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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.


Related in: MedlinePlus

(a) The integrand of Eq. (6) for the microcalcification detection task. (b) The reconstructed microcalcification signal energy as a function of radius from the signal, . In this case, the resulting region of interest (ROI) would have dimension of . The results shown here correspond to 50 projection views.
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f2: (a) The integrand of Eq. (6) for the microcalcification detection task. (b) The reconstructed microcalcification signal energy as a function of radius from the signal, . In this case, the resulting region of interest (ROI) would have dimension of . The results shown here correspond to 50 projection views.

Mentions: The integrand of Eq. (6) is shown in Fig. 2(a) for the microcalcification signal and a single Hanning filter width. In order to select the ROI size, we approximate the above integral at evenly spaced values of and set the ROI radius to the value of that minimizes . An example of the function is shown in Fig. 2(b), corresponding to the image shown in Fig. 2(a).


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) The integrand of Eq. (6) for the microcalcification detection task. (b) The reconstructed microcalcification signal energy as a function of radius from the signal, . In this case, the resulting region of interest (ROI) would have dimension of . The results shown here correspond to 50 projection views.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4326074&req=5

f2: (a) The integrand of Eq. (6) for the microcalcification detection task. (b) The reconstructed microcalcification signal energy as a function of radius from the signal, . In this case, the resulting region of interest (ROI) would have dimension of . The results shown here correspond to 50 projection views.
Mentions: The integrand of Eq. (6) is shown in Fig. 2(a) for the microcalcification signal and a single Hanning filter width. In order to select the ROI size, we approximate the above integral at evenly spaced values of and set the ROI radius to the value of that minimizes . An example of the function is shown in Fig. 2(b), corresponding to the image shown in Fig. 2(a).

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.


Related in: MedlinePlus