Resampling scheme for improving maximum likelihood reconstructions of positron emission tomography images

Kevin J. Coakley

doi:10.1117/12.175061

11 May 1994 Resampling scheme for improving maximum likelihood reconstructions of positron emission tomography images

Kevin J. Coakley

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175061
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

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.

Citation Download Citation

Kevin J. Coakley "Resampling scheme for improving maximum likelihood reconstructions of positron emission tomography images", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175061

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