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.
Highlights
Objective assessment of image quality through task-specific metrics has a long history in medical imaging and is regarded by many as being the most meaningful approach to medical image evaluation.[1,2,3,4] the application of taskbased assessment to x-ray computed tomography (CT) is recent relative to its application to planar imaging modalities and nuclear medicine
The results of applying the region of interest (ROI)-Hotelling observer (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 0.75νN to 0.825νN for each task
Summary
Objective assessment of image quality through task-specific metrics has a long history in medical imaging and is regarded by many as being the most meaningful approach to medical image evaluation.[1,2,3,4] the application of taskbased assessment to x-ray computed tomography (CT) is recent relative to its application to planar imaging modalities and nuclear medicine. One reason for this delay is that metrics based on the Hotelling observer (HO),[3] such as those considered in this work, involve the image covariance matrix, and in CT, this matrix is often extremely large (well over 109 elements), poorly conditioned, and possesses few, if any, simplifying structural properties.
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