Expanding the Concept of Diagnostic Reference Levels to Noise and Dose Reference Levels in CT
Francesco Ria1,2, Joseph T. Davis3, Justin B. Solomon1,2,4, Joshua M. Wilson1,2,4, Taylor B. Smith1,4, Donald P. Frush3,4 and Ehsan Samei1,2,3,4 Show less
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Citation: American Journal of Roentgenology: 1-6. 10.2214/AJR.18.21030
AbstractFull TextReferencesPDFPDF PlusAdd to FavoritesPermissionsDownload Citation
ABSTRACT :
OBJECTIVE. Diagnostic reference levels were developed as guidance for radiation dose in medical imaging and, by inference, diagnostic quality. The objective of this work was to expand the concept of diagnostic reference levels to explicitly include noise of CT examinations to simultaneously target both dose and quality through corresponding reference values.
MATERIALS AND METHODS. The study consisted of 2851 adult CT examinations performed with scanners from two manufacturers and two clinical protocols: abdominopelvic CT with IV contrast administration and chest CT without IV contrast administration. An institutional informatics system was used to automatically extract protocol type, patient diameter, volume CT dose index, and noise magnitude from images. The data were divided into five reference patient size ranges. Noise reference level, noise reference range, dose reference level, and dose reference range were defined for each size range.
RESULTS. The data exhibited strong dependence between dose and patient size, weak dependence between noise and patient size, and different trends for different manufacturers with differing strategies for tube current modulation. The results suggest size-based reference intervals and levels for noise and dose (e.g., noise reference level and noise reference range of 11.5–12.9 HU and 11.0–14.0 HU for chest CT and 10.1–12.1 HU and 9.4–13.7 HU for abdominopelvic CT examinations) that can be targeted to improve clinical performance consistency.
CONCLUSION. New reference levels and ranges, which simultaneously consider image noise and radiation dose information across wide patient populations, were defined and determined for two clinical protocols. The methods of new quantitative constraints may provide unique and useful information about the goal of managing the variability of image quality and dose in clinical CT examinations.
Keywords: CT performance, diagnostic reference levels, image noise, patient population, radiation dose
Acknowledgment
Previous sectionNext section
We thank Aiping Ding for help in various aspects of developing this study.
References
Previous section
1. [No authors listed]. The 2007 recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP 2007; 37:1–332 [Crossref] [Google Scholar]
2. [No authors listed]. Radiological protection in medicine. ICRP publication 105. Ann ICRP 2007; 37:1–63 [Google Scholar]
3. [No authors listed]. Diagnostic reference levels in medical imaging. ICRP publication 135. Ann ICRP 2017; 46:1–144 [Crossref] [Google Scholar]
4. International Atomic Energy Agency website. Diagnostic reference levels (DRLs). www.iaea.org/resources/rpop/health-professionals/radiology/diagnostic-reference-levels. Published 2017. Accessed April 16, 2019 [Google Scholar]
5. Valentin J; International Commission on Radiation Protection. Managing patient dose in multi-detector computed tomography (MDCT): ICRP publication 102. Ann ICRP 2007; 37:1–79 [Google Scholar]
6. Abadi E, Sanders J, Samei E. Patient-specific quantification of image quality: an automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images. Med Phys 2017; 44:4736–4746 [Crossref] [Medline] [Google Scholar]
7. Christianson O, Winslow J, Frush DP, Samei E. Automated technique to measure noise in clinical CT examinations. AJR 2015; 205:[web]W93–W99 [Abstract] [Google Scholar]
8. Sanders J, Hurwitz L, Samei E. Patient-specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images. Med Phys 2016; 43:5330–5338 [Crossref] [Medline] [Google Scholar]
