A reconstruction method for SPECT (single photon emission computerized tomography) that uses the maximum likelihood (ML) criterion and an iterative expectation-maximization (EM) algorithm solution is examined. The method is based on a model that incorporates the physical effects of photon statistics, nonuniform photon attenuation, and a camera-dependent point-spread response function. Reconstructions from simulation experiments are presented which illustrate the ability of the ML algorithm to correct for attenuation and point-spread. Standard filtered backprojection method reconstructions, using experimental and simulated data, are included for reference. Three studies were designed to focus on the effects of noise and point-spread, on the effect of nonuniform attenuation, and on the combined effects of all three. The last study uses a chest phantom and simulates Tl-201 imaging of the myocardium. A quantitative analysis of the reconstructed images is used to support the conclusion that the ML algorithm produces reconstructions that exhibit improved signal-to-noise ratios, improved image resolution, and image quantifiability.

Microwave remote sensing instruments such as radiometers and scatterometers have proven themselves effective in a variety of Earth Science studies. The resolution of these sensors, while adequate for many applications, is a limiting factor to their application in other studies. As a result, there is a strong interest in developing ground processing methods which can enhance the spatial resolution of the data. A number of resolution enhancement algorithms have been developed based on inverse filtering and irregular sampling reconstruction. This Chapter discusses the use of resolution enhancement and reconstruction algorithms in microwave remote sensing. While the focus is on microwave instruments, the techniques and algorithms considered are applicable to a variety of sensors, including those not originally designed for imaging.