Mathematical model observers have found an important place in the objective evaluation of medical images since they are better predictors of human observer performance than the traditional measures of image quality such as image resolution, variance, contrast, or mean square error.^{1} The ideal observer (IO) and the Hotelling observer (HO) are examples of widely used model observers. In a binary classification task, the IO requires the full knowledge of the probability density functions (PDFs) of the input data under both hypotheses. Determining these PDFs is challenging when the input data are realistic medical images from a patient population. The HO is a linear classifier and can thus be used as an alternative to the IO, requiring only the knowledge of the first- and second-order statistics of the image data.^{1}^{–}^{7} It is the optimal linear discriminant and has performance equal to the IO under certain conditions (see below). Due to its simplicity, the HO has been extensively used in medical imaging to assess image quality.^{1}^{–}^{4} However, the HO tends to outperform the human observer in the presence of correlated noise.^{8} Thus, the channelized Hotelling observer (CHO) has been proposed, where a frequency-selective channel mechanism is often applied to more closely approximate the performance of the IO^{9}^{,}^{10} or the human observer,^{11}^{–}^{14} depending on the choice of the channel model. In addition, the use of a small number of channels reduces the dimensionality of the observer.^{1} Several studies have shown that the CHO, with an appropriate channel model, can successfully predict human observer performance in the case of signal known exactly and background known exactly (SKE/BKE) detection task using simulated images^{11} and using realistic single-photon emission computed tomography (SPECT) images.^{14} Moreover, the CHO is a good predictor of the human observer in the case of signal known exactly and background known statistically (SKE/BKS) tasks.^{15}^{,}^{16} The signal known statistically and background known statistically (SKS/BKS) task poses limits to the CHO methodology as discussed in Park et al.^{9}^{,}^{16}^{,}^{17} An example of SKS tasks is presented in Ref. ^{1}, where Barrett and Myers discussed the effect of signal variability on the HO performance and presented an example of signal location variability, showing that the data from the defect-present class can follow a non-normal distribution as well as multimodal patterns. They have also proposed the concept of model observers for a signal known exactly but variable (SKEV) task to approximate performance in the SKS tasks. This concept has been further discussed by Eckstein et al.^{18}^{,}^{19}