Compressive Thermal Neutron Imaging using an MC-15

We demonstrate the feasibility of applying compressive sensing techniques to thermal neutron imaging with the MC-15 neutron multiplicity counter, where a small number of measurements (as low as 13 measurements) encode sufficient information to generate an image. We design a random pattern mask that, when placed in front of the MC-13 3He proportional tubes, modulates the thermal neutron signal encoding the 2D distribution of the thermal neutron source. We model the “mask+MC-15” system using a ray-tracing code along with particle transport codes, including MCNP and Geant4. Images reconstructed with a Maximum Likelihood Expectation Maximization (MLEM) algorithm show that this approach can localize sources in the field-of-view (FOV) as well as differentiate them by size. We also show that we can reduce the required infrastructure by rotating a single random mask by 90 degrees (up to 3 times) to obtain independent measurements, and that this increase in information correlates with image quality improvement.