PRACTICAL TRADEOFFS BETWEEN NOISE, QUANTITATION, AND NUMBER OF ITERATIONS FOR MAXIMUM LIKELIHOOD-BASED RECONSTRUCTIONS

Images reconstructed using a maximum likelihood-expectation maximization (ML)-based method with different reconstruction kernels and 1-200 iterations are compared to images reconstructed using filtered backprojection (FBP). ML-based reconstructions using a single pixel (SP) kernel with or without a sieve filter show no quantitative advantage over FBP except in the background where a reduction of noise is possible if the number of iterations is kept small (< 50). ML-based reconstructions using a Gaussian kernel with a multipixel full width-at-half-maximum (FWHM) and a large number of iterations (200) require a sieve filtering step to reduce the noise and contrast overshoot 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-based method during the iterative process without quantitative instabilities.