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.G. E. Pfahler, “A roentgen filter and a universal diaphragm and protecting screen,” Trans. Am. Roentgen Ray Soc. 217224 (1906).
2.J. W. Motz and M. Danos, “Image information content and patient exposure,” Med. Phys. 5(8), 822 (1978).
3.R. J. Jennings, R. J. Eastgate, M. P. Siedband, and D. L. Ergun, “Optimal x-ray spectra for screen-film mammography,” Med. Phys. 8(5), 629639 (1981).
4.D. R. Dance, A. Thilander Klang, M. Sandborg, C. L. Skinner, I. A. Castellano Smith, and G. Alm Carlsson, “Influence of anode/filter material and tube potential on contrast, signal-to-noise ratio and average absorbed dose in mammography: A Monte Carlo study,” Br. J. Radiol. 73(874), 10561067 (2000).
5.M. Åslund, B. Cederström, M. Lundqvist, and M. Danielsson, “Optimization of operating conditions in photon counting multi-slit mammography based on Si-strip detectors,” Proc. SPIE 6142, 61420A (2006).
6.R. Fahrig and M. J. Yaffe, “Optimization of spectral shape in digital mammography: Dependence on anode material, breast thickness, and lesion type,” Med. Phys. 21(9), 14731481 (1994).
7.A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28(4), 419437 (2001).
8.F. O. Bochud, J. F. Valley, F. R. Verdun, C. Hessler, and P. Schnyder, “Estimation of the noisy component of anatomical backgrounds,” Med. Phys. 26(7), 13651370 (1999).
9.W. Huda, K. Ogden, E. Scalzetti, D. Dance, and E. Bertrand, “How do lesion size and random noise affect detection performance in digital mammography? ,'' Acad. Radiol. 13, 13551366 (2006).
10.M. Ruschin, P. Timberg, M. Båth, B. Hemdal, T. Svahn, R. Saunders, E. Samei, I. Andersson, S. Mattson, D. Chakraborty, and A. Tingberg, “Dose dependence of mass and microcalcification detection in digital mammography: Free response human observer studies,” Med. Phys. 34(2), 400407 (2007).
11.E. Samei, R. S. Saunders, J. A. Baker, and D. M. Delong, “Digital mammography: Effects of reduced radiation dose on diagnostic performance,” Radiology 243(2), 396404 (2007).
12.S. Richard, J. H. Siewerdsen, D. A. Jaffray, D. J. Moseley, and B. Bakhtiar, “Generalized dqe anaysis of radiographic and dual-energy imaging using flat-panel detectors,” Med. Phys. 5, 13971413 (2005).
13.I. A. Cunningham, “Applied linear-systems theory,” in Handbook of Medical Imaging, Volume 1. Physics and Psychophysics (SPIE, Bellingham, 2000), chap. 2.
14.P. F. Sharp, C. E. Metz, R. F. Wagner, K. J. Myers, and A. E. Burgess, ”Medical imaging: The assessment of image quality,” International Commission on Radiological Units and Measurements, Bethesda, MD, ICRU Report No. 54, 1996.
15.C. E. Metz, R. F. Wagner, K. Doi, D. G. Brown, R. M. Nishikawa, and K. J. Myers, “Toward consensus on quantitative assessment of medical imaging-systems,” Med. Phys. 22, 10571061 (1995).
16.S. Richard and J. H. Siewerdsen, “Optimization of dual-energy imaging systems using generalized NEQ and imaging task,” Med. Phys. 34(1), 127139 (2007).
17.S. Richard and J. H. Siewerdsen, “Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images,” Med. Phys. 35, 50435053 (2008).
18.M. Åslund, B. Cederström, M. Lundqvist, and M. Danielsson, “Physical characterization of a scanning photon counting digital mammography system based on Si-strip detectors,” Med. Phys. 34(6), 19181925 (2007).
19.J. M. Boone, T. R. Fewell, and R. J. Jennings, “Molybdenum, rhodium, and tungsten anode spectral models using interpolating polynomials with application to mammography,” Med. Phys. 24(12), 18631874 (1997).
20.K. Cranley, B. Gilmore, G. Fogarty, and L. Desponds, ”Catalogue of diagnostic x-ray spectra and other data,” IPEM, York, Report No. 78, 1997.
21.M. J. Berger, J. H. Hubbell, S. M. Seltzer, J. S. Coursey, and D. S. Zucker, “XCOM: Photon cross section database,” National Institute of Standards and Technology, Gaithersburg, MD, 2005,
22.J. M. Boone, “Glandular breast dose for monoenergetic and high-energy x-ray beams: Monte Carlo assessment,” Radiology 203, 2337 (1999).
23.B. Zheng, Y. H. Chang, and D. Gur, “Adaptive computer-aided diagnosis scheme of digitized mammograms,” Acad. Radiol. 3(10), 806814 (1996).
24.E. Engstrom, I. Reiser, and R. Nishikawa, “Comparison of power spectra for tomosynthesis projections and reconstructed images,” Med. Phys. 36(5), 17531758 (2009).
25.J. J. Heine and R. P. Velthuizen, “Spectral analysis of full field digital mammography data,” Med. Phys. 29(5), 647661 (2002).
26.A. E. Burgess, “Mammographic structure: Data preparation and spatial statistics analysis,” Proc. SPIE 3661, 642653 (1999).
27.F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Express 4(1), 3343 (1999).
28.L. Chen, C. K. Abbey, A. Nosratieh, K. K. Lindfors, and J. M. Boone, “Anatomical complexity in breast parenchyma and its implications for optimal breast imaging strategies,” Med. Phys. 39(3), 14351441 (2012).
29.K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35(10), 46854694 (2008).
30.E. Fredenberg, M. Åslund, B. Cederström, M. Lundqvist, and M. Danielsson, “Observer model optimization of a spectral mammography system,” Proc. SPIE 7622, 762210 (2010).
31.E. Fredenberg, B. Svensson, M. Danielsson, B. Lazzari, and B. Cederström, “Optimization of mammography with respect to anatomical noise,” Proc. SPIE 7961, 796112 (2011).
32.E. Fredenberg, M. Lundqvist, B. Cederström, M. Åslund, and M. Danielsson, “Energy resolution of a photon-counting silicon strip detector,” Nucl. Instrum. Methods. Phys. Res., Sect. A 613(1), 156162 (2010).
33.P. C. Johns and M. J. Yaffe, “X-ray characterisation of normal and neoplastic breast tissues,” Phys. Med. Biol. 32(6), 675695 (1987).
34.G. R. Hammerstein, D. W. Miller, D. R. White, M. E. Masterson, H. Q. Woodard, and J. S. Laughlin, “Absorbed radiation dose in mammography,” Radiology 130(2), 485491 (1979).
35.G. J. Gang, D. J. Tward, J. Lee, and J. H. Siewerdsen, “Anatomical background and generalized detectability in tomosynthesis and cone-beam CT,” Med. Phys. 37(5), 19481965 (2010).
36.E. Fredenberg, “Noise-power spectrum,” 2012,
37.M. Åslund, B. Cederström, M. Lundqvist, and M. Danielsson, “Scatter rejection in multi-slit digital mammography,” Med. Phys. 33, 933940 (2006).
38.R. E. Alvarez and A. Macovski, “Energy-selective reconstructions in x-ray computerized tomography,” Phys. Med. Biol. 21, 733744 (1976).
39.A. R. Pineda and H. H. Barrett, “Figures of merit for detectors in digital radiography. II. Finite number of secondaries and structured backgrounds,” Med. Phys. 31(2), 359367 (2004).
40.J. T. Dobbins, “Effects of undersampling on the proper interpretation of modulation transfer-function, noise power spectra, and noise equivalent quanta of digital imaging-systems,” Med. Phys. 22(2), 171181 (1995).
41.M. Albert and A. D. A. Maidment, “Linear response theory for detectors consisting of discrete arrays,” Med. Phys. 27(10), 24172434 (2000).
42.P. Monnin, D. Gutierrez, S. Bulling, D. Guntern, and F. R. Verdun, “A comparison of the performance of digital mammography systems,” Med. Phys. 34(3), 906914 (2007).

