Abstract
Statistically-based iterative image reconstruction is widely used in emission computed tomography. This paper explains the mathematical foundations and computational techniques in statistical image reconstruction through the formulation and solution of the maximum-likelihood (ML) estimation problem, and introduces some pioneering techniques as extensions of the ML estimation.