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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

Efficiency values are shown for various views and Hanning window widths (relative to the Nyquist frequency on the detector) for both the detection task (a) and the Rayleigh task (b). The Nyquist frequency in this case is . While the efficiency values for moderate filtering to no filtering were seen to have a dependence on ROI size, the same trend pictured here was seen for ROI sizes up to  diameter.
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f3: Efficiency values are shown for various views and Hanning window widths (relative to the Nyquist frequency on the detector) for both the detection task (a) and the Rayleigh task (b). The Nyquist frequency in this case is . While the efficiency values for moderate filtering to no filtering were seen to have a dependence on ROI size, the same trend pictured here was seen for ROI sizes up to diameter.

Mentions: The results of applying the ROI-HO for microcalcification detection and Rayleigh discrimination are shown in Fig. 3 for a range of Hanning filter widths. The ROI-HO is noticeably sensitive to the reconstruction filter width, showing a clear maximum in performance for Hanning windows in the range of to for each task. However, this result should not be interpreted as giving a universally optimal filter width, but rather as a demonstration of the sensitivity of HO efficiency to relevant reconstruction algorithm parameters.


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)

Efficiency values are shown for various views and Hanning window widths (relative to the Nyquist frequency on the detector) for both the detection task (a) and the Rayleigh task (b). The Nyquist frequency in this case is . While the efficiency values for moderate filtering to no filtering were seen to have a dependence on ROI size, the same trend pictured here was seen for ROI sizes up to  diameter.
© Copyright Policy
Related In: Results  -  Collection

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

f3: Efficiency values are shown for various views and Hanning window widths (relative to the Nyquist frequency on the detector) for both the detection task (a) and the Rayleigh task (b). The Nyquist frequency in this case is . While the efficiency values for moderate filtering to no filtering were seen to have a dependence on ROI size, the same trend pictured here was seen for ROI sizes up to diameter.
Mentions: The results of applying the ROI-HO for microcalcification detection and Rayleigh discrimination are shown in Fig. 3 for a range of Hanning filter widths. The ROI-HO is noticeably sensitive to the reconstruction filter width, showing a clear maximum in performance for Hanning windows in the range of to for each task. However, this result should not be interpreted as giving a universally optimal filter width, but rather as a demonstration of the sensitivity of HO efficiency to relevant reconstruction algorithm parameters.

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