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Region of interest based Hotelling observer for computed tomography with comparison to alternative methods.

Sanchez AA, Sidky EY, Pan X - J Med Imaging (Bellingham) (2014)

Bottom Line: This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible.Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images.Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

View Article: PubMed Central - PubMed

Affiliation: The University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60615, United States.

ABSTRACT

We compare several approaches to estimation of Hotelling observer (HO) performance in x-ray computed tomography (CT). We consider the case where the signal of interest is small so that the reconstructed image can be restricted to a small region of interest (ROI) surrounding the signal. This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible. We propose that this approach is directly applicable to systems optimization in CT; however, many alternative approaches exist, which make computation of HO performance tractable through a range of approximations, assumptions, or estimation strategies. Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images. Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

No MeSH data available.


Dependency of channelized Hotelling observer (CHO) efficiency on number of channels for a range of reconstruction filter widths from 0.125 to 0.5 (a), and 1.0 to the (unapodized) ramp filter (b), in units of the detector Nyquist frequency. For most filter widths, the CHO efficiency stabilizes with 50 channels; however, the performance estimates for this task and system are not sensitive to the number of channels used, in general. Subsequent results shown are for 50 channels.
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f4: Dependency of channelized Hotelling observer (CHO) efficiency on number of channels for a range of reconstruction filter widths from 0.125 to 0.5 (a), and 1.0 to the (unapodized) ramp filter (b), in units of the detector Nyquist frequency. For most filter widths, the CHO efficiency stabilizes with 50 channels; however, the performance estimates for this task and system are not sensitive to the number of channels used, in general. Subsequent results shown are for 50 channels.

Mentions: Next, we consider the use of channels for estimating HO performance in microcalcification detection. As with the ROI-HO, we restrict the CHO to an ROI, however, due to the Gaussian envelope that modulates the LG channels, this only has an effect for the smallest ROIs used. For the CHO using LG channels, results correspond solely to the microcalcification task, since the Rayleigh task involves a signal that is not radially symmetric. FiguresĀ 4 and 5 demonstrate the dependence of CHO efficiency on the number of channels and the scale factor that modulates the width of the Gaussian envelope of the LG functions. For the majority of filter widths considered, the CHO efficiency estimate is completely stable above channels, while the optimum scale factor of the channels (full width at half maximum of the Gaussian) is roughly seven times the width of the microcalcification diameter. The remainder of the results presented corresponds to these CHO parameters. In our case, however, the dependence of the CHO performance estimates upon each of these parameters is weak, so that fewer channels or a slightly different scale factor could likely produce comparable results.


Region of interest based Hotelling observer for computed tomography with comparison to alternative methods.

Sanchez AA, Sidky EY, Pan X - J Med Imaging (Bellingham) (2014)

Dependency of channelized Hotelling observer (CHO) efficiency on number of channels for a range of reconstruction filter widths from 0.125 to 0.5 (a), and 1.0 to the (unapodized) ramp filter (b), in units of the detector Nyquist frequency. For most filter widths, the CHO efficiency stabilizes with 50 channels; however, the performance estimates for this task and system are not sensitive to the number of channels used, in general. Subsequent results shown are for 50 channels.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4326074&req=5

f4: Dependency of channelized Hotelling observer (CHO) efficiency on number of channels for a range of reconstruction filter widths from 0.125 to 0.5 (a), and 1.0 to the (unapodized) ramp filter (b), in units of the detector Nyquist frequency. For most filter widths, the CHO efficiency stabilizes with 50 channels; however, the performance estimates for this task and system are not sensitive to the number of channels used, in general. Subsequent results shown are for 50 channels.
Mentions: Next, we consider the use of channels for estimating HO performance in microcalcification detection. As with the ROI-HO, we restrict the CHO to an ROI, however, due to the Gaussian envelope that modulates the LG channels, this only has an effect for the smallest ROIs used. For the CHO using LG channels, results correspond solely to the microcalcification task, since the Rayleigh task involves a signal that is not radially symmetric. FiguresĀ 4 and 5 demonstrate the dependence of CHO efficiency on the number of channels and the scale factor that modulates the width of the Gaussian envelope of the LG functions. For the majority of filter widths considered, the CHO efficiency estimate is completely stable above channels, while the optimum scale factor of the channels (full width at half maximum of the Gaussian) is roughly seven times the width of the microcalcification diameter. The remainder of the results presented corresponds to these CHO parameters. In our case, however, the dependence of the CHO performance estimates upon each of these parameters is weak, so that fewer channels or a slightly different scale factor could likely produce comparable results.

Bottom Line: This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible.Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images.Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

View Article: PubMed Central - PubMed

Affiliation: The University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60615, United States.

ABSTRACT

We compare several approaches to estimation of Hotelling observer (HO) performance in x-ray computed tomography (CT). We consider the case where the signal of interest is small so that the reconstructed image can be restricted to a small region of interest (ROI) surrounding the signal. This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible. We propose that this approach is directly applicable to systems optimization in CT; however, many alternative approaches exist, which make computation of HO performance tractable through a range of approximations, assumptions, or estimation strategies. Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images. Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

No MeSH data available.