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1. D. J. Brenner, E. J. Hall, “Computed tomography—An increasing source of radiation exposure,” N. Engl. J. Med. 357, 22772284 (2007).
2. A. Berrington de González, M. Mahesh, K. P. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomography scans performed in the United States in 2007,” Arch. Intern. Med. 169, 20712077 (2009).
3. R. Smith-Bindman, “Is computed tomography safe?,” N. Engl. J. Med. 363, 14 (2010).
4. R. F. Redberg, “Cancer risks and radiation exposure from computed tomographic scans: How can we be sure that the benefits outweight the risks?,” Arch. Intern. Med. 169, 20492050 (2009).
5. A. C. T. Martinsen, H. K. Saether, D. R. Olsen, P. A. Wolff, and P. Skaane, “Improved image quality of low-dose thoracic CT examinations with a new postprocessing software,” J. Appl. Clin. Med. Phys. 11, 250258 (2010), Available from:
6. International Commission on Radiation Units and Measurements, “Receiver operating characteristic analysis in medical imaging,” ICRU Report No. 79 (International Commission on Radiation Units and Measurements, Bethesda, MD, 2008).
7. M. S. Chesters, “Human visual perception and ROC methodology in medical imaging,” Phys. Med. Biol. 37, 14331476 (1992).
8. A. Thilander-Klang, K. Ledenius, J. Hansson, P. Sund, and M. Bath, “Evaluation of subjective assessment of the low-contrast visibility in constancy control of computed tomography,” Radiat. Prot. Dosimetry 139, 449454 (2010).
9. International Commission on Radiation Units and Measurements, “Medical imaging—The assessment of image quality,” ICRU Report No. 54 (International Commission on Radiation Units and Measurements, Bethesda, MD, 1996).
10. M. P. Eckstein, C. K. Abbey, and F. O. Bochud, “A practical guide to model observers for visual detection in synthetic and natural noisy images,” in Handbook of Medical Imaging. Physics and Psychophysics, Vol. 1, edited by J. Beutel, H. L. Kundel, and R. L. Van Metter (SPIE, 2000), pp. 595629.
11. E. H. Chao, T. L. Toth, N. B. Bromberg, E. C. Williams, S. H. Fox, and D. A. Carleton, “A statistical method of defining low contrast detectability,” Radiology 217, 162 (2000).
12. F. R. Verdun, A. Denys, P. S. Valley, R. A. Meuli, “Detection of low-contrast objects: Experimental comparison of single-and multi-detector row CT,” Radiology 223, 426431 (2002).
13. T. Ishida, S. Tsukagoshi, K. Kondo, K. Kainuma, M. Okumura, and T. Sasaki, “Evaluation of dose efficiency index compared to receiver operating characteristics for assessing CT low-contrast performance,” Proc. SPIE. 5368, 527533 (2004).
14. S. J. Riederer, N. J. Pelc, and D. A. Chesler, “The noise power spectrum in computed x-ray tomography,” Phys. Med. Biol. 23, 446454 (1978).
15. R. Brooks and G. Di Chiro, “Statistical limitations in X-ray reconstructive tomography,” Med. Phys. 3, 237240 (1976).
16. Imaging Performance and Assessment of CT scanners, “32 to 64 slice CT scanner comparison report version 14,” ImPACT Report 06013 (NHS Purchasing and supply agency, NHS PASA, 2005).
17. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419437 (2001).
18. I. Reiser and R. M. Nishikawa, “Identification of simulated microcalcifications in white noise and mammographic backgrounds,” Med. Phys. 33, 29052911 (2006).
19. B. M. Verbist, R. M. S. Joemai, W. M. Teeuwisse, W. J. H. Veldkamp, J. Geleijns, and J. H. M. Frijns, “Evaluation of 4 multisection CT systems in postoperative imaging of a cochlear implant: A human cadaver and phantom study,” AJNR 29, 13821388 (2008).
20. A. Wunderlich and F. Noo, “Estimation of channelized hotelling observer performance with known class means or known difference of class means,” IEEE Trans. Med. Imag. 28, 11981207 (2009).
21. W. J. H. Veldkamp, L. J. M. Kroft, J. P. Van Delft, and J. Geleijns, “A technique for simulating the effect of dose reduction on image quality in digital chest radiography,” J. Digit. Imag. 2, 11141125 (2009).
22. N. Karssemeijer and M. A. O. Thijssen, “Determination of contrast-detail curves of mammography systems by automated image analysis,” in Digital Mammography, edited by K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt (Elsevier, Amsterdam, 1996), pp. 155160.
23. W. J. H. Veldkamp, M. A. O. Thijssen, and N. Karssemeijer, “The value of scatter removal by a grid in full field digital mammography,” Med. Phys. 30, 17121718 (2003).
24. 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 (IEC, Geneva, Switzerland, 2002).
25. R. Fahrig, R. Dixon, T. Payne, and R. L. Morin, “Dose and image quality for a cone-beam C-arm CT system,” Med. Phys. 33, 45414550 (2006).
26. A. Ganguly, S. Yoon, and R. Fahrig, “Dose and detectability for a cone-beam C-arm system revisited,” Med. Phys. 37, 22642268 (2010).
27. E. Samei, A. Badano, D. Chakraborty, K. Compton, C. Cornelius, K. Corrigan, M.J. Flynn, B. Hemminger, N. Hangiandreou, J. Johnson, D. M. Moxley-Stevens, W. Pavlicek, H. Roehrig, L. Rutz, J. Shepard, R. A. Uzenoff, J. Wang, and C. E. Willis, “Assessment of display performance for medical imaging systems: Executive summary of AAPM TG18 report,” Med. Phys. 32, 12051225 (2005).
28. R. F. Woolson, Statistical Methods of Analysis of Biomedical Data, 1st ed. (Wiley, New York, 1987), pp. 172187.
29. L. M. Morán, R. Rodríguez, A. Calzado, A. Turrero, A. Arenas, A. Cuevas, B. García-Castaño, N. Gómez, and P. Morán, “Image quality and dose evaluation in spiral chest CT examinations of patients with lung carcinoma,” Br. J. Radiol. 77, 839846 (2004).
30. A. E. Burgess, “Statistically defined backgrounds: Performance of a modified non-prewhitening observer model,” J. Opt. Soc. Am. A 11, 12371242 (1994).
31. A. Wunderlich and F. Noo, “Image covariance and lesion detectability in direct fan-beam x-ray computed tomography,” Phys. Med. Biol. 53, 24712493 (2008).

