Neural network method for localization of radioactive sources within a partially coded field-of-view in coded-aperture imaging.

Coded-aperture imagers typically have a smaller field-of-view (FOV) than in un-collimated gamma imaging systems. However, sources out of the fully coded field-of-view (FCFOV) can cause pseudo hotspots on the wrong side of an image reconstructed using the cross-correlation method. In this work, we propose a neural network method to identify and localize the sources within the partially coded field-of-view (PCFOV). The model was trained using Monte Carlo simulation data and evaluated with both simulation and experimental data. The results showed that the proposed model can identify and localize sources with good classification accuracy, low positioning error, and strong robustness to the statistical noise.

Image reconstruction using multi-energy system matrices with a scintillator-based gamma camera for nuclear security applications.

The system response of a gamma camera is dependent on the photon energy, thus the energy-dependent response function needs to be considered to improve the quality and fidelity of reconstructed images for identifying radionuclides in security applications. In this study, two reconstruction strategies using the maximum-likelihood expectation maximization (MLEM) algorithm with the multi-energy system matrices calculated by Monte Carlo simulations are proposed. The difference between the two is in data acquisition; one uses the sum of all events into a single projection image while the other sorts them into separate energy windows. Various radiation images of gamma-ray sources were simulated with a Monte Carlo code, and an actual image was acquired with a gamma camera. Both simulation and experiment results demonstrated the feasibility of the presented multi-energy reconstruction strategies in the detection of orphan sources.