Random mask optimization for fast neutron coded aperture imaging

Abstract

In coded aperture imaging, one of the most important factors determining the quality of reconstructed images is the choice of mask/aperture pattern. In many applications, uniformly redundant arrays (URAs) are widely accepted as the optimal mask pattern. Under ideal conditions, thin and highly opaque masks, URA patterns are mathematically constructed to provide artifact-free reconstruction however, the number of URAs for a chosen number of mask elements is limited and when highly penetrating particles such as fast neutrons and high-energy gamma-rays are being imaged, the optimum is seldom achieved. In this case more robust mask patterns that provide better reconstructed image quality may exist. Through the use of heuristic optimization methods and maximum likelihood expectation maximization (MLEM) image reconstruction, we show that for both point and extended neutron sources a random mask pattern can be optimized to provide better image quality than that of a URA.