The Center for Gamma-Ray Imaging is developing a number of small-animal SPECT imaging systems. These systems consist of multiple stationary detectors, each of which has its own multiple-pinhole collimator. The location of the pinhole plates (i.e., magnification), the number of pinholes within each plate, as well the pinhole locations are all adjustable. The performance of the Bayesian ideal observer sets the upper limit on task performance and can be used to optimize imaging hardware, such as pinhole configurations. Markov-chain Monte Carlo techniques have been developed to compute the ideal observer but require complete knowledge of the statistics of both the imaging system (such as the noise) and the class of random objects being imaged, in addition to an accurate forward model connecting the object to the image. Ideal observer computations using Monte Carlo techniques are burdensome because the forward model must be simulated millions of times for each imaging system. We present an efficient technique for computing the Bayesian ideal observer for multiple-pinhole, small-animal SPECT systems that accounts for both the finite-size of the pinholes and the stochastic nature of the objects being imaged. This technique relies on an efficient, radiometrically correct forward model that maps an object to an image in less than 20 milliseconds. An analysis of the error of the forward model, as well as the results of a ROC study using the ideal observer test statistic is presented.