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

Objective 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. Methods 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 (ACW gray 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. Results The gray weighted average area of the chest wall (aACW gray 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, aACW gray weighted showed the strongest correlation with image noise on both slices. Conclusions The average quantitative parameters of the chest wall components, particularly aACW gray weighted showed the strongest correlations with all the image quality parameters. Hence, aACW gray weighted can be proposed as a better patient-specific predictor than the BMI of the image quality parameters in CCTA.