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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.


Results from Fig 7 for 700 noisy sample images are shown with error bars corresponding to two standard deviations derived from jackknifing. The error bars illustrate statistical variation, but do not account for inherent estimator bias, which can be seen by comparing with the analytically computed ROI-HO curves. It is possible that other sources of uncertainty not investigated here, aside from statistical variations, could influence these results. As in Fig. 7, independent sets of images were used in (a), while the same set of images was used for each point in (b).
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f8: Results from Fig 7 for 700 noisy sample images are shown with error bars corresponding to two standard deviations derived from jackknifing. The error bars illustrate statistical variation, but do not account for inherent estimator bias, which can be seen by comparing with the analytically computed ROI-HO curves. It is possible that other sources of uncertainty not investigated here, aside from statistical variations, could influence these results. As in Fig. 7, independent sets of images were used in (a), while the same set of images was used for each point in (b).

Mentions: Results of estimating the HO efficiency from sample images using the method proposed in Ref. 9 with 300 and 700 noisy sample images. (a) An independent set of images is used for each filter width. (b) The same noisy data realizations are used for each filter width. Error bars corresponding to two standard deviations of the estimates for 700 sample images are shown in Fig. 8.


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)

Results from Fig 7 for 700 noisy sample images are shown with error bars corresponding to two standard deviations derived from jackknifing. The error bars illustrate statistical variation, but do not account for inherent estimator bias, which can be seen by comparing with the analytically computed ROI-HO curves. It is possible that other sources of uncertainty not investigated here, aside from statistical variations, could influence these results. As in Fig. 7, independent sets of images were used in (a), while the same set of images was used for each point in (b).
© Copyright Policy
Related In: Results  -  Collection

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

f8: Results from Fig 7 for 700 noisy sample images are shown with error bars corresponding to two standard deviations derived from jackknifing. The error bars illustrate statistical variation, but do not account for inherent estimator bias, which can be seen by comparing with the analytically computed ROI-HO curves. It is possible that other sources of uncertainty not investigated here, aside from statistical variations, could influence these results. As in Fig. 7, independent sets of images were used in (a), while the same set of images was used for each point in (b).
Mentions: Results of estimating the HO efficiency from sample images using the method proposed in Ref. 9 with 300 and 700 noisy sample images. (a) An independent set of images is used for each filter width. (b) The same noisy data realizations are used for each filter width. Error bars corresponding to two standard deviations of the estimates for 700 sample images are shown in Fig. 8.

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.