Uranium Holdup Mass Quantification by Hybrid Gamma Imaging

Accurate quantification of uranium holdup is crucial in the efficient operation of many processing facilities involved with special nuclear material (SNM). The varying shapes and sizes of holdup deposits can make accurate quantification a challenge. The most common approach is the Generalized Geometry Holdup (GGH) method which simplifies the shape of the deposit to a point, line, or area source. Although the GGH method is quick and easy to implement, the oversimplifying assumptions can lead to systematic uncertainties as high as 50%. The research presented here is exploring gamma-ray imaging as a more accurate method of quantifying deposits. A PHDs Germanium Gamma Imager (GeGI) is used with a coded aperture mask to simultaneously collect coded-aperture and Compton images that make use of the entire gamma-ray emission spectrum from the deposits. Rather than rely on the native PHDs Compton imaging software framework, which is not accessible to users, we are creating the Compton images separately, allowing further development of a methodology for combining the two imaging modalities, determining deposit shape, and then quantitively determining deposit mass. To achieve this, the Compton back-projected cones are discretized to individual rays which can be traced through 3D space and geometries to provide supplemental source localization information. Each ray is assigned a weighting factor for its probability of originating from the source based on attenuation through the coded-aperture mask, Compton angular uncertainty, and agreement with the coded aperture image. The weighting system provides a more accurate representation of the deposit distribution by accounting for more physical factors of the detection system and shaping based off the higher angular resolution coded aperture image obtained from the same dataset. The images and data produced contain pixel-by-pixel spectra to be used in the evaluation of holdup deposit mass from a method developed at ORNL using an inverse transport solver.