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The ascending aortic elasticity feature in normotensive subjects: evaluation with coronary CT angiography
Clinical Imaging 38 (2014) 686–692
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The ascending aortic elasticity feature in normotensive subjects: evaluation with coronary CT angiography Wenqian Liang, Dandan Chen, Weicui Chen, Guanxun Cheng ⁎ Department of Imaging Center, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Ave. N., Guangzhou, Guangdong 510515, China
a r t i c l e
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Article history: Received 3 February 2014 Received in revised form 27 May 2014 Accepted 2 June 2014 Keywords: Aortic elasticity Computed tomography Risk factors Coronary CT angiography
a b s t r a c t To evaluate the ascending aortic elasticity feature, 118 normotensive subjects who underwent coronary computed tomography angiography (CCTA) were enrolled. Two groups of parameters assessing elasticity were calculated based on the measurements of volume and area of ascending aorta. Multivariate analysis revealed that some factors including age, systolic BP, diastolic BP, heart rate, smoking status and hyperlipidemia independently related to decreased aortic elasticity. Both measuring methods are applicable for evaluation of aortic elasticity. As the prevalence of CCTA, it is meaningful that CCTA can provide not only the structural details of ascending aorta but also functional information of the vessel elasticity. © 2014 Elsevier Inc. All rights reserved.
1. Introduction Aortic elasticity has been identified as an independent predictor of adverse cardiovascular diseases including hypertension, coronary events and stroke at the early stage in healthy subjects [1,2]. Furthermore, it is also an independent predictor of all-cause and cardiovascular mortality in hypertensive patients [3,4]. Therefore, early and accurate evaluation of the functional abnormality of aortic elasticity with non-invasive methods in normotensive patients becomes very significant. There are several methods and parameters for evaluating aortic elasticity. In recent years, the potential of a combined morphology and functional image protocol has made the imaging methods more attractive, such as magnetic resonance (MR), ultrasonography and computed tomographic (CT). However, MR measurements are unreliable in situations with complex flow patterns, limited by the long scan time and unavailable for patients with instable situation [5,6]. As for Ultrasound, it suffers inconstant applicability because of its result which is easy influenced by other factors [7,8]. The improvement of CT technology has made possible cardiac images with high temporal resolution [9,10]. It has been reported that vessel distensibility measurements obtained by using electrocardiographically gated multidetector-row (MD)CT was feasible in phantom, porcine specimen and aortic model of polydimethylsiloxane [11–13]. In addition, Ning Li et al. showed a negative correlation between age and ascending aortic elasticity by using electrocardiographically
⁎ Corresponding author. Department of Imaging Center, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Ave. N., Guangzhou, Guangdong 510515, China. E-mail address: [email protected] (G. Cheng). http://dx.doi.org/10.1016/j.clinimag.2014.06.003 0899-7071/© 2014 Elsevier Inc. All rights reserved.
(ECG)-gated dual-source (DS) CT [14]. However, the methods and procedures of assessment of arterial elasticity are different in studies using CT [14–18]. Some measured certain cross-sectional area (CSA) that paralleling to the axis of human body for evaluation of aortic distensibility or stiffness [15–18], while some measured certain CSA that perpendicular to the trace that passed through the centre of the vessel [14]. Also, which method is more appropriate and applicable has not been studied. In this study, we evaluated the elasticity of ascending aorta in normotensive patients with two different measuring methods using ECG-gated DSCT. The method of measuring the volume changes of vessel has not been reported. Thus, we tested the hypothesis that the volume method is also an applicable method for evaluation of aortic elasticity. The aim of the present study was to evaluate the ECG-gated DSCT in the assessment of ascending aortic elasticity in normotensive subjects and demonstrate the risk factors associated with reduced aortic elasticity.
2. Materials and methods 2.1. Subjects From February 2012 to January 2013, several hundreds of people who are suspected coronary artery disease (CAD) have performed CCTA in our institution Subjects were excluded if CCTA image quality was poor for the detection due to motion artifacts. Subjects with aortic diseases, CAD, secondary hypertension, diabetes, renal dysfunction or history of any known systemic illness were also excluded. Finally, 118 patients with normotension (87 males, 31 females, aged 26–80 years with a mean of 53.28±11.09 years) were enrolled. Normotensive subjects were divided into four groups according to their age: group A
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b45 years (32 cases), group B 45–54 years (37 cases), group C 55–64 years (25 cases), group D N64 years (24 cases). The lifestyle and clinical characteristics of all participants were obtained by completion of a questionnaire and checked by interview. We collected information including age, sex, body mass index (BMI), heart rate during scanning (without any drugs to reduce heart rate), blood press (BP) before image acquisition, smoking status, alcohol consumption and hyperlipidemia. The BMI was calculated by dividing body weight (kg) by the square of height (m). BP was measured twice before examination using a validated automated Omron 705CP (Omron 705cp, Kyoto, Japan) and the mean value of the measurements were recorded. Smoking status was defined as current smoking (quit smoking less than half a year and smoking more than two cigarettes a day) and nonsmoking. Alcohol consumption was assessed by the alcohol intake ≥2 times per week by the subjects. Hyperlipidemic subjects were defined as people with abnormal lipid levels or receiving lipid-lowing therapy. The study was approved by the ethical committee, and informed consents including coronary examination and our special study protocol were obtained from all enrolled subjects before examination.
