Quantification of water and lipid composition of perivascular adipose tissue using coronary CT angiography: a simulation study.

Early detection of vascular inflammation via perivascular adipose tissue (PVAT) compositional changes (e.g., increased water content) could improve cardiovascular risk stratification. However, CT-based measurements face variability due to tube voltage and patient size. This study aims to quantify perivascular adipose tissue (PVAT) composition (water, lipid, protein) using coronary CT angiography and assess impacts of tube voltage, patient size, and positional variability on measurements. A 320-slice CT simulation generated anthropomorphic thorax phantoms (small, medium, large) with fat rings mimicking different patient sizes. Ten randomized water-lipid-protein inserts were placed within the thorax phantom. Three-material decomposition was applied using medium phantoms with different tube voltages and different patient sizes at 120 kV. PVAT CT number (HU) increased with higher tube voltages and larger patient sizes. The root-mean-squared errors (RMSE) for water volumetric fraction measurements were 0.26%, 0.64%, 0.01%, and 0.15% for 80, 100, 120, and 135 kV, respectively, and 0.19%, 0.35%, and 0.61% for small, medium, and large size phantoms at 120 kV, respectively. The root-mean-squared deviations (RMSD) were 3.52%, 2.94%, 4.96%, and 6.00% for 80, 100, 120, and 135 kV, respectively, and 3.82%, 3.74%, and 6.05% for small, medium, and large size phantoms at 120 kV, respectively. Clinically relevant water fractions spanned 17-37%, with inflammation expected to alter values by approximately 5%. The findings of this study indicate that, after accounting for the effects of tube voltage and patient size, perivascular adipose tissue CT number can be quantitatively represented in terms of its water composition. This decomposition method has the potential to enable quantification of water composition and facilitate early detection of coronary artery inflammation.