Practical Tradeoffs Between Noise, Resolution And Quantitation, And Number Of Iterations For Maximum Likelihood Reconstructions

Abstract

Images reconstructed by the Maximum Likelihood (ML) method using different reconstruction kernels are compared with those using Filtered Backprojection (FBP). ML reconstruction with a single pixel (SP) kernel with or without a sieve filter shows no quantitative advantage over FBP except in the background where a reduction of noise is possible if the number of iterations is kept small (40). ML reconstruction using a Gaussian kernel with a multi-pixel full-width-at-halfmaximum and a large number of iterations (200) requires a sieve filtering step to reduce the noise and edge artifacts in the final images. These images have some small quantitative advantages over FBP depending on the structures being imaged. It is demonstrated that a feasibility stopping criterion controls the noise in a reconstructed image, but is insensitive to quantitation errors, and that the use of an appropriate overrelaxation parameter can accelerate the convergence of the ML method during the iterative process without quantitative instabilities.