Texture feature comparison between step-and-shoot and continuous bed motion in ¹⁸F-fluorodeoxyglucose positron emission tomography

Abstract

Objective: To investigate the differences in texture features between step-and-shoot (SS) and continuous bed motion (CBM) imaging in phantom and clinical studies. Methods: A NEMA body phantom was filled with ¹⁸F-fluorodeoxyglucose solution at a sphere-to-background ratio of 4:1. SS and CBM were performed using the same acquisition duration and the data were reconstructed using 3-D ordered subset expectation maximization with time-of-flight algorithms. Texture features were extracted using the software LIFEx. A volume of interest was delineated on the 22-, 28-, and 37-mm spheres with a threshold of 42% of the maximum standardized uptake value. The voxel intensities were discretized using two resampling methods, namely, a fixed bin size and a fixed bin number discretization. The discrete resampling values were set to 64 and 128. A total of 31 texture features were calculated with gray-level co-occurrence matrix (GLCM), gray-level run length matrix, neighborhood gray-level different matrix, and gray-level zone length matrix. The texture features of the SS and CBM images were compared for all settings using the paired t-test and the coefficient of variation (CV). In a clinical study, a total of 27 lesions from 20 patients were examined using the same acquisition and image processing that was performed during the phantom study. The % difference (%Diff) and correlation between the texture features from SS and CBM images were calculated to evaluate the agreement of the two scanning techniques. Results: In the phantom study, the 11 features exhibited no significant difference between SS and CBM images and CV of ≤10% depending on resampling conditions, whereas entropy and dissimilarity from GLCM fulfilled the criteria for all settings. In the clinical study, the entropy and dissimilarity from GLCM exhibited low %Diff and excellent correlation in all resampling conditions. %Diff of the entropy was lower than that of dissimilarity. Conclusion: The magnitude in differences between texture features of SS and CBM images were different depending on types of features. Because entropy for GLCM exhibits minimal differences between SS and CBM images irrespective of resampling conditions, it may be the optimal feature to reduce the differences between the two scanning techniques.