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1. Class II Special Controls Guidance Document: Full Field Digital Mammography System (U.S. Department of Health and Human Services, Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, 2012).
2. J. M. Sandrik and R. F. Wagner, “Absolute measures of physical image quality: Measurement and application to radiographic magnification,” Med. Phys. 9, 540549 (1982).
3. C. E. Metz and K. Doi, “Transfer function analysis of radiographic imaging systems,” Phys. Med. Biol. 24, 10791106 (1979).
4. M. L. Giger and K. Doi, “Investigation of basic imaging properties of digital radiography. Part 1: Modulation transfer function,” Med. Phys. 11, 287295 (1984).
5. R. F. Wagner, “Toward a unified view of radiological imaging systems. Part II: Noisy images,” Med. Phys. 4, 279296 (1977).
6. R. F. Wagner, “Fast Fourier digital quantum mottle analysis with application to rare earth intensifying screen systems,” Med. Phys. 4, 157162 (1977).
7. R. F. Wagner, G. T. Barnes, and B. S. Askins, “Effect of reduced scatter on radiographics information content and patient exposure: A quantitative demonstration,” Med. Phys. 7, 1318 (1980).
8. E. P. Muntz, “Analysis of the significance of scattered radiation in reduced dose mammography, including magnification effects, scatter suppression, and focal spot and detector blurring,” Med. Phys. 6, 110117 (1979).
9. J. M. Boone, B. A. Arnold, and J. A. Seibert, “Characterization of the point spread function and modulation transfer function of scattered radiation using a digital imaging system,” Med. Phys. 13(2), 254256 (1986).
10. V. N. Cooper III, J. M. Boone, J. Anthony Seibert, and C. J. Pellot-Barakat, “An edge spread technique for measurement of the scatter-to-primary ratio in mammograph,” Med. Phys. 27(5), 845853 (2000).
11. K. Doi and K. Rossman, “The effect of radiographic magnification on blood vessel imaging with various screen-film systems,” Med. Phys. 1(5), 257261 (1974).
12. C. C. Shaw, X. Liu, M. Lemacks, J. X. Rong, and G. J. Whitman, “Optimization of MTF and DQE in magnification radiography: A theoretical analysis,” Proc. SPIE 3977, 466475 (2000).
13. I. S. Kyprianou, “A method for total x-ray imaging system evaluation application to a microangiographic detector for neurovascular procedures,” Ph.D. thesis (University of New York at Buffalo, NY, 2004).
14. I. S. Kyprianou, S. Rudin, D. R. Bednarek, and K. R. Hoffmann, “Generalizing the MTF and DQE to include x-ray scatter and focal spot unsharpness: Application to a new microangiographic system,” Med. Phys. 32(2), 613626 (2005).
15. I. S. Kyprianou, A. Ganguly, S. Rudin, D. R. Bednarek, B. D. Gallas, and K. J. Myers, “Efficiency of the human observer compared to an ideal observer based on a generalized NEQ which incorporates scatter and geometric unsharpness: Evaluation with a 2AFC experiment,” Proc. SPIE 5749, 251263 (2005).
16. E. Samei, N. T. Ranger, A. MacKenzie, I. D. Honey, J. T. Dobbins III, and C. E. Ravin, “Detector or system? Extending the concept of detective quantum efficiency to characterize the performance of digital radiographic imaging systems,” Radiology 249, 926937 (2008).
17. ICRU, “Medical imaging: The assessment of image quality,” Report No. 54 (International Commission of Radiation Units and Measurements, Bethesda, MD, 1996).
18. H. H. Barrett, J. Yao, J. P. Rolland, and K. J. Myers, “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. U.S.A. 90(21), 97589765 (1993).
19. R. D. Fiete, H. H. Barrett, W. E. Smith, and K. J. Myers, “Hotelling trace criterion and its correlation with human-observer performance,” J. Opt. Soc. Am. A 4(5), 945953 (1987).
20. B. D. Gallas, “Variance of the channelized-Hotelling observer from a finite number of trainers and testers,” Proc. SPIE 5034, 100111 (2003).
21. R. M. Gagne, B. D. Gallas, and K. J. Myers, “Toward objective and quantitative evaluation of imaging systems using images of phantoms,” Med. Phys. 33(1), 8395 (2006).
22. I. S. Kyprianou, B. Gallas, A. Badano, S. Park, H. Liu, and K. J. Myers, “Noise and signal detection in digital x-ray detectors using the spatial definition of SNR,” Proc. SPIE 7258, 725819 (2009).
23. H. Liu, I. S. Kyprianou, A. Badano, K. J. Myers, R. J. Jennings, S. Park, R. V. Kaczmarek, and K. Chakrabarti, “SKE/BKE task-based methodology for calculating Hotelling observer SNR in mammography,” Proc. SPIE 7258, 72581D (2009).
24. P. Monnin, N. W. Marshall, H. Bosmans, F. O. Bochud, and F. R. Verdun, “Image quality assessment in digital mammography: Part II. NPWE as a validated alternative for contrast detail analysis,” Phys. Med. Biol. 56, 42214238 (2011).
25. J. E. Gray and J. A. Princehorn, “HTC grids improve mammography contrast,” White Paper W-BI-HTC(9/04), 2004.
26. R. Visser and N. Karssemeijer, “Manual CDCOM version 1.5: Software for automated readout of CDMAM 3.4 images,” 2007.
27. D. R. Dance, C. L. Skinner, K. C. Young, J. R. Beckett, and C. J. Kotre, “Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol,” Phys. Med. Biol. 45, 32253240 (2000).
28. D. R. Dance, “Monte Carlo calculation of conversion factors for the estimation of mean glandular breast dose,” Phys. Med. Biol. 35(9), 12111219 (1990).
29. I. S. Kyprianou, A. Badano, B. D. Gallas, and K. J. Myers, “Singular value description of a digital radiographic detector: Theory and measurements,” Med. Phys. 35(10), 47444756 (2008).
30. I. A. Brezovich and G. T. Barnes, “A new type of grid,” Med. Phys. 4(5), 451453 (1977).
31.“International Standard – Medical electrical equipment: Characteristics of digital x-ray imaging devices,” IEC 62220-1 (International Electrotechnical Commission, Geneva, Switzerland, 2003), p. 18.
32. A. E. Burgess, “Comparison of receiver operating characteristic and forced choice observer performance measurement methods,” Med. Phys. 22(5), 643655 (1995).
33. K. C. Young, A. Alsager, J. M. Oduko, H. Bosmans, B. Verbrugge, T. Geertse, and R. V. Engen, “Evaluation of software for reading images of the CDMAM test object to assess digital mammography systems,” Proc. SPIE 6913, 69131C (2008).
34. K. C. Young, J. J. H. Cook, J. M. Oduko, and H. Bosmans, “Comparison of software and human observers in reading images of the CDMAM test object to assess digital mammography systems,” Proc. SPIE 6142, 614206 (2006).
35. S. Rivetti, N. Lanconelli, R. Campanini, M. Bertolini, G. Borasi, A. Nitrosi, C. Danielli, L. Angelini, and S. Maggi, “Comparison of different commercial FFDM units by means of physical characterization and contrast-detail analysis,” Med. Phys. 33(11), 41984209 (2006).
36. M. J. Tapiovaara and R. Wagner, “SNR and DQE analysis of broad spectrum X-ray imaging,” Phys. Med. Biol. 30(6), 519529 (1985).
37. A. Badano, I. S. Kyprianou, M. Freed, R. J. Jennings, and J. Sempau, “Effect of oblique x-ray incidence in flat-panel computed tomography of the breast,” IEEE Trans. Med. Imaging 28(4), 696709 (2009).
38. A. Badano, I. S. Kyprianou, R. J. Jennings, and J. Sempau, “Anisotropic imaging performance in breast tomosynthesis,” Med. Phys. 34(11), 40764091 (2007).
39. A. Badano, I. S. Kyprianou, and J. Sempau, “Anisotropic imaging performance in indirect x-ray imaging detectors,” Med. Phys. 33(8), 26982713 (2006).
40. A. E. Burgess, “The Rose model, revisited,” J. Opt. Soc. Am. 16(3), 633646 (1999).
41. H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley Interscience, New Jersey, 2004).

