Abstract

We investigate the effect of anatomical noise on the detectability of cone beam CT (CBCT) images with different slice directions, slice thicknesses, and volume glandular fractions (VGFs). Anatomical noise is generated using a power law spectrum of breast anatomy, and spherical objects with diameters from 1mm to 11mm are used as breast masses. CBCT projection images are simulated and reconstructed using the FDK algorithm. A channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels is used to evaluate detectability for the signal-known-exactly (SKE) binary detection task. Detectability is calculated for various slice thicknesses in the transverse and longitudinal planes for 15%, 30% and 60% VGFs. The optimal slice thicknesses that maximize the detectability of the objects are determined. The results show that the β value increases as the slice thickness increases, but that thicker slices yield higher detectability in the transverse and longitudinal planes, except for the case of a 1mm diameter spherical object. It is also shown that the longitudinal plane with a 0.1mm slice thickness provides higher detectability than the transverse plane, despite its higher β value. With optimal slice thicknesses, the longitudinal plane exhibits better detectability for all VGFs and spherical objects.

© 2016 Optical Society of America

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References

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  2. K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
    [Crossref] [PubMed]
  3. I. Reiser and R. M. Nishikawa, “Task-based assessment of breast tomosynthesis: Effect of acquisition parameters and quantum noisea),” Med. Phys. 37, 1591–1600 (2010).
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  5. A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
    [Crossref] [PubMed]
  6. A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A. 24, B52–B60 (2007).
    [Crossref]
  7. L. Chen, J. M. Boone, A. Nosratieh, and C. K. Abbey, “NPS comparison of anatomical noise characteristics in mammography, tomosynthesis, and breast CT images using power law metrics,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2011), pp. 79610F.
    [Crossref]
  8. 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, 1435–1441 (2012).
    [Crossref] [PubMed]
  9. X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
    [Crossref] [PubMed]
  10. L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
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    [Crossref] [PubMed]
  17. B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A. 20, 1725–1738 (2003).
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    [Crossref] [PubMed]
  20. H. H. Barrett, C. K. Abbey, B. D. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in “Medical Imaging’98,” (International Society for Optics and Photonics, 1998), pp. 27–43.
  21. C. K. Abbey and H. H. Barrett, “Human-and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A. 18, 473–488 (2001).
    [Crossref]
  22. Z. J. Cao and B. M. W. Tsui, “A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT,” IEEE. Trans. Nucl. Sci. 40, 280–287 (1993).
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    [Crossref]
  26. P. C. Johns and M. J. Yaffe, “X-ray characterisation of normal and neoplastic breast tissues,” Phys. Med. Biol. 32, 675–695 (1987).
    [Crossref] [PubMed]
  27. R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12, 252–255 (1985).
    [Crossref] [PubMed]
  28. S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
    [Crossref]
  29. C. Lee, J. Baek, and S. Park, “Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2016), pp. 97870V.
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    [Crossref] [PubMed]
  31. S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
    [Crossref]
  32. L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
    [Crossref]
  33. P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38, 3165–3176 (2011).
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    [Crossref] [PubMed]
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    [Crossref] [PubMed]

2016 (1)

M. Han, C. Lee, S. Park, and J. Baek, “Investigation on slice direction dependent detectability of volumetric cone beam CT images,” Opt. Express. 24, 3749–3764 (2016).
[Crossref] [PubMed]

2015 (1)

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

2013 (4)

L. Chen, C. K. Abbey, and J. M. Boone, “Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities,” Phys. Med. Biol. 58, 1663–1681 (2013).
[Crossref] [PubMed]

X. He and S. Park, “Model observers in medical imaging research,” Theranostics 3, 774–786 (2013).
[Crossref] [PubMed]

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

S. Vedantham, L. Shi, S. J. Glick, and A. Karellas, “Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT,” Med. Phys. 40, 011901 (2013).
[Crossref] [PubMed]

2012 (2)

