Noise properties of the EM algorithm: II. Monte Carlo simulations.
In an earlier paper we derived a theoretical formulation for estimating the statistical properties of images reconstructed using the iterative ML-EM algorithm. To gain insight into this complex problem, two levels of approximation were considered in the theory. These techniques revealed the dependence of the variance and covariance of the reconstructed image noise on the source distribution, imaging system transfer function, and iteration number. In this paper a Monte Carlo approach was taken to study the noise properties of the ML-EM algorithm and to test the predictions of the theory. The study also served to evaluate the approximations used in the theory. Simulated data from phantoms were used in the Monte Carlo experiments. The ML-EM statistical properties were calculated from sample averages of a large number of images with different noise realizations. The agreement between the more exact form of the theoretical formulation and the Monte Carlo formulation was better than 10% in most cases examined, and for many situations the agreement was within the expected error of the Monte Carlo experiments. Results from the studies provide valuable information about the noise characteristics of ML-EM reconstructed images. Furthermore, the studies demonstrate the power of the theoretical and Monte Carlo approaches for investigating noise properties of statistical reconstruction algorithms.
Wilson
D W
DW
Department of Biomedical Engineering, The University of North Carolina, Chapel Hill, NC 27599, USA.
Tsui
B M
BM
Barrett
H H
HH
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes