Maximum likelihood reconstructions for rotating scatter mask imaging

Abstract

The rotating scatter mask system is capable of determining the direction of a single point gamma or neutron source. However, the point source approximation does not hold for many realistic applications, requiring more detailed reconstructions with this system. This study is the first to characterize the rotating scatter mask as a gamma imager, or camera, through a detailed analysis of the relative error, precision, noise, and convergence time as a measure of reconstruction performance. Simulated 137Cs sources and detector responses were generated in MCNP v6.1.4 and v6.2 with high fidelity and low statistical uncertainty. An analysis of variance was applied to maximum-likelihood expectation–maximization algorithms to determine the most significant factors in reconstructing the image, focusing on the source’s shape, size, and direction relative to the detector. Parameters for a Median Root Prior smoothing function were optimized to balance performance over a variety of source distributions. The algorithm performed reasonably well for point sources and for sources that spanned less than 30° in size and were located near the equatorial region of the rotating system. In most other scenarios, the algorithm either oversmoothed the image, resulting in blurry images, or completely failed to reconstruct the image. Increasing the resolution improved the reconstruction quality, while increasing the neighborhood size for the Median Root Prior reduced the image’s noise, but at a significant cost to computational efficiency. These results demonstrate that rotating scatter mask imaging is possible and introduces a potential alternative to other imagers. However, they also demonstrate that new techniques must be developed to increase the system’s performance and robustness for gamma imaging applications.