Accurate and consistent lesion quantitation with clinically acceptable penalized likelihood images

• Main Authors: Asma, Evren, Ahn, Sangtae, Ross, Steven G., Chen, Anthony, Manjeshwar, Ravindra M.
• Format: Conference Proceeding
• Language: English
• ISSN: 1082-3654; 2577-0829
• DOI: 10.1109/NSSMIC.2012.6551928

Clinical widespread use of edge-preserving penalized-likelihood (PL) methods has been hindered by the properties of the resulting images such as blocky background noise textures, piecewise-constant appearances of organs and relative noise strengths in high and low activity regions despite their potential for improved lesion quantitation over OSEM and quadratically penalized PL. Here, we investigate the use of the convex relative difference penalty first introduced by Nuyts et at. (TNS '02) for improved quantitation over OSEM in whole-body clinical PET imaging while maintaining visual image properties similar to OSEM and therefore clinical acceptability. We perform data-independent axial smoothing modulation based on the system sensitivity profile in order to avoid excessively smooth bed-position overlap regions. We also perform data-independent transaxial smoothing modulation to avoid oversmoothing the central portions of the field-of-view that occur with the use of a constant smoothing parameter. The resulting overall smoothing modulation profile allows for improved resolution uniformity in regions with high sensitivity and improved noise uniformity between regions of low and high sensitivity. We evaluate our approach in multiple clinical datasets with lesions inserted into representative locations with time-of-flight (TOF) and non-TOF reconstructions. Such "hybrid" datasets combine clinically realistic image backgrounds with known lesion activities. We demonstrate that using the relative difference penalty with proper smoothing modulation, superior quantitation over early-stopped and post-filtered OS EM can be achieved while maintaining clinically acceptable image quality. Furthermore, the approach lends itself to theoretical contrast recovery prediction and bias correction for improved contrast recovery consistency across lesions and further improvements in quantitation.