Abstract

We investigate the detection performance of transverse and longitudinal planes for various signal sizes (i.e., 1 mm to 8 mm diameter spheres) in cone beam computed tomography (CBCT) images. CBCT images are generated by computer simulation and images are reconstructed using an FDK algorithm. For each slice direction and signal size, a human observer study is conducted with a signal-known-exactly/background-known-exactly (SKE/BKE) binary detection task. The detection performance of human observers is compared with that of a channelized Hotelling observer (CHO). The detection performance of an ideal linear observer is also calculated using a CHO with Laguerre-Gauss (LG) channels. The detectability of high contrast small signals (i.e., up to 4-mm-diameter spheres) is higher in the longitudinal plane than the transverse plane. It is also shown that CHO performance correlates well with human observer performance in both transverse and longitudinal plane images.

© 2016 Optical Society of America

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References

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  27. 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]
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    [Crossref] [PubMed]
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    [Crossref]
  32. L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
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  35. M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
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  42. B. D. Gallas, G. A. Pennello, and K. J. Myers, “Multireader multicase variance analysis for binary data,” J. Opt. Soc. Am. A 24, B70–B80 (2007).
    [Crossref]
  43. C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
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    [Crossref] [PubMed]
  45. 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]
  46. S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
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  52. J. Baek and N. J. Pelc, “Effect of detector lag on CT noise power spectra,” Med. Phys. 38, 2995–3005 (2011).
    [Crossref] [PubMed]
  53. J. Hsieh and X. Tang, “Tilted cone-beam reconstruction with row-wise fan-to-parallel rebinning,” Phys. Med. Biol. 51, 5259–5276 (2006).
    [Crossref] [PubMed]
  54. C. Lee, M. Han, and J. Baek, “SU-E-I-10: Investigation on detectability of a small target for different slice direction of a volumetric cone beam CT image,” Med. Phys. 42, 3243(2015).
    [Crossref]
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  56. J. Baek, A. R. Pineda, and N. J. Pelc, “To bin or not to bin? the effect of CT system limiting resolution on noise and detectability,” Phys. Med. Biol. 58, 1433 (2013).
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  58. S. Park, H. Liu, A. Badano, and K. J. Myers, “A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms,” Med. Phys. 37, 6253–6270 (2010).
    [Crossref]
  59. 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]

2015 (3)

C. Lee and J. Baek, “A new method to measure directional modulation transfer function using sphere phantoms in a cone beam computed tomography system,” IEEE Trans. Med. Img. 34, 902–910 (2015).
[Crossref]

E. Samei and S. Richard, “Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology,” Med. Phys. 42, 314–323 (2015).
[Crossref] [PubMed]

C. Lee, M. Han, and J. Baek, “SU-E-I-10: Investigation on detectability of a small target for different slice direction of a volumetric cone beam CT image,” Med. Phys. 42, 3243(2015).
[Crossref]

2014 (1)

B. Chen, O. Christianson, J. M. Wilson, and E. Samei, “Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods,” Med. Phys. 41, 071909 (2014).
[Crossref] [PubMed]

2013 (5)

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

R. Spin-Neto, E. Gotfredsen, and A. Wenzel, “Impact of voxel size variation on CBCT-based diagnostic outcome in dentistry: a systematic review,” J. Dig. Imag. 26, 813–820 (2013).
[Crossref]

J. Baek, A. R. Pineda, and N. J. Pelc, “To bin or not to bin? the effect of CT system limiting resolution on noise and detectability,” Phys. Med. Biol. 58, 1433 (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]

2012 (1)

A. R. Pineda, D. J. Tward, A. Gonzalez, and J. H. Siewerdsen, “Beyond noise power in 3D computed tomography: the local NPS and off-diagonal elements of the Fourier domain covariance matrix,” Med. Phys. 39, 3240–3252 (2012).
[Crossref] [PubMed]

2011 (3)

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]

S. Richard, X. Li, G. Yadava, and E. Samei, “Predictive models for observer performance in ct: applications in protocol optimization,” Proc. SPIE 7961, 79610H (2011).
[Crossref]

J. Baek and N. J. Pelc, “Effect of detector lag on CT noise power spectra,” Med. Phys. 38, 2995–3005 (2011).
[Crossref] [PubMed]

2010 (4)

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Img. 29, 1050–1058 (2010).
[Crossref]

S. Park, H. Liu, A. Badano, and K. J. Myers, “A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms,” Med. Phys. 37, 6253–6270 (2010).
[Crossref]

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]

J. Baek and N. J. Pelc, “The noise power spectrum in CT with direct fan beam reconstruction,” Med. Phys. 37, 2074–2081 (2010).
[Crossref] [PubMed]

2009 (5)

