Uncertainty identification in mass estimation applications with imaging detectors

Mass quantification using standoff gamma imaging techniques is a boon to the holdup monitoring community, but before the method is deemed acceptable as a replacement for historical gamma detection-based methods, there are several considerable sources of uncertainty that must be addressed. These uncertainty sources have received sparse coverage in the literature, but provide the bulk of the uncertainty estimated in practice outside of standard counting statistics. For imaging, there is a fundamental difference between detection/localization and mass estimation operating modalities, where detection/localization can tolerate significantly higher uncertainty because the goal is detection of a distant source, rather than the quantification of that source. Historically, quantification requires a known geometry between source and detector with a calibrated efficiency correction curve, which is well suited to laboratory practice for a stationary detector; however, this requires some corrections and assumptions (e.g., generalized geometry) for field applications, since additional degrees of freedom are present in the source-to-detector position, intervening materials, and non-uniform source distributions. The imaging detector aims to address these issues by calculating the spatial geometry of the distant source as seen by the detector, with efficiency corrected according to the source-to-detector angle. While this aim promises a better estimation accuracy than generalized geometry assumptions, additional uncertainties are added from the imaging mechanism itself. Of these uncertainties, some are readily mitigated and methods to do so are included in the discussion, while some uncertainties are specific to the imaging class of instruments and are investigated and discussed in detail. Uncertainties are categorized into two general areas: instrument design and algorithmic. Instrument design-based uncertainties include basic detector characteristics for timing and energy resolution, handling of list mode data for coincidence detection, geometric layout of and propagation of spatial uncertainty into imaging uncertainty. Algorithmic-based uncertainties discussed herein pertain to image reconstruction, the weight assignment problem from cone interactions, and the effects of spatial discretization on mass estimation. With these major sources of uncertainty identified, the path towards acceptance of imaging as a holdup monitoring method becomes clearer.