Often, the HO’s performance for a given task is estimated using a collection of sample images, resulting in statistically variable estimates of the HO’s figure of merit. Since we wish to perform optimization of several reconstruction parameters, for efficiency, we construct an approximation of HO performance which does not rely on samples of noisy images, and is therefore nonstochastic. Most previous studies in constructing nonstochastic HO performance estimates rely on channels,^{12} such as Laguerre–Gauss channels,^{13} which rely on assumptions of symmetry in the signal of interest in order to reduce the dimensionality of the HO metric computation. Instead, in this work the HO signal-to-noise ratio (SNR) is obtained in the spatial domain without the use of channels. Rather, extending previous work,^{14}^{–}^{16} the dimensionality of the HO system of equations is reduced by considering microcalcification detection only within a restricted region of interest (ROI). Meanwhile, the effect of reconstruction on the signal and quantum noise is captured by explicitly modeling the effect of each linear operation on a Gaussian detector noise model. We demonstrate that the proposed method can enable efficient simultaneous optimization of multiple reconstruction parameters. This is potentially useful for DBT system development, where the interplay between various reconstruction parameters and their collective effect on image quality can be difficult to predict a priori. This method, in combination with more conventional assessment methods (e.g., artifact evaluation), can provide a fuller picture of the utility of a given algorithm implementation for DBT.