D. J. Tward and J. H. Siewerdsen, “Noise aliasing and the 3D NEQ of flat-panel cone-beam CT: effect of 2D/3D apertures and sampling,” Med. Phys. 36, 3830–3843 (2009).
[Crossref] [PubMed]

A. Wunderlich and F. Noo, “Estimation of channelized Hotelling observer performance with known class means or known difference of class means,” IEEE Trans. Med. Img. 28, 1198–1207 (2009).
[Crossref]

J. D. Silverman, N. S. Paul, and J. H. Siewerdsen, “Investigation of lung nodule detectability in low-dose 320-slice computed tomography,” Med. Phys. 36, 1700–1710 (2009).
[Crossref] [PubMed]

C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
[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]

2008 (2)

A. Linda, C. Zuiani, V. Londero, and M. Bazzocchi, “Outcome of initially only magnetic resonance mammography-detected findings with and without correlate at second-look sonography: distribution according to patient history of breast cancer and lesion size,” The Breast 17, 53–59 (2008).
[Crossref]

W. C. Scarfe and A. G. Farman, “What is cone-beam CT and how does it work?” Dent. Clin. N. Am. 52, 707–730 (2008).
[Crossref] [PubMed]

2007 (10)

A. L. C. Kwan, J. M. Boone, K. Yang, and S. Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner,” Med. Phys. 34, 275–281 (2007).
[Crossref] [PubMed]

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[Crossref] [PubMed]

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (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]

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]

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

B. D. Gallas, G. A. Pennello, and K. J. Myers, “Multireader multicase variance analysis for binary data,” J. Opt. Soc. Am. A 24, B70–B80 (2007).
[Crossref]

S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
[Crossref] [PubMed]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312–3322 (2007).
[Crossref] [PubMed]

C. K. Abbey and M. P. Eckstein, “Classification images for simple detection and discrimination tasks in correlated noise,” J. Opt. Soc. Am. A 24, B110–B124 (2007).
[Crossref]

2006 (5)

L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
[Crossref]

J. Hsieh and X. Tang, “Tilted cone-beam reconstruction with row-wise fan-to-parallel rebinning,” Phys. Med. Biol. 51, 5259–5276 (2006).
[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]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds,” IEEE Trans. Med. Img. 25, 1348–1362 (2006).
[Crossref]

A. Badano and B. D. Gallas, “Detectability decreases with off-normal viewing in medical liquid crystal displays,” Acad. Radiol. 13, 210–218 (2006).
[Crossref] [PubMed]

2005 (2)

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
[Crossref]

H. C. Gifford, M. A. King, P. H. Pretorius, and R. G. Wells, “A comparison of human and model observers in multislice lroc studies,” IEEE Trans. Med. Img. 24, 160–169 (2005).
[Crossref]

2004 (2)

Z. Chen and R. Ning, “Three-dimensional point spread function measurement of cone-beam computed tomography system by iterative edge-blurring algorithm,” Phys. Med. Biol. 49, 1865–1880 (2004).
[Crossref] [PubMed]

J. Oldan, S. Kulkarni, Y. Xing, P. Khurd, and G. Gindi, “Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction,” IEEE Trans. Nucl. Sci. 51, 733–741 (2004).
[Crossref]

2003 (3)

M. P. Eckstein, J. L. Bartroff, C. K. Abbey, J. S. Whiting, and F. O. Bochud, “Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks,” Opt. Express. 11, 460–475 (2003).
[Crossref] [PubMed]

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

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]

2002 (1)

C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments,” IEEE Trans. Med. Img. 21, 429–440 (2002).
[Crossref]

2001 (1)

2000 (1)

D. Wormanns, S. Diederich, M. G. Lentschig, F. Winter, and W. Heindel, “Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size,” Eur. Radiol. 10, 710–713 (2000).
[Crossref] [PubMed]

1999 (1)

M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. simulation studies,” Med. Phys. 26, 2235–2247 (1999).
[Crossref] [PubMed]

1997 (1)

L. Wang, H. M. Lai, A. J. Thompson, and D. H. Miller, “Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load,” J. Neurol. Neurosur. Ps. 63, 452–455 (1997).
[Crossref]

1990 (1)

1987 (1)

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]

1982 (1)

R. T. Droege and R. L. Morin, “A practical method to measure the MTF of CT scanners,” Med. Phys. 9, 758–760 (1982).
[Crossref] [PubMed]

Abbey, C. K.

Altunbas, M. C.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Aughenbaugh, G. L.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

Badano, A.

S. Park, H. Liu, A. Badano, and K. J. Myers, “A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms,” Med. Phys. 37, 6253–6270 (2010).
[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]

A. Badano and B. D. Gallas, “Detectability decreases with off-normal viewing in medical liquid crystal displays,” Acad. Radiol. 13, 210–218 (2006).
[Crossref] [PubMed]

Baek, J.

