Adaptive noise reduction and sharpening of OSEM-reconstructed data

The Pixon method, a statistically rigorous procedure for adaptive noise suppression that avoids the generation of spurious artifacts yet preserves all the statistically justifiable image features resident in the raw counts, is applied to nuclear studies. The present work focuses on adaptive postsmoothing and sharpening of OSEM-reconstructed data at various count levels, with the ultimate goals to (i) increase sensitivity for detection of lesions of small size and/or of small activity-to-background ratio, (ii) reduce data acquisition time, and (iii) reduce patient dose. We use simulated and measured data and human-observer studies, which are analyzed using quantitative measures. The detectability shows improvement, as does resolution, especially at low counts. Clinical trials would be required to assess this method of image postprocessing