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Low contrast sensitivity of CT scanners is regularly assessed by subjective scoring of low contrast detectability within phantom CTimages. Since in these phantoms low contrast objects are arranged in known fixed patterns, subjective rating of low contrastvisibility might be biased. The purpose of this study was to develop and validate a software for automated objective low contrast detectability based on a model observer.


Images of the low contrast module of the Catphan 600 phantom were used for the evaluation of the software. This module contains two subregions: the supraslice region with three groups of low contrast objects (each consisting of nine circular objects with diameter 2–15 mm and contrast 0.3, 0.5, and 1.0%, respectively) and the subslice region with three groups of four circular objects each (diameter 3–9 mm; contrast 1.0%). The software method offered automated determination of low contrast detectability using a NPWE (nonprewhitening matched filter with an eye filter) model observer for the supraslice region. The model observer correlated templates of the low contrast objects with the acquired images of the Catphan phantom and a discrimination indexd′ was calculated. This index was transformed into a proportion correct (PC) value. In the two-alternative forced choice (2-AFC) experiments used in this study, a PC ≥ 75% was proposed as a threshold to decide whether objects were visible. As a proof of concept, influence of kVp (between 80 and 135 kV), mAs (25–200 mAs range) and reconstruction filter (four filters, two soft and two sharp) on low contrast detectability was investigated. To validate the outcome of the software in a qualitative way, a human observer study was performed.


The expected influence of kV, mAs and reconstruction filter on image quality are consistent with the results of the proposed automated model. Higher values ford′ (or PC) are found with increasing mAs or kV values and for the soft reconstruction filters. For the highest contrast group (1%), PC values were fairly above 75% for all object diameters >2 mm, for all conditions. For the 0.5% contrast group, the same behavior was observed for object diameters >3 mm for all conditions. For the 0.3% contrast group, PC values were higher than 75% for object diameters >6 mm except for the series acquired at the lowest dose (25 mAs), which gave lower PC values. In the human observer study similar trends were found.


We have developed an automated method to objectively investigate image quality using the NPWE model in combination with images of the Catphan phantom low contrast module. As a first step, low contrast detectability as a function of both acquisition and reconstruction parameter settings was successfully investigated with the software. In future work, this method could play a role in image reconstruction algorithms evaluation, dose reduction strategies or novel CT technologies, and other model observers may be implemented as well.


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