A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction

In this work, we focus on developing a channelized Hotelling observer (CHO) that estimates ideal linear observer performance on signal detection in images resulting from non-linear image reconstruction in computed tomography. In particular, many options on specifying the channel functions are explored. A hybrid channel model is proposed where a set of traditional Laguerre-Gauss functions are concatenated with a set of central pixel functions. This expanded channel set allows the CHO to perform robustly over a wide range of image reconstruction and system parameters. The application of this model observer to determining of the total-variation constrained least-squares algorithm yields images that are seen to favor detection of small, subtle signals.