N. J. Packard, C. K. Abbey, K. Yang, and J. M. Boone, “Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions,” Med. Phys. 39, 1818–1830 (2012).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

2011 (4)

J. Baek and N. J. Pelc, “Local and global 3D noise power spectrum in cone-beam CT system with FDK reconstruction,” Med. Phys. 38, 2122–2131 (2011).
[Crossref] [PubMed]

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38, 3165–3176 (2011).
[Crossref] [PubMed]

I. Reiser, S. Lee, and R. M. Nishikawa, “On the orientation of mammographic structure,” Med. Phys. 38, 5303–5306 (2011).
[Crossref] [PubMed]

2010 (3)

S. Richard and E. Samei, “Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance,” Med. Phys. 37, 2627–2637 (2010).
[Crossref] [PubMed]

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, 1948–1965 (2010).
[Crossref] [PubMed]

I. Reiser and R. M. Nishikawa, “Task-based assessment of breast tomosynthesis: Effect of acquisition parameters and quantum noisea),” Med. Phys. 37, 1591–1600 (2010).
[Crossref] [PubMed]

2009 (1)

S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
[Crossref]

2008 (1)

K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
[Crossref] [PubMed]

2007 (3)

A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A. 24, B52–B60 (2007).
[Crossref]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

2006 (1)

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

2003 (1)

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A. 20, 1725–1738 (2003).
[Crossref]

2001 (2)

C. K. Abbey and H. H. Barrett, “Human-and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A. 18, 473–488 (2001).
[Crossref]

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[Crossref] [PubMed]

1993 (1)

Z. J. Cao and B. M. W. Tsui, “A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT,” IEEE. Trans. Nucl. Sci. 40, 280–287 (1993).
[Crossref]

1987 (1)

P. C. Johns and M. J. Yaffe, “X-ray characterisation of normal and neoplastic breast tissues,” Phys. Med. Biol. 32, 675–695 (1987).
[Crossref] [PubMed]

1985 (1)

R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12, 252–255 (1985).
[Crossref] [PubMed]

1984 (1)

L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. 1, 612–619 (1984).
[Crossref]

Abbey, C. K.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

L. Chen, C. K. Abbey, and J. M. Boone, “Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities,” Phys. Med. Biol. 58, 1663–1681 (2013).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

N. J. Packard, C. K. Abbey, K. Yang, and J. M. Boone, “Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions,” Med. Phys. 39, 1818–1830 (2012).
[Crossref] [PubMed]

K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
[Crossref] [PubMed]

C. K. Abbey and H. H. Barrett, “Human-and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A. 18, 473–488 (2001).
[Crossref]

H. H. Barrett, C. K. Abbey, B. D. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in “Medical Imaging’98,” (International Society for Optics and Photonics, 1998), pp. 27–43.

C. K. Abbey and J. M. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in “International Workshop on Digital Mammography,” (Springer, 2008), pp. 393–400.
[Crossref]

L. Chen, J. M. Boone, A. Nosratieh, and C. K. Abbey, “NPS comparison of anatomical noise characteristics in mammography, tomosynthesis, and breast CT images using power law metrics,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2011), pp. 79610F.
[Crossref]

Badano, A.

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
[Crossref]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

Baek, J.

M. Han, C. Lee, S. Park, and J. Baek, “Investigation on slice direction dependent detectability of volumetric cone beam CT images,” Opt. Express. 24, 3749–3764 (2016).
[Crossref] [PubMed]

J. Baek and N. J. Pelc, “Local and global 3D noise power spectrum in cone-beam CT system with FDK reconstruction,” Med. Phys. 38, 2122–2131 (2011).
[Crossref] [PubMed]

M. Han, S. Park, and J. Baek, “Effect of anatomical backgrounds on detectability in volumetric cone beam CT images,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2016), pp. 978717.

C. Lee, J. Baek, and S. Park, “Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2016), pp. 97870V.

Bakic, P. R.

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38, 3165–3176 (2011).
[Crossref] [PubMed]

Barrett, H. H.

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A. 20, 1725–1738 (2003).
[Crossref]

C. K. Abbey and H. H. Barrett, “Human-and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A. 18, 473–488 (2001).
[Crossref]

H. H. Barrett, C. K. Abbey, B. D. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in “Medical Imaging’98,” (International Society for Optics and Photonics, 1998), pp. 27–43.

Bateni, C.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

Boone, J. M.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

L. Chen, C. K. Abbey, and J. M. Boone, “Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities,” Phys. Med. Biol. 58, 1663–1681 (2013).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

N. J. Packard, C. K. Abbey, K. Yang, and J. M. Boone, “Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions,” Med. Phys. 39, 1818–1830 (2012).
[Crossref] [PubMed]

K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
[Crossref] [PubMed]

L. Chen, J. M. Boone, A. Nosratieh, and C. K. Abbey, “NPS comparison of anatomical noise characteristics in mammography, tomosynthesis, and breast CT images using power law metrics,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2011), pp. 79610F.
[Crossref]

C. K. Abbey and J. M. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in “International Workshop on Digital Mammography,” (Springer, 2008), pp. 393–400.
[Crossref]

Burgess, A. E.

A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A. 24, B52–B60 (2007).
[Crossref]

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[Crossref] [PubMed]

Cao, Z. J.

Z. J. Cao and B. M. W. Tsui, “A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT,” IEEE. Trans. Nucl. Sci. 40, 280–287 (1993).
[Crossref]

Chen, L.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

L. Chen, C. K. Abbey, and J. M. Boone, “Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities,” Phys. Med. Biol. 58, 1663–1681 (2013).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

L. Chen, J. M. Boone, A. Nosratieh, and C. K. Abbey, “NPS comparison of anatomical noise characteristics in mammography, tomosynthesis, and breast CT images using power law metrics,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2011), pp. 79610F.
[Crossref]

Clarkson, E.

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

Davis, L. C.

L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. 1, 612–619 (1984).
[Crossref]

Eckstein, M. P.

H. H. Barrett, C. K. Abbey, B. D. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in “Medical Imaging’98,” (International Society for Optics and Photonics, 1998), pp. 27–43.

Feldkamp, L. A.

L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. 1, 612–619 (1984).
[Crossref]

Gallas, B. D.

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
[Crossref]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A. 20, 1725–1738 (2003).
[Crossref]

H. H. Barrett, C. K. Abbey, B. D. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in “Medical Imaging’98,” (International Society for Optics and Photonics, 1998), pp. 27–43.

Gang, G. J.

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, 1948–1965 (2010).
[Crossref] [PubMed]

Gazi, P.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

Glick, S. J.

S. Vedantham, L. Shi, S. J. Glick, and A. Karellas, “Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT,” Med. Phys. 40, 011901 (2013).
[Crossref] [PubMed]

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

S. J. Glick, S. Vedantham, and A. Karellas, “Investigation of optimal kVp settings for CT mammography using a flat-panel imager,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2002), pp. 392–402.
[Crossref]

Gong, X.

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

Goossens, B.

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

Han, M.

M. Han, C. Lee, S. Park, and J. Baek, “Investigation on slice direction dependent detectability of volumetric cone beam CT images,” Opt. Express. 24, 3749–3764 (2016).
[Crossref] [PubMed]

M. Han, S. Park, and J. Baek, “Effect of anatomical backgrounds on detectability in volumetric cone beam CT images,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2016), pp. 978717.

Hargreaves, J.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

He, X.

X. He and S. Park, “Model observers in medical imaging research,” Theranostics 3, 774–786 (2013).
[Crossref] [PubMed]

Hernandez, A.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

Jacobson, F. L.

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[Crossref] [PubMed]

Jennings, R. J.

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

Johns, P. C.

P. C. Johns and M. J. Yaffe, “X-ray characterisation of normal and neoplastic breast tissues,” Phys. Med. Biol. 32, 675–695 (1987).
[Crossref] [PubMed]

Judy, P. F.

