title: 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.
authors.affiliation: Department of Engineering, Weber State University, Ogden, Utah, and Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology, University of Utah, Salt Lake City, Utah.
authors.forename: Gengsheng L
authors.initials: GL
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D059629:Signal-To-Noise Ratio; D015899:Tomography, Emission-Computed, Single-Photon
keywords: ML-EM algorithm; Poisson noise; filtered backprojection algorithm; image reconstruction