Computational and Applied Math Proseminar
Joint with Mathematical and Computational Aspects of Brain Imaging Seminar

Department of Mathematics, Arizona State University

Thursday, April 8, 1999, 12:15 p.m. in GWC Room 604

C.C. Chen

Department of Mathematics

Iterative MLE Reconstruction for Positron Emission Tomography

Abstract Image reconstruction methods fall broadly into one of the two categories. Fourier methods approximate explicit deterministic inversion formulas for reconstructing a function from its line integrals. On the other hand, algebraic reconstruction techniques can capture stochastic variation in photon counts and, in theory, yield more accurate reconstructions or provide equivalent reconstructions with lower patient radiation dose. Because algebraic techniques are usually iterative, and therefore slower, the single step Fourier-based methods are generally preferred in practice. With the advent of more powerful computers, the arguments favoring algebraic reconstruction become more compelling. As in most algebraic schemes, the region to be reconstructed is divided into small pixels. The photon counting in each pixel is assumed as a Poisson distribution. The Maximum Likelihood Estimator(MLE) algorithms for image reconstruction in Positron Emission Tomography(PET) are iterative techniques for finding maximum likelihood estimates. For further information please contact: mittelmann@asu.edu