ML Background Scale Factors Estimation for Prompt Gamma Tracers in PETCT Imaging

Correction of prompt gamma coincidences and scattered coincidences is required to obtain quantitative estimates for PETCT imaging with non-pure positron emitters. Currently, this background is estimated as a linear combination of scattered coincidences from single scatter simulation (SSS) and prompt gamma coincidences estimate (PGE). Each of these components are scaled by fitting to the net trues in a region outside the emission support or "tails" and is referred to as the tail fitted background scaling (TFBS). In this work, we extend the previously proposed maximum likelihood (ML) scatter scaling approach to compute scaling factors for both background components which is referred to as ML background scaling method (MLBS). Poisson ML based solution with nested loop for PGE and SSS scaling factors estimation was obtained similar to that proposed by Panin [1]. In order to validate this approach, experimental phantom studies with a uniform cylinder and image quality phantom filled with I-124 were performed. In addition, a preliminary assessment of visual image quality in two clinical cardiac Rb-82 studies was performed. Background fractions with both approaches were estimated for all studies. Based on the results from uniform cylinder phantom, the number of iterations for estimating scale factors was selected to be 15 for each 8 image updates. This resulted in similar background fraction with both methods. For the IQ phantom, background fraction obtained with MLBS was lower (47%) compared to TFBS method (50%). A 2% dip was observed at the center of phantom with TFBS method while the profile was flat with MLBS method. In the case of cardiac Rb-82 studies, MLBS and TFBS approach gave similar activity images for the relatively smaller patient. However for the large patient, some background over-correction was observed with TFBS but not MLBS approach. Our preliminary results indicate that ML background scale factor estimation approach may perform equivalent to the current TFBS approach for most studies with non-pure positron emitters, in addition to potentially improving image quality in the case of studies with large patients.