Improvement on depth resolution and reduction of Poisson noise in coded aperture emission CT

We applied a maximum likelihood expectation maximization (ML-EM) method to the reconstruction in coded aperture emission computed tomography (CECT) for the measurement of 3D distribution of a radioactive isotope, where the projection data have a statistical fluctuation obeying a Poisson distribution (Poisson noise). The problem to be solved for CECT is to improve the depth resolution. We made some numerical simulations to confirm that the improvement of depth resolution and the reduction of noise were remarkable. For a point emitter at a distance of 35 cm from the aperture, the depth resolution was about 4 mm even when the average number of photons per pixel in the projection was 10. The quality of images for some 3D objects reconstructed by ML-EM was much better than that by conventional methods