9. Christianson O, Li X, Frush D, Samei E. Automated size-specific CT dose monitoring program: assessing variability in CT dose. Med Phys 2012; 39:7131–7139 [Crossref] [Medline] [Google Scholar]
10. Ria F, Wilson JM, Zhang Y, Samei E. Image noise and dose performance across a clinical population: patient size adaptation as a metric of CT performance. Med Phys 2017; 44:2141–2147 [Crossref] [Medline] [Google Scholar]
11. Boone JM, Strauss KJ, Cody DD, McCollough CH, McNitt-Gray MF, Toth TL. Size specific dose estimates (SSDE) in pediatric and adult body CT examinations. AAPM report no. 204. College Park, MD: American Association of Physicists in Medicine, 2011 [Google Scholar]
12. Solomon JB, Li X, Samei E. Relating noise to image quality indicators in CT examinations with tube current modulation. AJR 2013; 200:592–600 [Abstract] [Google Scholar]
13. Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL. U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology 2017; 284:120–133 [Crossref] [Medline] [Google Scholar]
14. Gies M, Kalender WA, Wolf H, Suess C. Dose reduction in CT by anatomically adapted tube current modulation. Part I. Simulation studies. Med Phys 1999; 26:2235–2247 [Crossref] [Medline] [Google Scholar]
15. Kalender WA, Wolf H, Suess C. Dose reduction in CT by anatomically adapted tube current modulation. Part II. Phantom measurements. Med Phys 1999; 26:2248–2253 [Crossref] [Medline] [Google Scholar]
16. Kalra MK, Maher MM, Toth TL, et al. Techniques and applications of automatic tube current modulation for CT. Radiology 2004; 233:649–657 [Crossref] [Medline] [Google Scholar]
17. Samei E, Järvinen H, Kortesniemi M, et al. Medical imaging dose optimisation from ground up: expert opinion of an international summit. J Radiol Prot 2018; 38:967–989 [Crossref] [Medline] [Google Scholar]
18. Dougeni E, Faulkner K, Panayiotakis G. A review of patient dose and optimisation methods in adult and paediatric CT scanning. Eur J Radiol 2012; 81:e665–e683 [Crossref] [Medline] [Google Scholar]
19. Ryu YJ, Choi YH, Cheon JE, Ha S, Kim WS, Kim IO. Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom. Pediatr Radiol 2016; 46:303–315 [Crossref] [Medline] [Google Scholar]
20. Morel B, Bouëtté A, Lévy P, et al. Optimization of the pediatric head computed tomography scan image quality: reducing dose with an automatic tube potential selection in infants. J Neuroradiol 2016; 43:398–403 [Crossref] [Medline] [Google Scholar]
21. Schindera ST, Zaehringer C, D'Errico L, et al. Systematic radiation dose optimization of abdominal dual-energy CT on a second-generation dual-source CT scanner: assessment of the accuracy of iodine uptake measurement and image quality in an in vitro and in vivo investigations. Abdom Radiol (NY) 2017; 42:2562–2570 [Crossref] [Medline] [Google Scholar]
22. American Association of Physicists in Medicine. Report no. AAPM 220: use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT. College Park, MD: American Association of Physicists in Medicine, 2014 [Google Scholar]
23. Tian X, Segars WP, Dixon RL, Samei E. Convolution-based estimation of organ dose in tube current modulated CT. Phys Med Biol 2016; 61:3935–3954 [Crossref] [Medline] [Google Scholar]
24. Tian X, Li X, Segars WP, Frush DP, Samei E. Prospective estimation of organ dose in CT under tube current modulation. Med Phys 2015; 42:1575–1585 [Crossref] [Medline] [Google Scholar]
25. Fu W, Segars WP, Abadi E, Sharma S, Kapadia AJ, Samei E. From patient-informed to patient-specific organ dose estimation in clinical computed tomography. Proc SPIE 2018; 10573:1–6 [Google Scholar]
26. Samei E, Robins M, Chen B, Agasthya G. Estimability index for volume quantification of homogeneous spherical lesions in computed tomography. J Med Imaging (Bellingham) 2018; 5:031404 [Medline] [Google Scholar]
27. Smith TB, Solomon J, Samei E. Estimating detectability index in vivo: development and validation of an automated methodology. J Med Imaging (Bellingham) 2018; 5:031403 [Medline] [Google Scholar]
Address correspondence to F. Ria (francesco.ria@duke.edu).