Data & Media loading...


Article metrics loading...



Beam-quality optimization in digital mammography traditionally considers detection of a target obscured by quantum noise in a homogeneous background. This does not correspond well to the clinical imaging task because real mammographic images contain a complex superposition of anatomical structures, resulting in anatomical noise that may dominate over quantum noise. The purpose of this paper is to assess the influence on optimal beam quality in mammography when anatomical noise is taken into account.

The detectability of microcalcifications and masses was quantified using a theoretical ideal-observer model that included quantum noise as well as anatomical noise and a simplified model of a photon-counting mammography system. The outcome was experimentally verified using two types of simulated tissue phantoms.

The theoretical model showed that the detectability of tumors and microcalcifications behaves differently with respect to beam quality and dose. The results for small microcalcifications were similar to what traditional optimization methods yield, which is to be expected because quantum noise dominates over anatomical noise at high spatial frequencies. For larger tumors, however, low-frequency anatomical noise was the limiting factor. Because anatomical structure noise has similar energy dependence as tumor contrast, the optimal x-ray energy was found to be higher and the useful energy region was wider than traditional methods suggest. A simplified scalar model was able to capture this behavior using a fitted . The phantom measurements confirmed these theoretical results.

It was shown that since quantum noise constitutes only a small fraction of the noise, the dose could be reduced substantially without sacrificing tumor detectability. Furthermore, when anatomical noise is included, the tube voltage can be increased well beyond what is conventionally considered optimal and used clinically, without loss of image quality. However, no such conclusions can be drawn for the more complex mammographic imaging task as a whole.


Full text loading...


Access Key

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