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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 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.
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f7: 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.

Mentions: Figure 7 demonstrates the use of noisy sample images to estimate HO efficiency when the mean image under each hypothesis is known. In our case, since we consider only linear image reconstruction algorithms, the mean image is produced simply by reconstructing an image without noise. The left and right plots correspond, respectively, to the use of independent noisy data sets for each filter width and the reuse of the same simulated data for each filter width. The variability seen in the left-hand figure provides intuition as to the variance of the efficiency estimates, while the figure on the right illustrates that, although subject to the same variability, reusing the same data realizations correlates the estimates for different filter widths, potentially allowing for more reliable rank ordering of parameter settings. In other words, the estimated curves on the right could undergo vertical translation due to the variance of the estimator, but are more likely to preserve their shape than the curves shown on the left.


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 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.
© Copyright Policy
Related In: Results  -  Collection

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

f7: 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.
Mentions: Figure 7 demonstrates the use of noisy sample images to estimate HO efficiency when the mean image under each hypothesis is known. In our case, since we consider only linear image reconstruction algorithms, the mean image is produced simply by reconstructing an image without noise. The left and right plots correspond, respectively, to the use of independent noisy data sets for each filter width and the reuse of the same simulated data for each filter width. The variability seen in the left-hand figure provides intuition as to the variance of the efficiency estimates, while the figure on the right illustrates that, although subject to the same variability, reusing the same data realizations correlates the estimates for different filter widths, potentially allowing for more reliable rank ordering of parameter settings. In other words, the estimated curves on the right could undergo vertical translation due to the variance of the estimator, but are more likely to preserve their shape than the curves shown on the left.

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.