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The purpose of this work is to evaluate the performance of the image acquisition chain of clinical full field digital mammography (FFDM) systems by quantifying their image quality, and how well the desired information is captured by the images.

The authors present a practical methodology to evaluate FFDM using the task specific system-model-based Fourier Hotelling observer (SMFHO) signal to noise ratio (SNR), which evaluates the signal and noise transfer characteristics of FFDM systems in the presence of a uniform polymethyl methacrylate phantom that models the attenuation of a 6 cm thick 20/80 breast (20% glandular/80% adipose). The authors model the system performance using the generalized modulation transfer function, which accounts for scatter blur and focal spot unsharpness, and the generalized noise power spectrum, both estimated with the phantom placed in the field of view. Using the system model, the authors were able to estimate system detectability for a series of simulated disk signals with various diameters and thicknesses, quantified by a SMFHO SNR map. Contrast-detail (CD) curves were generated from the SNR map and adjusted using an estimate of the human observer efficiency, without performing time-consuming human reader studies. Using the SMFHO method the authors compared two FFDM systems, the GE Senographe DS and Hologic Selenia FFDM systems, which use indirect and direct detectors, respectively.

Even though the two FFDM systems have different resolutions, noise properties, detector technologies, and antiscatter grids, the authors found no significant difference between them in terms of detectability for a given signal detection task. The authors also compared the performance between the two image acquisition modes (fine view and standard) of the GE Senographe DS system, and concluded that there is no significant difference when evaluated by the SMFHO. The estimated human observer efficiency was 30 ± 5% when compared to the SMFHO. The results showed good agreement when compared to other model observers as well as previously published human observer data.

This method generates CD curves from the SMFHO SNR that can be used as figures of merit for evaluating the image acquisition performance of clinical FFDM systems. It provides a way of creating an empirical model of the FFDM system that accounts for patient scatter, focal spot unsharpness, and detector blur. With the use of simulated signals, this method can predict system performance for a signal known exactly/background known exactly detection task with a limited number of images, therefore, it can be readily applied in a clinical environment.


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