A Multi Grid Maximum Likelihood Reconstruction Algorithm For Positron Emission Tomography

Atam P. Dhawan; M. V. Ranganath; G. Ganti; N. Mullani; K. L. Gould

doi:10.1117/12.968645

27 June 1988 A Multi Grid Maximum Likelihood Reconstruction Algorithm For Positron Emission Tomography

Atam P. Dhawan, M. V. Ranganath, G. Ganti, N. Mullani, K. L. Gould

Proceedings Volume 0914, Medical Imaging II; (1988) https://doi.org/10.1117/12.968645
Event: Medical Imaging II, 1988, Newport Beach, CA, United States

Abstract

The problem of reconstruction in Positron Emission Tomography (PET) is basically estimating the number of photon pairs emitted from the source. Using the concept of maximum likelihood (ML) algorithm, the problem of reconstruction 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 non-uniform correction efficiency of each iteration making the algorithm very sensitive to the image-pattern. An efficient knowledge-based multi-grid reconstruction algorithm based on ML approach is presented to overcome these problems.

Citation Download Citation

Atam P. Dhawan, M. V. Ranganath, G. Ganti, N. Mullani, and K. L. Gould "A Multi Grid Maximum Likelihood Reconstruction Algorithm For Positron Emission Tomography", Proc. SPIE 0914, Medical Imaging II, (27 June 1988); https://doi.org/10.1117/12.968645

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