. H. Schlattl, M. Zankl, J. Becker, and C. Hoeschen, “Dose conversion coefficients for CT examinations of adults with automatic tube current modulation,” Phys. Med. Biol. 55, 62436261 (2010).
22. H. Schlattl, M. Zankl, J. Becker, and C. Hoeschen, “Dose conversion coefficients for paediatric CT examinations with automatic tube current modulation,” Phys. Med. Biol. 57, 63096326 (2012).
23. M. Gies, W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. I. Simulation studies,” Med. Phys. 26, 22352247 (1999).
24. G. Jarry, J. J. DeMarco, U. Beifuss, C. H. Cagnon, and M. F. McNitt-Gray, “A Monte Carlo-based method to estimate radiation dose from spiral CT: From phantom testing to patient-specific models,” Phys. Med. Biol. 48, 26452663 (2003).
25. J. J. DeMarco, C. H. Cagnon, D. D. Cody, D. M. Stevens, C. H. McCollough, J. O’Daniel, and M. F. McNitt-Gray, “A Monte Carlo based method to estimate radiation dose from multidetector CT (MDCT): Cylindrical and anthropomorphic phantoms,” Phys. Med. Biol. 50, 39894004 (2005).
26. J. J. DeMarco, C. H. Cagnon, D. D. Cody, D. M. Stevens, C. H. McCollough, M. Zankl, E. Angel, and M. F. McNitt-Gray, “Estimating radiation doses from multidetector CT using Monte Carlo simulations: Effects of different size voxelized patient models on magnitudes of organ and effective dose,” Phys. Med. Biol. 52, 25832597 (2007).
27. A. C. Turner, D. Zhang, H. J. Kim, J. J. DeMarco, C. H. Cagnon, E. Angel, D. D. Cody, D. M. Stevens, A. N. Primak, C. H. McCollough, and M. F. McNitt-Gray, “A method to generate equivalent energy spectra and filtration models based on measurement for multidetector CT Monte Carlo dosimetry simulations,” Med. Phys. 36, 21542164 (2009).
28. E. Waters, “MCNPX Version 2.5.C,” Los Alamos National Laboratory Report No. LA-UR-03-2202 (LANL, Los Alamos, NM, 2003).
29. E. Waters, “MCNPX User's Manual, Version 2.4.0,” Los Alamos National Laboratory Report No. LA-CP-02-408 (LANL, Los Alamos, NM, 2002).
30. ICRU, “Tissue substitutes in radiation dosimetry and measurement,” The International Commission on Radiation Units and Measurements Report No. 44 (ICRU, Bethesda, MD, 1989).
31. E. Angel, C. V. Wellnitz, M. M. Goodsitt, N. Yaghmai, J. J. DeMarco, C. H. Cagnon, J. W. Sayre, D. D. Cody, D. M. Stevens, A. N. Primak, C. H. McCollough, and M. F. McNitt-Gray, “Radiation dose to the fetus for pregnant patients undergoing multidetector CT imaging: Monte Carlo simulations estimating fetal dose for a range of gestational age and patient size,” Radiology 249, 220227 (2008).
32. J. J. DeMarco, T. D. Solberg, and J. B. Smathers, “A CT-based Monte Carlo simulation tool for dosimetry planning and analysis,” Med. Phys. 25, 111 (1998).
33. M. Khatonabadi, D. Zhang, K. Mathieu, H. J. Kim, P. Lu, D. Cody, J. J. DeMarco, C. H. Cagnon, and M. F. McNitt-Gray, “A comparison of methods to estimate organ doses in CT when utilizing approximations to the tube current modulation function,” Med. Phys. 39, 52125228 (2012).
34. B. Li, R. H. Behrman, and A. M. Norbash, “Comparison of topogram-based body size indices for CT dose consideration and scan protocol optimization,” Med. Phys. 39, 34563465 (2012).
35. J. Wang, X. Duan, J. A. Christner, S. Leng, L. Yu, and C. H. McCollough, “Attenuation-based estimation of patient size for the purpose of size specific dose estimation in CT. Part I. Development and validation of methods using the CT image,” Med. Phys. 39, 67646771 (2012).
36. J. Wang, J. A. Christner, X. Duan, S. Leng, L. Yu, and C. H. McCollough, “Attenuation-based estimation of patient size for the purpose of size specific dose estimation in CT. Part II. Implementation on abdomen and thorax phantoms using cross sectional CT images and scanned projection radiograph images,” Med. Phys. 39, 67726778 (2012).
37. J. Menke, “Comparison of different body size parameters for individual dose adaptation in body CT of adults,” Radiology 236, 565571 (2005).
38. A. Sodickson, G. I. Warden, C. E. Farkas, I. Ikuta, L. M. Prevedello, K. P. Andriole, and R. Khorasani, “Exposing exposure: Automated anatomy-specific CT radiation exposure extraction for quality assurance and radiation monitoring,” Radiology 264, 397405 (2012).
39. X. Jia, X. Gu, Y. J. Graves, M. Folkerts, and S. B. Jiang, “GPU-based fast Monte Carlo simulation for radiotherapy dose calculation,” Phys. Med. Biol. 56, 7017 (2011).
40. X. Jia, H. Yan, L. Cervino, M. Folkerts, and S. B. Jiang, “A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections,” Med. Phys. 39, 73687378 (2012).
41. W. Chen, D. Kolditz, M. Beister, R. Bohle, and W. A. Kalender, “Fast on-site Monte Carlo tool for dose calculations in CT applications,” Med. Phys. 39, 29852996 (2012).

Data & Media loading...


Article metrics loading...



In AAPM Task Group 204, the size-specific dose estimate (SSDE) was developed by providing size adjustment factors which are applied to the Computed Tomography (CT) standardized dose metric, CTDI . However, that work focused on fixed tube current scans and did not specifically address tube current modulation (TCM) scans, which are currently the majority of clinical scans performed. The purpose of this study was to extend the SSDE concept to account for TCM by investigating the feasibility of using anatomic and organ specific regions of scanner output to improve accuracy of dose estimates.

Thirty-nine adult abdomen/pelvis and 32 chest scans from clinically indicated CT exams acquired on a multidetector CT using TCM were obtained with Institutional Review Board approval for generating voxelized models. Along with image data, raw projection data were obtained to extract TCM functions for use in Monte Carlo simulations. Patient size was calculated using the effective diameter described in TG 204. In addition, the scanner-reported CTDI (CTDI ) was obtained for each patient, which is based on the average tube current across the entire scan. For the abdomen/pelvis scans, liver, spleen, and kidneys were manually segmented from the patient datasets; for the chest scans, lungs and for female models only, glandular breast tissue were segmented. For each patient organ doses were estimated using Monte Carlo Methods. To investigate the utility of regional measures of scanner output, regional and organ anatomic boundaries were identified from image data and used to calculate regional and organ-specific average tube current values. From these regional and organ-specific averages, CTDI values, referred to as regional and organ-specific CTDI , were calculated for each patient. Using an approach similar to TG 204, all CTDI values were used to normalize simulated organ doses; and the ability of each normalized dose to correlate with patient size was investigated.

For all five organs, the correlations with patient size increased when organ doses were normalized by regional and organ-specific CTDI values. For example, when estimating dose to the liver, CTDI yielded a R value of 0.26, which improved to 0.77 and 0.86, when using the regional and organ-specific CTDI for abdomen and liver, respectively. For breast dose, the global CTDI yielded a R value of 0.08, which improved to 0.58 and 0.83, when using the regional and organ-specific CTDI for chest and breasts, respectively. The R values also increased once the thoracic models were separated for the analysis into females and males, indicating differences between genders in this region not explained by a simple measure of effective diameter.

This work demonstrated the utility of regional and organ-specific CTDI as normalization factors when using TCM. It was demonstrated that CTDI is not an effective normalization factor in TCM exams where attenuation (and therefore tube current) varies considerably throughout the scan, such as abdomen/pelvis and even thorax. These exams can be more accurately assessed for dose using regional CTDI descriptors that account for local variations in scanner output present when TCM is employed.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd