ML based positioning algorithms for scintillation cameras

Positioning algorithms for scintillation cameras are investigated. Maximum Likelihood (ML) estimation techniques and centroid-based methods (e.g., weighted and local centroid) are evaluated. Two methods to reach the two-dimensional ML solution are proposed. First, we investigate a 1D based recursive ML positioning algorithm. The technique overcomes the dimensional separable violation for hexagonally packed photomultiplier tube (PMT) arrays. Second, we examine a 2D based ML method that uses local PMT clusters to reduce the computational demands of full 2D ML implementations. The methods are tested with data sets generated using Monte Carlo simulation and verified with experimental study. Simulation (experimental) results show that the full 2D ML algorithm is superior to the others. It has a 7(10)%, 67(47)% and 69(30)% improvement over the weighted centroid with bias subtraction method in terms of spatial resolution, linearity and MSE, respectively