Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET

Abstract

UNLABELLED: Evaluation of tumor heterogeneity based on texture parameters has recently attracted much interest in the PET imaging community. However, the impact of reconstruction settings on texture parameters is unclear, especially relating to time-of-flight and point-spread function modeling. Their effects on 55 texture features (TFs) and 6 features based on first-order statistics (FOS) were investigated. Standardized uptake value (SUV) measures were also evaluated as peak SUV (SUVpeak), maximum SUV, and mean SUV (SUVmean). METHODS: This study retrospectively recruited 20 patients with lesions in the lung who underwent whole-body (18)F-FDG PET/CT. The coefficient of variation (COV) of each feature across different reconstructions was calculated. RESULTS: SUVpeak, SUVmean, 18 TFs, and 1 FOS were the most robust (COV ≤ 5%) whereas skewness, cluster shade, and zone percentage were the least robust (COV > 20%) with respect to reconstruction algorithms using default settings. Heterogeneity parameters had different sensitivities to iteration number. Twenty-four parameters including SUVpeak and SUVmean exhibited variation with a COV less than 5%; 28 parameters, including maximum SUV, showed variation with a COV in the range of 5%-10%. In addition, skewness, cluster shade, and zone percentage were the most sensitive to iteration number. In terms of sensitivity to full width at half maximum (FWHM), 15 TFs and 1 FOS had the best performance with a COV less than 5%, whereas SUVpeak and SUVmean had a COV between 5% and 10%. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUVpeak and SUVmean, having a COV less than 10%. CONCLUSION: Different image features have different sensitivities to reconstruction settings. Iteration number and FWHM of the gaussian filter have a similar impact on the image features. Grid size has a larger impact on the features than iteration number and FWHM. The features that exhibited large variations such as skewness in FOS, cluster shade, and zone percentage should be used with caution. The entropy in FOS, difference entropy, inverse difference normalized, inverse difference moment normalized, low gray-level run emphasis, high gray-level run emphasis, and low gray-level zone emphasis are the most robust features.