C. Lee and J. Baek, “A new method to measure directional modulation transfer function using sphere phantoms in a cone beam computed tomography system,” IEEE Trans. Med. Img. 34, 902–910 (2015).
[Crossref]

C. Lee, M. Han, and J. Baek, “SU-E-I-10: Investigation on detectability of a small target for different slice direction of a volumetric cone beam CT image,” Med. Phys. 42, 3243(2015).
[Crossref]

J. Baek, A. R. Pineda, and N. J. Pelc, “To bin or not to bin? the effect of CT system limiting resolution on noise and detectability,” Phys. Med. Biol. 58, 1433 (2013).
[Crossref] [PubMed]

J. Baek and N. J. Pelc, “Effect of detector lag on CT noise power spectra,” Med. Phys. 38, 2995–3005 (2011).
[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]

J. Baek and N. J. Pelc, “The noise power spectrum in CT with direct fan beam reconstruction,” Med. Phys. 37, 2074–2081 (2010).
[Crossref] [PubMed]

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]

Barrett, H. H.

Bartroff, J. L.

M. P. Eckstein, J. L. Bartroff, C. K. Abbey, J. S. Whiting, and F. O. Bochud, “Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks,” Opt. Express. 11, 460–475 (2003).
[Crossref] [PubMed]

Bazzocchi, M.

A. Linda, C. Zuiani, V. Londero, and M. Bazzocchi, “Outcome of initially only magnetic resonance mammography-detected findings with and without correlate at second-look sonography: distribution according to patient history of breast cancer and lesion size,” The Breast 17, 53–59 (2008).
[Crossref]

Bochud, F. O.

C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
[Crossref]

M. P. Eckstein, J. L. Bartroff, C. K. Abbey, J. S. Whiting, and F. O. Bochud, “Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks,” Opt. Express. 11, 460–475 (2003).
[Crossref] [PubMed]

Boone, J. M.

A. L. C. Kwan, J. M. Boone, K. Yang, and S. Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner,” Med. Phys. 34, 275–281 (2007).
[Crossref] [PubMed]

Brem, R. F.

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
[Crossref]

Carter, R. E.

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

Castella, C.

Chen, B.

B. Chen, O. Christianson, J. M. Wilson, and E. Samei, “Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods,” Med. Phys. 41, 071909 (2014).
[Crossref] [PubMed]

Chen, L.

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Chen, Z.

Z. Chen and R. Ning, “Three-dimensional point spread function measurement of cone-beam computed tomography system by iterative edge-blurring algorithm,” Phys. Med. Biol. 49, 1865–1880 (2004).
[Crossref] [PubMed]

Christianson, O.

B. Chen, O. Christianson, J. M. Wilson, and E. Samei, “Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods,” Med. Phys. 41, 071909 (2014).
[Crossref] [PubMed]

Clarkson, E.

Clemens, M. A.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

Daly, M. J.

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[Crossref] [PubMed]

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]

Dershaw, D. D.

L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
[Crossref]

DeSimio, M. P.

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
[Crossref]

Diederich, S.

D. Wormanns, S. Diederich, M. G. Lentschig, F. Winter, and W. Heindel, “Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size,” Eur. Radiol. 10, 710–713 (2000).
[Crossref] [PubMed]

Droege, R. T.

R. T. Droege and R. L. Morin, “A practical method to measure the MTF of CT scanners,” Med. Phys. 9, 758–760 (1982).
[Crossref] [PubMed]

Eckstein, M. P.

C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
[Crossref]

C. K. Abbey and M. P. Eckstein, “Classification images for simple detection and discrimination tasks in correlated noise,” J. Opt. Soc. Am. A 24, B110–B124 (2007).
[Crossref]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312–3322 (2007).
[Crossref] [PubMed]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds,” IEEE Trans. Med. Img. 25, 1348–1362 (2006).
[Crossref]

M. P. Eckstein, J. L. Bartroff, C. K. Abbey, J. S. Whiting, and F. O. Bochud, “Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks,” Opt. Express. 11, 460–475 (2003).
[Crossref] [PubMed]

C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments,” IEEE Trans. Med. Img. 21, 429–440 (2002).
[Crossref]

Fahrig, R. A.

D. J. Tward, J. H. Siewerdsen, R. A. Fahrig, and A. R. Pineda, “Cascaded systems analysis of the 3D NEQ for cone-beam CT and tomosynthesis,” Proc. SPIE6913, 69131S (2008).
[Crossref]

Farman, A. G.

W. C. Scarfe and A. G. Farman, “What is cone-beam CT and how does it work?” Dent. Clin. N. Am. 52, 707–730 (2008).
[Crossref] [PubMed]

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]

Fletcher, J.

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

Gallas, B. D.

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]

Gies, M.

M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. simulation studies,” Med. Phys. 26, 2235–2247 (1999).
[Crossref] [PubMed]

Gifford, H. C.

H. C. Gifford, M. A. King, P. H. Pretorius, and R. G. Wells, “A comparison of human and model observers in multislice lroc studies,” IEEE Trans. Med. Img. 24, 160–169 (2005).
[Crossref]

Gindi, G.

S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
[Crossref] [PubMed]

J. Oldan, S. Kulkarni, Y. Xing, P. Khurd, and G. Gindi, “Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction,” IEEE Trans. Nucl. Sci. 51, 733–741 (2004).
[Crossref]

Glick, S. J.

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]

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]

Gonzalez, A.

A. R. Pineda, D. J. Tward, A. Gonzalez, and J. H. Siewerdsen, “Beyond noise power in 3D computed tomography: the local NPS and off-diagonal elements of the Fourier domain covariance matrix,” Med. Phys. 39, 3240–3252 (2012).
[Crossref] [PubMed]

Gotfredsen, E.

R. Spin-Neto, E. Gotfredsen, and A. Wenzel, “Impact of voxel size variation on CBCT-based diagnostic outcome in dentistry: a systematic review,” J. Dig. Imag. 26, 813–820 (2013).
[Crossref]

Gould, M. K.

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

Han, M.

C. Lee, M. Han, and J. Baek, “SU-E-I-10: Investigation on detectability of a small target for different slice direction of a volumetric cone beam CT image,” Med. Phys. 42, 3243(2015).
[Crossref]

Han, T.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Hartman, T. E.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

He, X.

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

Heindel, W.

D. Wormanns, S. Diederich, M. G. Lentschig, F. Winter, and W. Heindel, “Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size,” Eur. Radiol. 10, 710–713 (2000).
[Crossref] [PubMed]

Hoffmeister, J. W.

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
[Crossref]

Hsiao, I.

S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
[Crossref] [PubMed]

Hsieh, J.

J. Hsieh and X. Tang, “Tilted cone-beam reconstruction with row-wise fan-to-parallel rebinning,” Phys. Med. Biol. 51, 5259–5276 (2006).
[Crossref] [PubMed]

J. Hsieh, Computed Tomography: Principles, Design, Artifacts, and Recent Advances (SPIE, 2009).

Huang, S. Y.

A. L. C. Kwan, J. M. Boone, K. Yang, and S. Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner,” Med. Phys. 34, 275–281 (2007).
[Crossref] [PubMed]

Iannettoni, M. D.

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

Jaffray, D. A.

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[Crossref] [PubMed]

J. H. Siewerdsen and D. A. Jaffray, “Three-dimensional NEQ transfer characteristics of volume CT using direct-and indirect-detection flat-panel imagers,” Proc. SPIE5030, 92–102 (2003).
[Crossref]

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]

Jett, J. R.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

Kalender, W. A.

M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. simulation studies,” Med. Phys. 26, 2235–2247 (1999).
[Crossref] [PubMed]

Khurd, P.

S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
[Crossref] [PubMed]

J. Oldan, S. Kulkarni, Y. Xing, P. Khurd, and G. Gindi, “Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction,” IEEE Trans. Nucl. Sci. 51, 733–741 (2004).
[Crossref]

King, M. A.

H. C. Gifford, M. A. King, P. H. Pretorius, and R. G. Wells, “A comparison of human and model observers in multislice lroc studies,” IEEE Trans. Med. Img. 24, 160–169 (2005).
[Crossref]

Kinkel, K.

Kofler, J. M.

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

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]

Kulkarni, S.

S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
[Crossref] [PubMed]

J. Oldan, S. Kulkarni, Y. Xing, P. Khurd, and G. Gindi, “Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction,” IEEE Trans. Nucl. Sci. 51, 733–741 (2004).
[Crossref]

Kupinski, M. A.

Kwan, A. L. C.

A. L. C. Kwan, J. M. Boone, K. Yang, and S. Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner,” Med. Phys. 34, 275–281 (2007).
[Crossref] [PubMed]

Lai, C. J.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Lai, H. M.

L. Wang, H. M. Lai, A. J. Thompson, and D. H. Miller, “Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load,” J. Neurol. Neurosur. Ps. 63, 452–455 (1997).
[Crossref]

Lee, C.

C. Lee and J. Baek, “A new method to measure directional modulation transfer function using sphere phantoms in a cone beam computed tomography system,” IEEE Trans. Med. Img. 34, 902–910 (2015).
[Crossref]

C. Lee, M. Han, and J. Baek, “SU-E-I-10: Investigation on detectability of a small target for different slice direction of a volumetric cone beam CT image,” Med. Phys. 42, 3243(2015).
[Crossref]

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]

Leng, S.

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

Lentschig, M. G.

D. Wormanns, S. Diederich, M. G. Lentschig, F. Winter, and W. Heindel, “Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size,” Eur. Radiol. 10, 710–713 (2000).
[Crossref] [PubMed]

Li, X.

S. Richard, X. Li, G. Yadava, and E. Samei, “Predictive models for observer performance in ct: applications in protocol optimization,” Proc. SPIE 7961, 79610H (2011).
[Crossref]

Liberman, L.

L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
[Crossref]

Linda, A.

A. Linda, C. Zuiani, V. Londero, and M. Bazzocchi, “Outcome of initially only magnetic resonance mammography-detected findings with and without correlate at second-look sonography: distribution according to patient history of breast cancer and lesion size,” The Breast 17, 53–59 (2008).
[Crossref]

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]

Liu, H.

S. Park, H. Liu, A. Badano, and K. J. Myers, “A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms,” Med. Phys. 37, 6253–6270 (2010).
[Crossref]

Liu, X.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Londero, V.

A. Linda, C. Zuiani, V. Londero, and M. Bazzocchi, “Outcome of initially only magnetic resonance mammography-detected findings with and without correlate at second-look sonography: distribution according to patient history of breast cancer and lesion size,” The Breast 17, 53–59 (2008).
[Crossref]

Lynch, W. R.

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

Madsen, M. T.

M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. simulation studies,” Med. Phys. 26, 2235–2247 (1999).
[Crossref] [PubMed]

Mason, G.

L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
[Crossref]

McCollough, C. H.

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

Midthun, D. E.

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

Miller, D. H.

L. Wang, H. M. Lai, A. J. Thompson, and D. H. Miller, “Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load,” J. Neurol. Neurosur. Ps. 63, 452–455 (1997).
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R. T. Droege and R. L. Morin, “A practical method to measure the MTF of CT scanners,” Med. Phys. 9, 758–760 (1982).
[Crossref] [PubMed]

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L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
[Crossref]

Moseley, D. J.

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[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]

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Img. 29, 1050–1058 (2010).
[Crossref]

S. Park, H. Liu, A. Badano, and K. J. Myers, “A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms,” Med. Phys. 37, 6253–6270 (2010).
[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, 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]

B. D. Gallas, G. A. Pennello, and K. J. Myers, “Multireader multicase variance analysis for binary data,” J. Opt. Soc. Am. A 24, B70–B80 (2007).
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K. J. Myers and H. H. Barrett, “Addition of a channel mechanism to the ideal-observer model,”J. Opt. Soc. Am. A 4, 2447–2457 (1987).
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M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
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Z. Chen and R. Ning, “Three-dimensional point spread function measurement of cone-beam computed tomography system by iterative edge-blurring algorithm,” Phys. Med. Biol. 49, 1865–1880 (2004).
[Crossref] [PubMed]

Noo, F.

A. Wunderlich and F. Noo, “Estimation of channelized Hotelling observer performance with known class means or known difference of class means,” IEEE Trans. Med. Img. 28, 1198–1207 (2009).
[Crossref]

Oldan, J.

J. Oldan, S. Kulkarni, Y. Xing, P. Khurd, and G. Gindi, “Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction,” IEEE Trans. Nucl. Sci. 51, 733–741 (2004).
[Crossref]

Ost, D. E.

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

Park, 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]

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

S. Park, H. Liu, A. Badano, and K. J. Myers, “A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms,” Med. Phys. 37, 6253–6270 (2010).
[Crossref]

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Img. 29, 1050–1058 (2010).
[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]

Paul, N. S.

J. D. Silverman, N. S. Paul, and J. H. Siewerdsen, “Investigation of lung nodule detectability in low-dose 320-slice computed tomography,” Med. Phys. 36, 1700–1710 (2009).
[Crossref] [PubMed]

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
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J. Baek, A. R. Pineda, and N. J. Pelc, “To bin or not to bin? the effect of CT system limiting resolution on noise and detectability,” Phys. Med. Biol. 58, 1433 (2013).
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J. Baek and N. J. Pelc, “Effect of detector lag on CT noise power spectra,” Med. Phys. 38, 2995–3005 (2011).
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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).
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J. Baek and N. J. Pelc, “The noise power spectrum in CT with direct fan beam reconstruction,” Med. Phys. 37, 2074–2081 (2010).
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Pennello, G. A.

Petrick, N. A.

Pham, B. T.

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312–3322 (2007).
[Crossref] [PubMed]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds,” IEEE Trans. Med. Img. 25, 1348–1362 (2006).
[Crossref]

Pineda, A. R.