A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A. 24, B52–B60 (2007).
[Crossref]

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[Crossref] [PubMed]

Kak, A. C.

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE,1988).

Karellas, A.

S. Vedantham, L. Shi, S. J. Glick, and A. Karellas, “Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT,” Med. Phys. 40, 011901 (2013).
[Crossref] [PubMed]

S. J. Glick, S. Vedantham, and A. Karellas, “Investigation of optimal kVp settings for CT mammography using a flat-panel imager,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2002), pp. 392–402.
[Crossref]

Kress, J. W.

L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. 1, 612–619 (1984).
[Crossref]

Kupinski, M. A.

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

Lee, C.

M. Han, C. Lee, S. Park, and J. Baek, “Investigation on slice direction dependent detectability of volumetric cone beam CT images,” Opt. Express. 24, 3749–3764 (2016).
[Crossref] [PubMed]

C. Lee, J. Baek, and S. Park, “Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2016), pp. 97870V.

Lee, J.

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, 1948–1965 (2010).
[Crossref] [PubMed]

Lee, S.

I. Reiser, S. Lee, and R. M. Nishikawa, “On the orientation of mammographic structure,” Med. Phys. 38, 5303–5306 (2011).
[Crossref] [PubMed]

Lindfors, K. K.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

Liu, B.

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

Maidment, A. D. A.

P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38, 3165–3176 (2011).
[Crossref] [PubMed]

Metheany, K. G.

K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
[Crossref] [PubMed]

Myers, K. J.

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
[Crossref]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

Nishikawa, R. M.

I. Reiser, S. Lee, and R. M. Nishikawa, “On the orientation of mammographic structure,” Med. Phys. 38, 5303–5306 (2011).
[Crossref] [PubMed]

I. Reiser and R. M. Nishikawa, “Task-based assessment of breast tomosynthesis: Effect of acquisition parameters and quantum noisea),” Med. Phys. 37, 1591–1600 (2010).
[Crossref] [PubMed]

Nosratieh, A.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

L. Chen, J. M. Boone, A. Nosratieh, and C. K. Abbey, “NPS comparison of anatomical noise characteristics in mammography, tomosynthesis, and breast CT images using power law metrics,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2011), pp. 79610F.
[Crossref]

Packard, N.

K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
[Crossref] [PubMed]

Packard, N. J.

N. J. Packard, C. K. Abbey, K. Yang, and J. M. Boone, “Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions,” Med. Phys. 39, 1818–1830 (2012).
[Crossref] [PubMed]

Park, S.

M. Han, C. Lee, S. Park, and J. Baek, “Investigation on slice direction dependent detectability of volumetric cone beam CT images,” Opt. Express. 24, 3749–3764 (2016).
[Crossref] [PubMed]

X. He and S. Park, “Model observers in medical imaging research,” Theranostics 3, 774–786 (2013).
[Crossref] [PubMed]

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
[Crossref]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

M. Han, S. Park, and J. Baek, “Effect of anatomical backgrounds on detectability in volumetric cone beam CT images,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2016), pp. 978717.

C. Lee, J. Baek, and S. Park, “Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2016), pp. 97870V.

Pelc, N. J.

J. Baek and N. J. Pelc, “Local and global 3D noise power spectrum in cone-beam CT system with FDK reconstruction,” Med. Phys. 38, 2122–2131 (2011).
[Crossref] [PubMed]

Petrick, N. A.

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

Philips, W.

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

Platiša, L.

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

Reiser, I.

I. Reiser, S. Lee, and R. M. Nishikawa, “On the orientation of mammographic structure,” Med. Phys. 38, 5303–5306 (2011).
[Crossref] [PubMed]

I. Reiser and R. M. Nishikawa, “Task-based assessment of breast tomosynthesis: Effect of acquisition parameters and quantum noisea),” Med. Phys. 37, 1591–1600 (2010).
[Crossref] [PubMed]

Richard, S.

