Study on the effect of virtual grid on chest X-ray image quality / 中华放射学杂志

ABSTRACT

Objective:To explore the image quality and its evaluation method using virtual grid under different tube voltages in the clinical chest X-ray exam.Methods:According to the conditions of chest X-ray photography commonly used in clinical practice, the corresponding thickness of plexiglass (20 cm, including CDRAD phantom) was determined as the experimental object. With a fixed tube loading of 4 mAs and the tube voltage from 60 to 125 kV, the experimental object was imaged in three ways physical grid, none grid and virtual grid. The common physical parameters (CNR, σ, C, SNR), texture analysis (Angular second moment, texture Contrast, Correlation, Inverse difference moment, Entropy) and CDRAD phantom score (IQF inv) were evaluated. Two-way ANOVA test was used for each group of common physical parameters, and further pairwise comparisons were made. At the same time, applying virtual grids on the obtained images with chest anthropomorphic model and texture indexing the images with and without virtual grids, then rank sum test of paired sample can be conducted. Results:There were differences in image quality among the three groups of grid mode( P<0.05), and the physical grid delivered the best image quality. The tube voltage had an impact on all image quality evaluation indexes ( P<0.05). The tube voltage was positively correlated with CNR, SNR, angular second moment, inverse difference moment and IQF inv ( P<0.05), and negatively correlated with σ, C, texture contrast and entropy ( P<0.05). There was no significant correlation between the tube voltage and Correlation ( P>0.05). The chest anthropomorphic model images were used to evaluate the virtual grids, and the texture indexes (Angle second moment, Contrast, Correlation, Inverse difference moment, Entropy) were statistically significant (P<0.05). Conclusions:The virtual grid can improve the image quality of chest X-ray photography, and the image texture analysis method can be a useful supplement to the image quality evaluation parameters.