Quantitative measurement of chest wall components: a potential patient-specific replacement for BMI to predict image quality parameters in coronary CT angiography

Abstract

By applying computer image processing technology, this study aims to propose better biometric parameters of coronary computed tomography angiography (CCTA) by evaluating correlations between image quality parameters, the body mass index (BMI), and parameters of chest wall components. One hundred and seventeen subjects (59 males, 58 females, M = 59.3 years, SD = 10.2 years) who underwent CCTA were recruited. A Matlab program was used to measure the chest wall components in chest imaging automatically. In the parameters of the chest wall components, the gray weighted area of the chest wall (ACWgray weighted) was proposed as a new parameter with consideration of the area and CT attenuation of each solid tissue (fat, muscle, and bone). Image quality parameters [image noise, signal to noise ratio, and contrast to noise ratio] were measured on the slices of the aortic root and the maximum heart. The Shapiro–Wilk test was performed to evaluate data distribution. Correlation analyses were conducted to investigate relationships between image quality parameters, the BMI, and parameters of chest wall components. Linear correlation coefficients were used as indicators of the strength of the relationships. The gray weighted average area of the chest wall (aACWgray weighted) and the BMI were correlated with the image quality parameters on the slices of the aortic root and the maximum heart (p < 0.05). The correlation coefficients with image noise were 0.635 and 0.516 on the slices of the aortic root, which were 0.672 and 0.543 on the slices of the maximum heart, respectively. Among all the parameters, aACWgray weighted showed the strongest correlation with image noise on both slices. The average quantitative parameters of the chest wall components, particularly aACWgray weighted showed the strongest correlations with all the image quality parameters. Hence, aACWgray weighted can be proposed as a better patient-specific predictor than the BMI of the image quality parameters in CCTA.