S. Richard and E. Samei, “Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance,” Med. Phys. 37, 2627–2637 (2010).
[Crossref] [PubMed]

Samei, E.

S. Richard and E. Samei, “Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance,” Med. Phys. 37, 2627–2637 (2010).
[Crossref] [PubMed]

Shi, L.

S. Vedantham, L. Shi, S. J. Glick, and A. Karellas, “Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT,” Med. Phys. 40, 011901 (2013).
[Crossref] [PubMed]

Siddon, R. L.

R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12, 252–255 (1985).
[Crossref] [PubMed]

Siewerdsen, J. H.

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, 1948–1965 (2010).
[Crossref] [PubMed]

Slaney, M.

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE,1988).

Thacker, S.

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

Tsui, B. M. W.

Z. J. Cao and B. M. W. Tsui, “A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT,” IEEE. Trans. Nucl. Sci. 40, 280–287 (1993).
[Crossref]

Tward, D. J.

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, 1948–1965 (2010).
[Crossref] [PubMed]

Vansteenkiste, E.

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

Vedantham, S.

S. Vedantham, L. Shi, S. J. Glick, and A. Karellas, “Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT,” Med. Phys. 40, 011901 (2013).
[Crossref] [PubMed]

S. J. Glick, S. Vedantham, and A. Karellas, “Investigation of optimal kVp settings for CT mammography using a flat-panel imager,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2002), pp. 392–402.
[Crossref]

Vedula, A. A.

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

Yaffe, M. J.

P. C. Johns and M. J. Yaffe, “X-ray characterisation of normal and neoplastic breast tissues,” Phys. Med. Biol. 32, 675–695 (1987).
[Crossref] [PubMed]

Yang, K.

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

N. J. Packard, C. K. Abbey, K. Yang, and J. M. Boone, “Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions,” Med. Phys. 39, 1818–1830 (2012).
[Crossref] [PubMed]

Young, S.

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

Zhang, C.

P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38, 3165–3176 (2011).
[Crossref] [PubMed]

IEEE Trans. Med. Img (1)

S. Park, A. Badano, B. D. Gallas, and K. J. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Img 28, 339–347 (2009).
[Crossref]

IEEE. Trans. Nucl. Sci. (1)

Z. J. Cao and B. M. W. Tsui, “A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT,” IEEE. Trans. Nucl. Sci. 40, 280–287 (1993).
[Crossref]

J. Opt. Soc. Am. (1)

L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” J. Opt. Soc. Am. 1, 612–619 (1984).
[Crossref]

J. Opt. Soc. Am. A. (6)

C. K. Abbey and H. H. Barrett, “Human-and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A. 18, 473–488 (2001).
[Crossref]

S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A. 24, 911–921 (2007).
[Crossref]

L. Platiša, B. Goossens, E. Vansteenkiste, S. Park, B. D. Gallas, A. Badano, and W. Philips, “Channelized Hotelling observers for the assessment of volumetric imaging data sets,” J. Opt. Soc. Am. A. 28, 1145–1163 (2011).
[Crossref]

A. E. Burgess and P. F. Judy, “Signal detection in power-law noise: effect of spectrum exponents,” J. Opt. Soc. Am. A. 24, B52–B60 (2007).
[Crossref]

B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A. 20, 1725–1738 (2003).
[Crossref]

S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A. 24, B136–B150 (2007).
[Crossref]

Med. Phys. (14)

S. Young, P. R. Bakic, K. J. Myers, R. J. Jennings, and S. Park, “A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data,” Med. Phys. 40, 051914 (2013).
[Crossref] [PubMed]

N. J. Packard, C. K. Abbey, K. Yang, and J. M. Boone, “Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions,” Med. Phys. 39, 1818–1830 (2012).
[Crossref] [PubMed]

