Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

Abstract

Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

“…We based this approach on methods reported in several clinical studies 41, 53, 54, 55. Also, Galavis et al36 used a similar scale to categorize the features based on their variation, and Tixier et al37 indicated that such limits were referenced to previously defined reproducibility limits for standard uptake values.…”

“…In recent years, varying reconstruction methods have evolved into sophisticated algorithms with various image qualities due to modern computing. A study by Galavis et al36 showed that different acquisition modes and image reconstruction settings might cause variation in radiomic features. Similarly, the gray‐level discretization of PET/CT images has shown to have a great impact on some radiomic features 37…”

Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result

“…Automated drawing ROI methods may reduce intra-and inter-observer variation. Likewise, Galavis et al [28] reported that some texture parameters exhibited large variations when PET images are acquired and processed with different modes. We included PET scans acquired on a single PET/CT system not to make factors of feature variability.…”

Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma

“…On the one hand, intra-tumour uptake heterogeneity was quantified considering only those indices that have been previously shown as robust considering image reconstruction and acquisition variability [23], and physiological reproducibility based on double baseline PET acquisitions [24]. Figure 1 illustrates the two types of heterogeneity parameters under consideration.…”

“…However, recent studies have shown that only a limited number of these TF derived indices are robust with respect to the clinical range of image reconstruction algorithms and acquisitions protocols [23], and reproducible with respect to physiological variability assessed on double baseline FDG PET scans [24]. Within the same context it has also been hypothesized that heterogeneity quantification could be dependent on the necessary functional tumour volume delineation method used, as well as be compromised by the low PET image spatial resolution and associated partial volume effects (PVE) [18,22], although this has never been thoroughly investigated.…”