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A parametric Bayesian RMC gamma-ray image reconstruction

Rotational modulation collimation (RMC) is a technique commonly used for standoff imaging of radiological sources in the context of homeland security. The paper presents a novel method for gamma ray image reconstruction from modulation signals acquired by a RMC detector prototyped by DSTO. The image is represented in a parametric form as a weighted sum of Gaussian radial basis functions. The problem is thus formulated as a parameter estimation problem and solved in the Bayesian framework using a multi-stage Monte Carlo technique known as progressive correction. A comparison with EM and MAP image reconstruction algorithms is provided.