Read More: https://www.ajronline.org/doi/abs/10.2214/AJR.18.21030
Francesco Ria1,2, Joseph T. Davis3, Justin B. Solomon1,2,4, Joshua M. Wilson1,2,4, Taylor B. Smith1,4, Donald P. Frush3,4 and Ehsan Samei1,2,3,4 Show less
Share Share
+ Affiliations:
Citation: American Journal of Roentgenology: 1-6. 10.2214/AJR.18.21030
AbstractFull TextReferencesPDFPDF PlusAdd to FavoritesPermissionsDownload Citation
ABSTRACT :
OBJECTIVE. Diagnostic reference levels were developed as guidance for radiation dose in medical imaging and, by inference, diagnostic quality. The objective of this work was to expand the concept of diagnostic reference levels to explicitly include noise of CT examinations to simultaneously target both dose and quality through corresponding reference values.
MATERIALS AND METHODS. The study consisted of 2851 adult CT examinations performed with scanners from two manufacturers and two clinical protocols: abdominopelvic CT with IV contrast administration and chest CT without IV contrast administration. An institutional informatics system was used to automatically extract protocol type, patient diameter, volume CT dose index, and noise magnitude from images. The data were divided into five reference patient size ranges. Noise reference level, noise reference range, dose reference level, and dose reference range were defined for each size range.
RESULTS. The data exhibited strong dependence between dose and patient size, weak dependence between noise and patient size, and different trends for different manufacturers with differing strategies for tube current modulation. The results suggest size-based reference intervals and levels for noise and dose (e.g., noise reference level and noise reference range of 11.5–12.9 HU and 11.0–14.0 HU for chest CT and 10.1–12.1 HU and 9.4–13.7 HU for abdominopelvic CT examinations) that can be targeted to improve clinical performance consistency.
CONCLUSION. New reference levels and ranges, which simultaneously consider image noise and radiation dose information across wide patient populations, were defined and determined for two clinical protocols. The methods of new quantitative constraints may provide unique and useful information about the goal of managing the variability of image quality and dose in clinical CT examinations.
Keywords: CT performance, diagnostic reference levels, image noise, patient population, radiation dose
Acknowledgment
Previous sectionNext section
We thank Aiping Ding for help in various aspects of developing this study.
References
Previous section
1. [No authors listed]. The 2007 recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP 2007; 37:1–332 [Crossref] [Google Scholar]
2. [No authors listed]. Radiological protection in medicine. ICRP publication 105. Ann ICRP 2007; 37:1–63 [Google Scholar]
3. [No authors listed]. Diagnostic reference levels in medical imaging. ICRP publication 135. Ann ICRP 2017; 46:1–144 [Crossref] [Google Scholar]
4. International Atomic Energy Agency website. Diagnostic reference levels (DRLs). www.iaea.org/resources/rpop/health-professionals/radiology/diagnostic-reference-levels. Published 2017. Accessed April 16, 2019 [Google Scholar]
5. Valentin J; International Commission on Radiation Protection. Managing patient dose in multi-detector computed tomography (MDCT): ICRP publication 102. Ann ICRP 2007; 37:1–79 [Google Scholar]
6. Abadi E, Sanders J, Samei E. Patient-specific quantification of image quality: an automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images. Med Phys 2017; 44:4736–4746 [Crossref] [Medline] [Google Scholar]
7. Christianson O, Winslow J, Frush DP, Samei E. Automated technique to measure noise in clinical CT examinations. AJR 2015; 205:[web]W93–W99 [Abstract] [Google Scholar]
8. Sanders J, Hurwitz L, Samei E. Patient-specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images. Med Phys 2016; 43:5330–5338 [Crossref] [Medline] [Google Scholar]
9. Christianson O, Li X, Frush D, Samei E. Automated size-specific CT dose monitoring program: assessing variability in CT dose. Med Phys 2012; 39:7131–7139 [Crossref] [Medline] [Google Scholar]
