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Certain elements (such as Fe, Cu, Zn, etc.) are vital to the body and an imbalance of such elements can either be a symptom or cause of certain pathologies. Neutron Stimulated Emission Computed Tomography (NSECT) is a spectroscopic imaging technique whereby the body is illuminated via a beam of neutrons causing elemental nuclei to become excited and emit characteristic gamma radiation. Acquiring the gamma energy spectra in a tomographic geometry allows reconstruction of elemental concentration images. Previously we have demonstrated the feasibility of NSECT using first generation CT approaches; while successful, the approach does not scale well and has limited resolution. Additionally, current gamma cameras operate in an energy range too low for NSECT imaging. However, the orbiting Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) captures and images gamma rays over the high-energy range equivalent to NSECT's (3 keV to 17 MeV) by utilizing Collimator-based Fourier transform imaging. A High Purity Germanium (HPGe) detector counts the number of energy events per unit of time, providing spectroscopic data. While a pair of rotating collimators placed in front of the detector modulates the number of gamma events, providing spatial information. Knowledge of the number of energy events at each discrete collimator angle allows for 2D image reconstruction. This method has proven successful at a focus of infinity in the RHESSI application. Our goal is to achieve similar results at a reasonable near-field focus. Here we describe the results of our simulations to implement a rotating modulation collimator (RMC) gamma imager for use in NSECT using simulations in Matlab. To determine feasible collimator setups and the stability of the inverse problem a Matlab environment was created that uses the geometry of the system to generate 1D observation data from 2D images and then to reconstruct 2D images using the MLEM algorithm. Reasonable collimator geometries were determined, successful reconstruction was achieved and the inverse problem was found to be stable.
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