Using of a maximum-likelihood (ML) algorithm, the problem of reconstruction in positron emission tomography is reduced to determining an estimate of the emitter density that maximizes the probability of observing the actual detector count data over all possible emitter density distributions. A solution using this type of expectation-maximization (EM) algorithm with a fixed grid size is severely handicapped by the slow convergence rate, the large computation time, and the nonuniform correction efficiency of each iteration, making the algorithm very sensitive to the particular image pattern. An efficient knowledge-based multigrid reconstruction algorithm based on the ML approach is presented to overcome these problems. Results show that it performs much better than the conventional EM algorithm for the same computation effort.
|Number of pages
|ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
|Published - Jan 1 1988
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering