Comparison of a Noise-Weighted Filtered Backprojection Algorithm with the Standard MLEM Algorithm for Poisson Noise

Abstract

Iterative maximum-likelihood expectation maximization and ordered-subset expectation maximization algorithms are excellent for image reconstruction and usually provide better images than filtered backprojection (FBP). Recently, an FBP algorithm able to incorporate noise weighting during reconstruction was developed. This paper compares the performance of the noise-weighted FBP algorithm and the iterative maximum-likelihood expectation maximization algorithm with Poisson noise–corrupted emission data generated by computer simulations and a SPECT experimental study. The results show comparable performance for these 2 algorithms.