J. Baek, A. R. Pineda, and N. J. Pelc, “To bin or not to bin? the effect of CT system limiting resolution on noise and detectability,” Phys. Med. Biol. 58, 1433 (2013).
[Crossref] [PubMed]

A. R. Pineda, D. J. Tward, A. Gonzalez, and J. H. Siewerdsen, “Beyond noise power in 3D computed tomography: the local NPS and off-diagonal elements of the Fourier domain covariance matrix,” Med. Phys. 39, 3240–3252 (2012).
[Crossref] [PubMed]

D. J. Tward, J. H. Siewerdsen, R. A. Fahrig, and A. R. Pineda, “Cascaded systems analysis of the 3D NEQ for cone-beam CT and tomosynthesis,” Proc. SPIE6913, 69131S (2008).
[Crossref]

Pretorius, P. H.

H. C. Gifford, M. A. King, P. H. Pretorius, and R. G. Wells, “A comparison of human and model observers in multislice lroc studies,” IEEE Trans. Med. Img. 24, 160–169 (2005).
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J. Qi, “Investigation of lesion detection in MAP reconstruction with non-Gaussian priors,” in Proc. IEEE Nucl. Sci. Symp. (IEEE, 2005), pp. 1704–1708.

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E. Samei and S. Richard, “Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology,” Med. Phys. 42, 314–323 (2015).
[Crossref] [PubMed]

S. Richard, X. Li, G. Yadava, and E. Samei, “Predictive models for observer performance in ct: applications in protocol optimization,” Proc. SPIE 7961, 79610H (2011).
[Crossref]

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[Crossref] [PubMed]

Rogers, S. K.

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
[Crossref]

Samei, E.

E. Samei and S. Richard, “Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology,” Med. Phys. 42, 314–323 (2015).
[Crossref] [PubMed]

B. Chen, O. Christianson, J. M. Wilson, and E. Samei, “Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods,” Med. Phys. 41, 071909 (2014).
[Crossref] [PubMed]

S. Richard, X. Li, G. Yadava, and E. Samei, “Predictive models for observer performance in ct: applications in protocol optimization,” Proc. SPIE 7961, 79610H (2011).
[Crossref]

C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009).
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Saunders, R.

Scarfe, W. C.

W. C. Scarfe and A. G. Farman, “What is cone-beam CT and how does it work?” Dent. Clin. N. Am. 52, 707–730 (2008).
[Crossref] [PubMed]

Shaw, C. C.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Siewerdsen, J. H.

A. R. Pineda, D. J. Tward, A. Gonzalez, and J. H. Siewerdsen, “Beyond noise power in 3D computed tomography: the local NPS and off-diagonal elements of the Fourier domain covariance matrix,” Med. Phys. 39, 3240–3252 (2012).
[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]

D. J. Tward and J. H. Siewerdsen, “Noise aliasing and the 3D NEQ of flat-panel cone-beam CT: effect of 2D/3D apertures and sampling,” Med. Phys. 36, 3830–3843 (2009).
[Crossref] [PubMed]

J. D. Silverman, N. S. Paul, and J. H. Siewerdsen, “Investigation of lung nodule detectability in low-dose 320-slice computed tomography,” Med. Phys. 36, 1700–1710 (2009).
[Crossref] [PubMed]

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[Crossref] [PubMed]

J. H. Siewerdsen and D. A. Jaffray, “Three-dimensional NEQ transfer characteristics of volume CT using direct-and indirect-detection flat-panel imagers,” Proc. SPIE5030, 92–102 (2003).
[Crossref]

D. J. Tward, J. H. Siewerdsen, R. A. Fahrig, and A. R. Pineda, “Cascaded systems analysis of the 3D NEQ for cone-beam CT and tomosynthesis,” Proc. SPIE6913, 69131S (2008).
[Crossref]

Silverman, J. D.

J. D. Silverman, N. S. Paul, and J. H. Siewerdsen, “Investigation of lung nodule detectability in low-dose 320-slice computed tomography,” Med. Phys. 36, 1700–1710 (2009).
[Crossref] [PubMed]

Sloan, J. A.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

Spin-Neto, R.

R. Spin-Neto, E. Gotfredsen, and A. Wenzel, “Impact of voxel size variation on CBCT-based diagnostic outcome in dentistry: a systematic review,” J. Dig. Imag. 26, 813–820 (2013).
[Crossref]

Suess, C.

M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. simulation studies,” Med. Phys. 26, 2235–2247 (1999).
[Crossref] [PubMed]

Swensen, S. J.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
[Crossref] [PubMed]

Sykes, A. M.

S. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, “Lung cancer screening with CT: Mayo clinic experience 1,” Radiology 226, 756–761 (2003).
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Tang, X.

