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/lp/spie/region-of-interest-based-hotelling-observer-for-computed-tomography-NLIHeg0G8I
- Publisher
- SPIE
- Copyright
- © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
- Subject
- Special Section Papers; Paper
- ISSN
- 2329-4302
- eISSN
- 2329-4310
- DOI
- 10.1117/1.JMI.1.3.031010
- Publisher site
- See Article on Publisher Site
Abstract
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.
Journal
Journal of Medical Imaging – SPIE
Published: Oct 1, 2014