Analysis of the Influence of CT Imaging Conditions on the CT Image Features of Pulmonary Nodule Phantoms

Abstract

By studying the impact of CT imaging conditions on image features of pulmonary nodule phantoms, this study explores possibility to optimize the diversity of imaging conditions when building a test set for AI-enabled medical device software for pulmonary nodule analysis. In this study, 15 phantoms mimicking three types of pulmonary nodules were placed in different positions in a whole lung phantom. CT images were collected under combination of different parameters, including tube voltage, tube current-time product, collimator width and reconstruction operator (14 sets in total). The texture features (GLCM, GLRLM, GLSZM, GLDM, NGTDM) of CT images of pulmonary nodules were extracted using the PyRadiomics package in Python, and the five main texture features were selected using the LASSO method. To analyze the data, correlation analysis and non-parametric tests were applied, with a correction method used to control errors in multiple comparisons. The results showed that changes in the reconstruction kernel affected the stability of texture features in CT images of pulmonary nodules more significantly than other imaging parameters in this study. Different reconstruction kernels influenced the representation of image details, leading to variations in image sharpness and noise levels, which in turn affected the stability of the extracted feature values. Meanwhile, no significant changes were induced by variation of tube voltage, tube current-time product and collimator width during image acquisition. These findings suggest that when building the test set for AI-enabled medical device software for pulmonary nodule analysis, it is essential to carefully consider the diversity of reconstruction kernels and their impact on data quality, thereby enhancing the representativeness of the test set.