J. Hsieh and X. Tang, “Tilted cone-beam reconstruction with row-wise fan-to-parallel rebinning,” Phys. Med. Biol. 51, 5259–5276 (2006).
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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]

Thompson, A. J.

L. Wang, H. M. Lai, A. J. Thompson, and D. H. Miller, “Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load,” J. Neurol. Neurosur. Ps. 63, 452–455 (1997).
[Crossref]

Tu, S. J.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Tward, D. J.

A. R. Pineda, D. J. Tward, A. Gonzalez, and J. H. Siewerdsen, “Beyond noise power in 3D computed tomography: the local NPS and off-diagonal elements of the Fourier domain covariance matrix,” Med. Phys. 39, 3240–3252 (2012).
[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]

D. J. Tward and J. H. Siewerdsen, “Noise aliasing and the 3D NEQ of flat-panel cone-beam CT: effect of 2D/3D apertures and sampling,” Med. Phys. 36, 3830–3843 (2009).
[Crossref] [PubMed]

D. J. Tward, J. H. Siewerdsen, M. J. Daly, S. Richard, D. J. Moseley, D. A. Jaffray, and N. S. Paul, “Soft-tissue detectability in cone-beam CT: Evaluation by 2AFC tests in relation to physical performance metrics,” Med. Phys. 34, 4459–4471 (2007).
[Crossref] [PubMed]

D. J. Tward, J. H. Siewerdsen, R. A. Fahrig, and A. R. Pineda, “Cascaded systems analysis of the 3D NEQ for cone-beam CT and tomosynthesis,” Proc. SPIE6913, 69131S (2008).
[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]

Verdun, F. R.

Wang, L.

L. Wang, H. M. Lai, A. J. Thompson, and D. H. Miller, “Survey of the distribution of lesion size in multiple sclerosis: implication for the measurement of total lesion load,” J. Neurol. Neurosur. Ps. 63, 452–455 (1997).
[Crossref]

Wang, T.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Wells, R. G.

H. C. Gifford, M. A. King, P. H. Pretorius, and R. G. Wells, “A comparison of human and model observers in multislice lroc studies,” IEEE Trans. Med. Img. 24, 160–169 (2005).
[Crossref]

Wenzel, A.

R. Spin-Neto, E. Gotfredsen, and A. Wenzel, “Impact of voxel size variation on CBCT-based diagnostic outcome in dentistry: a systematic review,” J. Dig. Imag. 26, 813–820 (2013).
[Crossref]

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M. P. Eckstein, J. L. Bartroff, C. K. Abbey, J. S. Whiting, and F. O. Bochud, “Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks,” Opt. Express. 11, 460–475 (2003).
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C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[Crossref] [PubMed]

Wilson, J. M.

B. Chen, O. Christianson, J. M. Wilson, and E. Samei, “Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods,” Med. Phys. 41, 071909 (2014).
[Crossref] [PubMed]

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D. Wormanns, S. Diederich, M. G. Lentschig, F. Winter, and W. Heindel, “Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size,” Eur. Radiol. 10, 710–713 (2000).
[Crossref] [PubMed]

Witten, J. M.

J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Img. 29, 1050–1058 (2010).
[Crossref]

Wolf, H.

M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. simulation studies,” Med. Phys. 26, 2235–2247 (1999).
[Crossref] [PubMed]

Wormanns, D.

D. Wormanns, S. Diederich, M. G. Lentschig, F. Winter, and W. Heindel, “Spiral CT of pulmonary nodules: interobserver variation in assessment of lesion size,” Eur. Radiol. 10, 710–713 (2000).
[Crossref] [PubMed]

Wunderlich, A.

A. Wunderlich and F. Noo, “Estimation of channelized Hotelling observer performance with known class means or known difference of class means,” IEEE Trans. Med. Img. 28, 1198–1207 (2009).
[Crossref]

Xing, Y.

J. Oldan, S. Kulkarni, Y. Xing, P. Khurd, and G. Gindi, “Channelized hotelling and human observer study of optimal smoothing in SPECT MAP reconstruction,” IEEE Trans. Nucl. Sci. 51, 733–741 (2004).
[Crossref]

Yadava, G.

S. Richard, X. Li, G. Yadava, and E. Samei, “Predictive models for observer performance in ct: applications in protocol optimization,” Proc. SPIE 7961, 79610H (2011).
[Crossref]

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A. L. C. Kwan, J. M. Boone, K. Yang, and S. Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner,” Med. Phys. 34, 275–281 (2007).
[Crossref] [PubMed]

Yang, W. T.

C. J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S. J. Tu, “Visibility of microcalcification in cone beam breast CT: effects of x-ray tube voltage and radiation dose,” Med. Phys. 34, 2995–3004 (2007).
[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]

Yu, L.

L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (2013).
[Crossref] [PubMed]

Zhang, Y.

