Nie Shu, Molloi Sabee
Department of Radiological Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
Department of Radiological Sciences, Medical Sciences I, B-140, University of California, Irvine, CA, 92697, USA.
Int J Cardiovasc Imaging. 2025 Jun;41(6):1091-1101. doi: 10.1007/s10554-025-03390-1. Epub 2025 Apr 10.
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.
通过血管周围脂肪组织（PVAT）成分变化（如含水量增加）早期检测血管炎症，可改善心血管风险分层。然而，基于CT的测量因管电压和患者体型而存在变异性。本研究旨在利用冠状动脉CT血管造影术量化血管周围脂肪组织（PVAT）的成分（水、脂质、蛋白质），并评估管电压、患者体型和位置变异性对测量的影响。一个320层CT模拟生成了具有模仿不同患者体型的脂肪环的拟人化胸部体模（小、中、大）。在胸部体模内放置了10个随机的水 - 脂质 - 蛋白质插入物。使用具有不同管电压和不同患者体型的中型体模在120 kV下进行三材料分解。PVAT的CT值（HU）随着管电压升高和患者体型增大而增加。水体积分数测量的均方根误差（RMSE）在80、100、120和135 kV时分别为0.26%、0.64%、0.01%和0.15%，在120 kV时小、中、大体型体模分别为0.19%、0.35%和0.61%。均方根偏差（RMSD）在80、100、120和135 kV时分别为3.52%、2.94%、4.96%和6.00%，在120 kV时小、中、大体型体模分别为3.82%、3.74%和6.05%。临床上相关的水分数范围为17 - 37%，预计炎症会使值改变约5%。本研究结果表明，在考虑管电压和患者体型的影响后，血管周围脂肪组织的CT值可以根据其水成分进行定量表示。这种分解方法有潜力实现水成分的量化，并有助于冠状动脉炎症的早期检测。
