Over time, nuclear materials can accumulate in process equipment and keeping track of such material is of interest to many stakeholder communities including safeguards, process monitoring, criticality safety, etc. Passive gamma-ray imaging provides a visual and quantitative  tool that can be applied to this problem, allowing one to determine the amount of material present from the images. In this work we emulate the accumulation of highly enriched uranium (HEU) in a large rectangular stainless-steel duct used as a mock-up for uranium process equipment. The HEU is distributed on 50 × 75 cm2 cards (as U02), with each card having ~ 11 g of 235U. A total of 5 cards were used, with the cards stacked one behind the other within the duct. A series of images were collected, starting with a single card, adding a second card, etc. Long integrations were collected to allow evaluating the performance of the approach as a function of integration time. Data were collected using a coded-aperture gamma-ray imager comprising a position-sensitive GeGI-5TM high-purity germanium detector outfitted with a rank-19 modified uniformly redundant array coded-aperture mask. The mask is ~ 50% open area and encodes the scene onto the detector as a shadowgram. Inverting the shadowgram for each energy bin results in a hyperspectral image that has a full spectrum for each pixel of the image. The images are analyzed using first principles  and using an iterative Monte Carlo inverse solver  to determine the amount of material in each image. The results from the two techniques are compared to each other, to the known amount of material in each configuration, and as a function of time. Both techniques provide a statistical error analysis and those will be compared as well. To further evaluate the uncertainty, a boot-strap sampling approach is also applied to the data to map the uncertainty distribution. The work offers a proof of concept for unattended monitoring of uranium holdup with a quantitative gamma-ray imaging technique. K.P. Ziock, et al, Joint INMM ESARDA Annual Meeting, August, 2021 R. Venkataraman, et al. Nucl. Inst. Meth. A, submitted, 2022.