Method to Determine the Standard Deviation of SUV Parameters

Abstract Some of the parameters used for the quantification of Positron Emission Tomography (PET) images are the Standardized Uptake Value (SUV)Max, SUV Mean and SUV Peak. In order to assess the significance of an increasing or decreasing of these parameters for diagnostic purposes it is relevant to determine their standard deviation. In this study we present a method to determine the range of statistical variation of the SUV in PET images. Our method is based on dividing an original dataset into subsets of shorter time-frames. The variation between the SUV parameters of the subsets is used to estimate the standard deviation of the of the original acquisition. This method was tested on images of a NEMA quality phantom with acquisition time of 150 s per bed position and foreground to background activity ratio of F18-2-fluoro-2-deoxy-D-glucose (FDG) of 10:1. This original dataset has been reconstructed with different reconstruction lengths, generating new data subsets. The SUV Max, Mean and Peak were calculated for each image in the subsets. Their standard deviation has been calculated per subset for the different spheres included in the phantom. The variation of each subset has then been used to estimate the expected variation between images at 150 s reconstruction length. We report the largest standard deviation of the SUV parameters for the smallest sphere, and the smallest variation for the largest sphere. The expected variation at 150 s reconstruction length does not exceed 6% for the smallest sphere and 2% for the largest sphere, but we report an higher coefficient of variation (up to 30%) for shorter reconstruction lengths. We also report significant differences in the variation of SUV parameters for the larger spheres. With the presented method we are able to determine the standard deviation of SUV parameters only due to and the statistical variation. The method enables us to evaluate the effect of parameter selection and lesion size on the standard deviation, and therefore to evaluate its relevance on the total variation of the SUV value between studies.