Raped calculation of detectability in Bayesian SPECT

To speed up the laborious process of optimizing and comparing SPECT imaging systems, computational methods using model observers can be used to predict human performance in the context of a lesion detection task. We address the case of SPECT images reconstructed by Bayesian MAP methods and the use of a channelized Hotelling observer (CHO) for a detection task. It is possible to write a closed-form theoretical expression to evaluate an image quality figure-of-merit (FOM) for this case. The evaluation of this FOM expression is intractably computationally intensive due, it turns out, to the nature of attenuation and depth-dependent blur in SPECT. We make several approximations that make this computation tractable, and validate our new expressions using Monte-Carlo simulations.