Reducing Negativity Artifacts in Emission Tomography: Postprocessing Filtered Backprojection Solutions

The problem of negative artifacts in emission tomography reconstructions computed by filtered backprojection (FBP) is of practical concern particularly in low count studies. Statistical reconstruction methods based on maximum likelihood (ML) are automatically constrained to be non-negative but their excessive computational overhead (orders of magnitude greater than FBP) has limited their use in operational settings. Motivated by the statistical character of the negativity artifact, we develop a simple postprocessing technique that iteratively adjusts negative values by cancellation with positive values in a surrounding local neighborhood. The compute time of this approach is roughly equivalent to two applications of FBP. The approach was evaluated by numerical simulation in one-and two-dimensional settings. In two dimensions, the source distributions included the Hoffman, the Shepp and Vardi, and a digitized version of the Jaszczak cold spheres phantoms. Our studies compared smoothed versions of FBP, the post-processed FBP, and ML implemented by the expectation-maximization algorithm. The root mean square (RMS) error between the true and estimated source distribution was used to evaluate performance; in two dimensions, additional region-of-interest-based measures of reconstruction accuracy were also employed. In making comparisons between the different methods, the amount of smoothing applied to each reconstruction method was adapted to minimize the RMS error—this was found to be critical. After adjusting for this effect, the average RMS error for FBP was typically between 13% and 20% higher than ML; the modified FPB method was between 2% and 5% greater than ML. Similar results were found for the region-of-interest error. These experiments suggest that it may be possible to achieve much of the gain associated with ML reconstruction in emission tomography by simpler computational means.