NSECT sinogram sampling optimization by normalized mutual information

Abstract

Neutron Stimulated Emission Computed Tomography (NSECT) is an emerging noninvasive imaging technique that measures the distribution of isotopes from biological tissue using fast-neutron inelastic scattering reaction. As a high-energy neutron beam illuminates the sample, the excited nuclei emit gamma rays whose energies are unique to the emitting nuclei. Tomographic images of each element in the spectrum can then be reconstructed to represent the spatial distribution of elements within the sample using a first generation tomographic scan. NSECT's high radiation dose deposition, however, requires a sampling strategy that can yield maximum image quality under a reasonable radiation dose. In this work, we introduce an NSECT sinogram sampling technique based on the Normalized Mutual Information (NMI) of the reconstructed images. By applying the Radon Transform on the ground-truth image obtained from a carbon-based synthetic phantom, different NSECT sinogram configurations were simulated and compared by using the NMI as a similarity measure. The proposed methodology was also applied on NSECT images acquired using MCNP5 Monte Carlo simulations of the same phantom to validate our strategy. Results show that NMI can be used to robustly predict the quality of the reconstructed NSECT images, leading to an optimal NSECT acquisition and a minimal absorbed dose by the patient.