K. G. Metheany, C. K. Abbey, N. Packard, and J. M. Boone, “Characterizing anatomical variability in breast CT images,” Med. Phys. 35, 4685–4694 (2008).
[Crossref] [PubMed]

I. Reiser and R. M. Nishikawa, “Task-based assessment of breast tomosynthesis: Effect of acquisition parameters and quantum noisea),” Med. Phys. 37, 1591–1600 (2010).
[Crossref] [PubMed]

A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[Crossref] [PubMed]

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, 1435–1441 (2012).
[Crossref] [PubMed]

X. Gong, S. J. Glick, B. Liu, A. A. Vedula, and S. Thacker, “A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging,” Med. Phys. 33, 1041–1052 (2006).
[Crossref] [PubMed]

P. R. Bakic, C. Zhang, and A. D. A. Maidment, “Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm,” Med. Phys. 38, 3165–3176 (2011).
[Crossref] [PubMed]

I. Reiser, S. Lee, and R. M. Nishikawa, “On the orientation of mammographic structure,” Med. Phys. 38, 5303–5306 (2011).
[Crossref] [PubMed]

S. Vedantham, L. Shi, S. J. Glick, and A. Karellas, “Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT,” Med. Phys. 40, 011901 (2013).
[Crossref] [PubMed]

R. L. Siddon, “Fast calculation of the exact radiological path for a three-dimensional CT array,” Med. Phys. 12, 252–255 (1985).
[Crossref] [PubMed]

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, 1948–1965 (2010).
[Crossref] [PubMed]

S. Richard and E. Samei, “Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance,” Med. Phys. 37, 2627–2637 (2010).
[Crossref] [PubMed]

J. Baek and N. J. Pelc, “Local and global 3D noise power spectrum in cone-beam CT system with FDK reconstruction,” Med. Phys. 38, 2122–2131 (2011).
[Crossref] [PubMed]

Opt. Express. (1)

M. Han, C. Lee, S. Park, and J. Baek, “Investigation on slice direction dependent detectability of volumetric cone beam CT images,” Opt. Express. 24, 3749–3764 (2016).
[Crossref] [PubMed]

Phys. Med. Biol. (3)

L. Chen, J. M. Boone, C. K. Abbey, J. Hargreaves, C. Bateni, K. K. Lindfors, K. Yang, A. Nosratieh, A. Hernandez, and P. Gazi, “Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast,” Phys. Med. Biol. 60, 3347–3358 (2015).
[Crossref] [PubMed]

L. Chen, C. K. Abbey, and J. M. Boone, “Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities,” Phys. Med. Biol. 58, 1663–1681 (2013).
[Crossref] [PubMed]

P. C. Johns and M. J. Yaffe, “X-ray characterisation of normal and neoplastic breast tissues,” Phys. Med. Biol. 32, 675–695 (1987).
[Crossref] [PubMed]

Theranostics (1)

X. He and S. Park, “Model observers in medical imaging research,” Theranostics 3, 774–786 (2013).
[Crossref] [PubMed]

Other (7)

L. Chen, J. M. Boone, A. Nosratieh, and C. K. Abbey, “NPS comparison of anatomical noise characteristics in mammography, tomosynthesis, and breast CT images using power law metrics,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2011), pp. 79610F.
[Crossref]

H. H. Barrett, C. K. Abbey, B. D. Gallas, and M. P. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in “Medical Imaging’98,” (International Society for Optics and Photonics, 1998), pp. 27–43.

M. Han, S. Park, and J. Baek, “Effect of anatomical backgrounds on detectability in volumetric cone beam CT images,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2016), pp. 978717.

S. J. Glick, S. Vedantham, and A. Karellas, “Investigation of optimal kVp settings for CT mammography using a flat-panel imager,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2002), pp. 392–402.
[Crossref]

C. K. Abbey and J. M. Boone, “An ideal observer for a model of x-ray imaging in breast parenchymal tissue,” in “International Workshop on Digital Mammography,” (Springer, 2008), pp. 393–400.
[Crossref]

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE,1988).

