Optimization of input parameters for a segmented attenuation algorithm for the GE advance/sup TM/ scanner

Segmented attenuation correction (SAG) techniques provide the possibility for increasing patient throughput by decreasing transmission scan times from those required for standard measured attenuation correction (MAC) methods. A SAC algorithm using an unsupervised least biased fuzzy clustering method has been implemented on the GE Advance/sup TM/ system. This implementation has the advantage of requiring no a priori region definitions. There are, however, several parameters which affect both the bias and variance produced by the SAC algorithm. The authors used statistical design of experiment (DOE) techniques to determine the subset of critical input parameters that have the largest impact on both bias and variance. These critical parameters were then analyzed to determine their influence on bias and variance. A second DOE was then carried to optimize the remaining parameters. Settings are identified which can be used to reduce both bias and variance in the segmentation algorithm.