Abstract
In nuclear security applications, coded-aperture imagers provide the opportu-
nity for a wealth of information regarding the attributes of both the radioac-
tive and non-radioactive components of the objects being imaged. However,
for optimum benefit to the community, spatial attributes need to be deter-
mined in a quantitative and statistically meaningful manner. To address the
deficiency of quantifiable errors in coded-aperture imaging, we present uncer-
tainty matrices containing covariance terms between image pixels for MURA
mask patterns. We calculated these correlated uncertainties as functions of
variation in mask rank, mask pattern over-sampling, and whether or not anti-
mask data are included. Utilizing simulated point source data, we found that
correlations (and inverse correlations) arose when two or more image pixels
were summed. Furthermore, we found that the presence of correlations (and
their inverses) was heightened by the process of over-sampling, while correla-
tions were suppressed by the inclusion of anti-mask data and with increased
mask rank. As an application of this result, we explore how statistics-based
alarming in nuclear security is impacted.