C. Lee, J. Baek, and S. Park, “Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation,” in “Proc. SPIE.”, (International Society for Optics and Photonics, 2016), pp. 97870V.

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Figures (14)

Fig. 1
Fig. 1 Central planes of generated 3D anatomical noise volumes for (a) 15% VGF, (b) 30% VGF, and (c) 60% VGF. The white region indicates glandular tissue, whereas the gray region indicates adipose tissue. The display window is [0 0.8]cm−1.
Fig. 2
Fig. 2 Background power spectra of projected 30% VGF anatomical noise volume for (a) 0.8×0.8×0.8mm3, (b) 0.4×0.4×0.4mm3, (c) 0.2×0.2×0.2mm3, and (d) 0.16×0.16×0.16mm3 voxel sizes.
Fig. 3
Fig. 3 0.1mm slice thickness image of 30% VGF along the (a) transverse and (b) longitudinal directions. Signal diameter increases from 1mm (left) to 11mm (right). The display window is [0.2 1]cm−1.
Fig. 4
Fig. 4 13mm slice thickness image of 30% VGF along the (a) transverse and (b) longitudinal directions. Signal diameter increases from 1mm (left) to 11mm (right). The display window is [0.4 0.85]cm−1.
Fig. 5
Fig. 5 20 LG spatial channel images with au=10 from p=0 (top left) to p=19 (bottom right).
Fig. 6
Fig. 6 10 D-DOG frequency channel images from j=1 (left) to j=10 (right).
Fig. 7
Fig. 7 SNRLG as a function of the slice thickness with 95% confidence interval for (a) 15% VGF, (b) 30% VGF, (c) 60% VGF, and (d) uniform background.
Fig. 8
Fig. 8 SNRD−DOG as a function of the slice thickness with 95% confidence interval for (a) 15% VGF, (b) 30% VGF, (c) 60% VGF, and (d) uniform background.
Fig. 9
Fig. 9 SNRLG ratio of longitudinal over transverse planes with 95% confidence interval for (a) 0.1mm, (b) 1.9mm, (c) 3.8mm and (d) 5.6mm slice thicknesses.
Fig. 10
Fig. 10 SNRD−DOG ratio of longitudinal over transverse planes with 95% confidence interval for (a) 0.1mm, (b) 1.9mm, (c) 3.8mm and (d) 5.6mm slice thicknesses.
Fig. 11
Fig. 11 The value of β as a function of the slice thickness.
Fig. 12
Fig. 12 Logarithm-applied 2D NPS images with (a) 0.1mm and (b) 5.6mm slice thicknesses.
Fig. 13
Fig. 13 Log-log plots of radially averaged NPS for (a) 15% VGF, (b) 30% VGF, and (c) 60% VGF.
Fig. 14
Fig. 14 (a) 0.1mm, (b) 1.9mm, (c) 3.8mm, and (d) 5.6mm slice thicknesses images of 30% VGF along the transverse and longitudinal directions. Signal diameter increases from 1mm (left) to 11mm (right).

Tables (1)

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Table 1 Efficiency of D-DOG CHO relative to the LG CHO (0.1mm slice thickness).

Equations (11)

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P ( f ) = α / f β
H 0 : g = f b + f n
H 1 : g = f s + f n
u p ( r | a u ) = 2 a u exp ( π r 2 a u 2 ) L p ( 2 π r 2 a u 2 )
L p ( x ) = k = 0 p ( 1 ) k ( p k ) x k k !
C j ( ρ ) = exp [ 1 2 ( ρ Q σ j ) 2 ] exp [ 1 2 ( ρ σ j ) 2 ]
v = Tg + ϵ
w = K v 1 Δ v
t = w t v j
SNR = E [ t 1 ] E [ t 0 ] ( σ 0 2 + σ 1 2 ) / 2
W ( r ) = { 0.5 + 0.5 c o s ( π r / D ) r D 0 r > D

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