Fat volume measurements as a predictor of image noise in coronary computed tomography angiography

Abstract

Introduction: Image noise can negatively affect the overall quality of coronary computed tomography angiography (CCTA). Objectives: The purpose of this study was to evaluate the relationship between image noise and fat volumes in the chest wall. We also aimed to compare these with other patient-specific predictors of image noise, such as body weight (BW) and body mass index (BMI). Methods: We undertook a cross-sectional, single-center study. A tube voltage of 100 kV was used for patients with BW <85 kg and 120 kV for BW ≥85 kg. The image noise in the aortic root, single-slice fat volume (SFV) at the level of the left main coronary artery and the total fat volume of the chest (TFV) were analyzed. Results: A total of 132 consecutive patients were enrolled (mean age ± standard deviation, 51 ± 11 years; 64% male). The mean image noise was 30.5 ± 11 Hounsfield units (HU). We found that patients with image noise >30 HU had significantly higher SFV (75 ± 33 vs. 51 ± 24, p < 0.0001) and TFV (2206 ± 927 vs. 1815 ± 737, p < 0.01) compared with patients having noise ≤30 HU, whereas BW and BMI showed no significant difference (78 ± 13 vs. 81 ± 14, p < 0.34) and (28.7 ± 4.7 vs. 26.8 ± 3.8, p < 0.19), respectively. Linear regression analysis showed that image noise has better correlation with SFV (R = 0.399; p < 0.0001); and TFV (R = 0, p < 0.009) than BMI (R = 0.154, p < 0.039) and BW (R = –0.102, p = 0.12). Conclusions: Fat volume measurements of the chest wall can predict CCTA image noise better than other patient-specific predictors, such as BW and BMI.