Abstract
In a Maximum Likelihood approach, reconstructions of positron emissions tomography images are obtained with the iterative Expectation Maximization (EM) algorithm. After too many iterations, the reconstruction becomes too rough. In recent work, the EM algorithm was halted by a cross-validation procedure. However, at this stopping point, reconstructions still exhibited some undesirable roughness. Here, the variability of the reconstruction about its expected value is reduced by a Monte Carlo resampling scheme. For simulated data, reconstructions obtained by resampling were somewhat sharper than reconstructions obtained by a simpler linear filtering method. Real data from a FDG study is also studied. Near the boundaries, the Monte Carlo method yielded a sharper reconstruction.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Published Version
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