10. Ria F, Wilson JM, Zhang Y, Samei E. Image noise and dose performance across a clinical population: patient size adaptation as a metric of CT performance. Med Phys 2017; 44:2141–2147 [Crossref] [Medline] [Google Scholar]
11. Boone JM, Strauss KJ, Cody DD, McCollough CH, McNitt-Gray MF, Toth TL. Size specific dose estimates (SSDE) in pediatric and adult body CT examinations. AAPM report no. 204. College Park, MD: American Association of Physicists in Medicine, 2011 [Google Scholar]
12. Solomon JB, Li X, Samei E. Relating noise to image quality indicators in CT examinations with tube current modulation. AJR 2013; 200:592–600 [Abstract] [Google Scholar]
13. Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL. U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology 2017; 284:120–133 [Crossref] [Medline] [Google Scholar]
14. Gies M, Kalender WA, Wolf H, Suess C. Dose reduction in CT by anatomically adapted tube current modulation. Part I. Simulation studies. Med Phys 1999; 26:2235–2247 [Crossref] [Medline] [Google Scholar]
15. Kalender WA, Wolf H, Suess C. Dose reduction in CT by anatomically adapted tube current modulation. Part II. Phantom measurements. Med Phys 1999; 26:2248–2253 [Crossref] [Medline] [Google Scholar]
16. Kalra MK, Maher MM, Toth TL, et al. Techniques and applications of automatic tube current modulation for CT. Radiology 2004; 233:649–657 [Crossref] [Medline] [Google Scholar]
17. Samei E, Järvinen H, Kortesniemi M, et al. Medical imaging dose optimisation from ground up: expert opinion of an international summit. J Radiol Prot 2018; 38:967–989 [Crossref] [Medline] [Google Scholar]
18. Dougeni E, Faulkner K, Panayiotakis G. A review of patient dose and optimisation methods in adult and paediatric CT scanning. Eur J Radiol 2012; 81:e665–e683 [Crossref] [Medline] [Google Scholar]
19. Ryu YJ, Choi YH, Cheon JE, Ha S, Kim WS, Kim IO. Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom. Pediatr Radiol 2016; 46:303–315 [Crossref] [Medline] [Google Scholar]
20. Morel B, Bouëtté A, Lévy P, et al. Optimization of the pediatric head computed tomography scan image quality: reducing dose with an automatic tube potential selection in infants. J Neuroradiol 2016; 43:398–403 [Crossref] [Medline] [Google Scholar]
21. Schindera ST, Zaehringer C, D'Errico L, et al. Systematic radiation dose optimization of abdominal dual-energy CT on a second-generation dual-source CT scanner: assessment of the accuracy of iodine uptake measurement and image quality in an in vitro and in vivo investigations. Abdom Radiol (NY) 2017; 42:2562–2570 [Crossref] [Medline] [Google Scholar]
22. American Association of Physicists in Medicine. Report no. AAPM 220: use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT. College Park, MD: American Association of Physicists in Medicine, 2014 [Google Scholar]
23. Tian X, Segars WP, Dixon RL, Samei E. Convolution-based estimation of organ dose in tube current modulated CT. Phys Med Biol 2016; 61:3935–3954 [Crossref] [Medline] [Google Scholar]
24. Tian X, Li X, Segars WP, Frush DP, Samei E. Prospective estimation of organ dose in CT under tube current modulation. Med Phys 2015; 42:1575–1585 [Crossref] [Medline] [Google Scholar]
25. Fu W, Segars WP, Abadi E, Sharma S, Kapadia AJ, Samei E. From patient-informed to patient-specific organ dose estimation in clinical computed tomography. Proc SPIE 2018; 10573:1–6 [Google Scholar]
26. Samei E, Robins M, Chen B, Agasthya G. Estimability index for volume quantification of homogeneous spherical lesions in computed tomography. J Med Imaging (Bellingham) 2018; 5:031404 [Medline] [Google Scholar]
27. Smith TB, Solomon J, Samei E. Estimating detectability index in vivo: development and validation of an automated methodology. J Med Imaging (Bellingham) 2018; 5:031403 [Medline] [Google Scholar]
Address correspondence to F. Ria (francesco.ria@duke.edu).
Read More: https://www.ajronline.org/doi/abs/10.2214/AJR.18.21030
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