Y. Zhang, B. T. Pham, and M. P. Eckstein, “Evaluation of internal noise methods for Hotelling observer models,” Med. Phys. 34, 3312–3322 (2007).
[Crossref] [PubMed]

Y. Zhang, B. T. Pham, and M. P. Eckstein, “The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds,” IEEE Trans. Med. Img. 25, 1348–1362 (2006).
[Crossref]

Zhou, L.

S. Kulkarni, P. Khurd, I. Hsiao, L. Zhou, and G. Gindi, “A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors,” Phys. Med. Biol. 52, 3601–3617 (2007).
[Crossref] [PubMed]

Zisman, G.

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
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Zuiani, C.

A. Linda, C. Zuiani, V. Londero, and M. Bazzocchi, “Outcome of initially only magnetic resonance mammography-detected findings with and without correlate at second-look sonography: distribution according to patient history of breast cancer and lesion size,” The Breast 17, 53–59 (2008).
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Acad. Radiol. (1)

A. Badano and B. D. Gallas, “Detectability decreases with off-normal viewing in medical liquid crystal displays,” Acad. Radiol. 13, 210–218 (2006).
[Crossref] [PubMed]

Am. J. Roentgenol. (2)

R. F. Brem, J. W. Hoffmeister, G. Zisman, M. P. DeSimio, and S. K. Rogers, “A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size,” Am. J. Roentgenol. 184, 893–896 (2005).
[Crossref]

L. Liberman, G. Mason, E. A. Morris, and D. D. Dershaw, “Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size,” Am. J. Roentgenol. 186, 426–430 (2006).
[Crossref]

Chest J. (1)

M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost, “Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines,” Chest J. 132, 108S–130S (2007).
[Crossref]

Dent. Clin. N. Am. (1)

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Figures (10)

Fig. 1
Fig. 1 Reconstructed sphere images from 1 mm diameter (left) to 8 mm diameter (right) in the (a) transverse and (b) longitudinal planes. Unit of the reconstructed values is cm−1 and the display window is [00.05].

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Fig. 2
Fig. 2 Noise structures in the (a) transverse and (b) longitudinal planes. Unit of the reconstructed values is cm−1 and the display window is [−0.2 0.2].

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Fig. 3
Fig. 3 2AFC detection task for human observer study.

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Fig. 4
Fig. 4 128 × 128 LG channels with Nc=7 and au=8 from p=0 (left) to p=6 (right). The Gaussian in the first channel is similar to 1 mm diameter signal.

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Fig. 5
Fig. 5 128 × 128 DOG channels from j=1 (left) to j=7 (right).

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Fig. 6
Fig. 6 (a) Averaged Pc with 95% confidence interval in the transverse and longitudinal planes. (b) Comparison of task SNR in the transverse and longitudinal planes. (c) The ratio of averaged task SNR between the transverse and longitudinal planes.

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Fig. 7
Fig. 7 (a) 2-D NPS of transverse plane and longitudinal plane, (b) the first 2000 × 2000 zoomed-in version of the 16384 × 16384 full covariance matrix in the transverse and longitudinal planes, (c) central profile of corresponding NPS.

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Fig. 8
Fig. 8 Sampled (a) transverse and (b) longitudinal plane images with different sphere sizes from 1 mm diameter (left) to 8 mm diameter (right). Unit of the reconstructed values is cm−1 and the display window is [−0.3 0.3]. The image size is 15.2 × 15.2 mm2.

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Fig. 9
Fig. 9 SNR of the LG CHO as a function of Nc in the transverse and longitudinal planes.

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Fig. 10
Fig. 10 SNR and Pc of the LG CHO, DOG CHO, and human observers in the transverse and longitudinal planes.

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Tables (4)

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Table 1 Simulation parameters

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Table 2 au for each signal size (pixels). Pixel size is 0.119 mm

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Table 3 Human efficiency

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Table 4 Relative dose efficiency of the two planes

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Equations (11)

Equations on this page are rendered with MathJax. Learn more.

H 0 : g = b + n
H 1 : g = s + b + n
P c = 1 N t ∑ i = 1 N t o i
u p ( r | a u ) = 2 a u e x p ( − 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 ) = e x p [ − 1 2 ( ρ Q σ j ) 2 ] − e x p [ − 1 2 ( ρ σ j ) 2 ]
SNR C H O = [ Δ g t T ( T t K g T + K ε ) − 1 T t Δ g ] 1 / 2
SNR h u m a n = 2 ⋅ e r f − 1 ( 2 ⋅ AUG − 1 )
K ε = p ⋅ d i a g ( K v )
SNR h u m a n 2 SNR L G C H O 2
SNR l o n g i t u d i n a l 2 SNR t r a n s v e r s e 2

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