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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.


A comparison of optimization of reconstruction filter width for 50 projection views. Shown are results obtained with the proposed ROI-HO, an approximate HO computed using the discrete Fourier transform (labeled FHO), and the CHO. The curves in (a) correspond to the detection task, while (b) corresponds to the Rayleigh task.
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f6: A comparison of optimization of reconstruction filter width for 50 projection views. Shown are results obtained with the proposed ROI-HO, an approximate HO computed using the discrete Fourier transform (labeled FHO), and the CHO. The curves in (a) correspond to the detection task, while (b) corresponds to the Rayleigh task.

Mentions: FigureĀ 6 compares three approaches to optimization of the reconstruction filter width parameter, namely the ROI-HO approach proposed in this work, the CHO with 50 LG channels, and the DFT-domain approach (labeled FHO). Since each of the methods utilizes the same ROI images, the ROI-HO that computes the exact HO performance should be taken as the ground truth for the sake of evaluating the alternative approaches. Clearly, for microcalcification detection, the CHO is an excellent approximation of the true HO. Further, the CHO allows for greater computation efficiency, since, in general, the ROIs used contained . However, the efficiency of this CHO comes with a loss of generality, as it is not applicable to general signals lacking radial symmetry, as in the Rayleigh discrimination task.


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)

A comparison of optimization of reconstruction filter width for 50 projection views. Shown are results obtained with the proposed ROI-HO, an approximate HO computed using the discrete Fourier transform (labeled FHO), and the CHO. The curves in (a) correspond to the detection task, while (b) corresponds to the Rayleigh task.
© Copyright Policy
Related In: Results  -  Collection

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

f6: A comparison of optimization of reconstruction filter width for 50 projection views. Shown are results obtained with the proposed ROI-HO, an approximate HO computed using the discrete Fourier transform (labeled FHO), and the CHO. The curves in (a) correspond to the detection task, while (b) corresponds to the Rayleigh task.
Mentions: FigureĀ 6 compares three approaches to optimization of the reconstruction filter width parameter, namely the ROI-HO approach proposed in this work, the CHO with 50 LG channels, and the DFT-domain approach (labeled FHO). Since each of the methods utilizes the same ROI images, the ROI-HO that computes the exact HO performance should be taken as the ground truth for the sake of evaluating the alternative approaches. Clearly, for microcalcification detection, the CHO is an excellent approximation of the true HO. Further, the CHO allows for greater computation efficiency, since, in general, the ROIs used contained . However, the efficiency of this CHO comes with a loss of generality, as it is not applicable to general signals lacking radial symmetry, as in the Rayleigh discrimination task.

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.