Assessment Of Quantification Accuracy With Ml Scatter Scaling For Variable Count Statistics

Quantitative PETCT imaging requires the correction of scattered coincidences in the net trues data. Typically, this is done using single scatter simulation (SSS) based model to estimate the scatter shape followed by scaling the scatter sinogram to the net trues data via scatter tails which is referred to as the tail-fitted scatter scaling (TFSS). An alternate to TFSS is the maximum likelihood scatter scaling (MLSS) which employs an iterative update solution to compute the scaling factors along with an image update. In this work, we further develop the MLSS method to improve quantification accuracy over a wide range of count statistics. Poisson ML based solution with nested loop for SSS scaling factors estimation was used to estimate scaling factors with MLSS. In order to reduce inaccuracies in the scaling parameters for low count statistics, a limit was set on the maximum number of reconstruction updates based on the number of planes with very high or very low scale factors. Assessment of quantification accuracy for the updated MLSS method was done using experimental phantom studies and Y-90 clinical studies. The phantom studies included PETCT scans of a large uniform cylinder filled with F-18 and a cardiac phantom with activity filled in the cardiac insert with cold background. For both studies, list-mode PET data were rebinned into sinograms to obtain different count statistics and processed with MLSS and TFSS approach. The PET activity concentration was compared with respect to the measured activity concentration (uniform cylinder phantom) or the activity concentration obtained for the highest count statistics data (cardiac phantom). A preliminary validation was also performed for 2 clinical Y-90 PETCT datasets. Scatter fractions were compared with absolute, TFSS and MLSS approach for both datasets. MLSS was found to provide improved quantification accuracy over a large degree of count statistics compared to TFSS approach in both phantom datasets. In addition, scatter fraction obtained with MLSS was comparable to absolute scatter while TFSS overestimated scatter in case of the large patient. MLSS has the potential to provide improved quantification in the case of low count PETCT imaging.