PET image reconstruction: A stopping rule for the MLEM algorithm based on properties of the updating coefficients.
An empirical stopping criterion for the 2D-maximum-likelihood expectation-maximization (MLEM) iterative image reconstruction algorithm in positron emission tomography (PET) has been proposed. We have applied the MLEM algorithm on Monte Carlo generated noise-free projection data and studied the properties of the pixel updating coefficients (PUC) in the reconstructed images. Appropriate fitting lead to an analytical expression for the parameterization of the minimum value in the PUC vector for all non-zero pixels for a given number of detected counts, which can be employed as basis for the stopping criterion proposed. These results have been validated with simulated data from real PET images.
Gaitanis
Anastasios
A
Department of Medical Physics, University of Patras, Greece. agaitanis@bioacademy.gr
Kontaxakis
George
G
Spyrou
George
G
Panayiotakis
George
G
Tzanakos
George
G
D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009010:Monte Carlo Method; D049268:Positron-Emission Tomography