Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Department of Engineering Physics

First Advisor

James Bevins, PhD


While recent studies have demonstrated the directional capabilities of the single-detector rotating scatter mask (RSM) system for discrete, dual-particle environments, there has been little progress towards adapting it as a true imaging device. In this research, two algorithms were developed and tested using an RSM mask design previously optimized for directional detection and simulated 137Cs signals from a variety of source distributions. The first, maximum-likelihood expectation-maximization (ML-EM), was shown to generate noisy images, with relatively low accuracy (145% average relative error) and signal-to-noise ratio (0.27) for most source distributions simulated. The second, a novel regenerative neural network (ReGeNN), performed exceptionally well, with significantly higher accuracy (33\% average relative error) over all source types compared to ML-EM and drastically improved signal-to-noise ratio (0.85) in the reconstructed images. The imaging capabilities of ReGeNN were then experimentally validated using an additively-manufactured mask. Measuring two point and one ring 22Na source distributions, a modified ReGeNN was able to successfully train on simulated noisy signals and accurately predict the relative size and direction of the three sources. To support future design optimizations to overcome current limitations of the current mask design, a ray tracing algorithm was also developed as an alternative to more rigorous Monte Carlo RSM simulations. This ray tracing code was shown to significantly improve computational efficiency, at a slight cost to the simulated signal accuracy for more complex mask designs.

AFIT Designator


DTIC Accession Number