Tomographic reconstruction technology enables three-dimensional imaging of volumes for a variety of applications, e.g., medical imaging. In some nuclear imaging applications, a radioactive substance is administered to a patient, and emitted radiation is detected with a detector system. An imaging detector detects the γ-radiation emitted from the patient and provides the data set to an image reconstruction unit, which computes an image object, e.g., a three dimensional (3D) image object, on the basis of the data set. A well-known objective statistical measure for modeling Poisson-distributed radiation counts, which is well behaved at low counts, is the Poisson cumulative distribution function (Poisson CDF). Because the CDF is piecewise constant with discontinuities at integer points, a random component may be added to provide a modified Poisson CDF (MCDF). The MCDF, although useful for its continuity and lack of inter-pixel correlation, is susceptible to sensitivity problems because it is processed one projection at a time. Furthermore, the MCDF is computed in data space (not image space) and thus does not provide meaningful insights into disparities between a predicted model and actual results in image space.