2.2. Image acquisition Image acquisition was performed with CCTA using ECG-gated DSCT (Somatom Definition, Siemens Healthcare, Forchheim, Germany). Routine CCTA protocol was performed to determine a scanning range from tracheal juga to the diaphragm by the calcium score scan. Then, 75–85 ml of contrast medium (Iopamiron 370, Bayer Schering Pharma AG, Berlin, Germany), according to the patient’s weight, was injected through a dual-head injector into cubital vein at a rate of 5.0–5.5 ml/s, followed by 30 ml of saline solution chaser, using a bolus tracking technique at the slice of aortic root to determine the trigger time. When the density reached a predefined threshold of 90 Hounsfield units (HU), the scan started automatically with a 6-s scan delay during one breath-hold with simultaneous recording of the ECGtracing. The image parameters were a slice collimation of 32×0.6 mm, slice acquisition 64×0.6 mm by means of a z-axis flying focal spot, 120 KV, 270 mA, 0.33 s rotation time, with an 83-ms temporal resolution.
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2.3. Reconstruction and measurements From the raw data, images were reconstructed every 5% (5–100%) of the RR interval with an effective slice thickness of 0.75-mm, an increment of 0.5-mm, and B26f reconstruction kernel. For every subject, it can be obtained 20 different phases of time-resolved image series during cardiac cycle. 2.3.1. Volume measurement process We used Volume software (Siemens AG, Germany) to measure the volume of ascending aorta under different phases. The contour of ascending aorta was manually delineated. The region of interest (ROI) was chosen from the level of the beginning of left coronary artery to 61 slices (3.08 cm) above it. The window level and width of 200 and 800 HU was used. The volume of ROI in ascending aorta was automatically detected (Fig. 1). 2.3.2. Area measurement process We used Vessel Analysis software (Siemens AG, Germany) to measure the area of ascending aorta under different phases. Makers were placed at the sinotubular junction and the ascending aorta. A line that passed through the centre of the vessel was automatically generated. We chose the plane of the beginning of left coronary artery as the reference and defined 25 mm above it as the ROI. The window level and width of 200 and 800 HU was also used. The CSA of ROI which was perpendicular to the trace of vessel was automatically measured. (Fig. 2) The aorta is a tube with geometric curvature and changes its shape regularly during the cardiac cycle. We measured the volumes and areas of ascending aorta among 20 different phases, and wrote down the maximum and minimum luminal volume/area. The volumes and areas were measured by two independent observers at the same time and by one experienced observer twice at two separate times. The average value of the three measurements was used as the reference data(Vs, Vd, cm 3; Ss, Sd, mm 2). 2.4. Aortic elasticity evaluation In our study, there were several different parameters to evaluate the elastic properties of the ascending aorta, including aortic distensibility
Fig. 1. Measurement of volume. (a–b) Example of measurement at the same region 3.08cm above the beginning of left coronary artery observed in a 42-year-old man. The maximum and minimum volume of the ROI occurred at 20% interval (a) and 100% interval (b) respectively.
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Fig. 2. Measurement of area. (a–b) Example of measurement at the same cross-section 2.5cm above the beginning of left coronary artery observed in a 42-year-old man. The maximum and minimum area of the ROI occurred at 20% interval (a) and 100% interval (b) respectively.
(AD, ×10 −3mmHg−1), aortic compliance (AC, mm2/mmHg) and aortic stiffness (β) which were reported by previously publications [19–21]: AD ¼ ½ðSs−SdÞ=Sd=ðSBP−DBPÞ; AC ¼ ðSs−SdÞ=ðSBP−DBPÞ; β ¼ ln ðSBP=DBPÞ=½ðDs−DdÞ=Dd Ss and Sd were defined as maximum luminal area and minimum luminal area, respectively. Ds and Dd were defined as maximum lumen diameters and minimum lumen diameters which were calculated from those areas with the assumption that the crosssection was circular (D=2×[area/π] 1/2). The average areas were obtained according to the volume of the ascending aorta using the formula S=volume/height. Therefore, the average cross-sectional areas which parallel to the axis of human body were converted by maximum and minimum volume of the ROI, the parameters (ADv, ACv, βv) of the ascending aortic elasticity were calculated; Another group of aortic elastic parameters (ADs, ACs, βs) were calculated by the cross-sectional areas which perpendicular to the center curve of the ascending aorta. 2.5. Statistical analysis Statistical analysis was performed using SPSS13.0 statistical package (SPSS, Chicago, IL, USA). All continuous variables were expressed as means+standard deviations. Paired-Samples t test was
used to identify the interobserver differences and intraobserver differences. Differences among groups were tested using independent t test or one-way analysis of variance (ANOVA), Least-significant difference (LSD) or Dunnett’s T3 was used for multiple comparisons. Categorical variables were reported as percentage and compared by chi-square test. Linear regression models were used to determine the association of elastic parameters with clinical characteristics including continuous and categorical variables. Multivariate linear regression models were used to assess the independent association of these factors with aortic elasticity parameters. Curve estimation was used to express the relationship between age and every elastic parameter. A statistically significant difference was defined as a two-sided P less than 0.05. 3. Results 3.1. Regulation and accuracy of measurement The images showed a high contrast between the vessel full of contrast medium and the surrounding fatty tissue. The volumes and cross-sectional areas of the ROI changed regularly during the cardiac cycle. Monitoring different phases throughout the cardiac cycle of all participants, the ascending aorta were found to achieve the maximum volume and area at an RR interval of 23.09±3.51% and the minimum at an RR interval of 97.03±2.55%. There were excellent interobserver and intraobserver agreement in measurements.
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3.2. Association between age and aortic elasticity in normotensive subjects Age showed significant correlation with elasticity parameters in normotensive participants. There was an age-dependent decrease of ADv (rp=−0.816, P=.000), ACv (rp=−0.763, P=.000), ADs (rp=−0.820, P=.000), ACs (rp=−0.769, P=.000) and increase of βv (rp=0.822, P=.000), βs (rp=0.826, P=.000). Curve estimation showed the best fitting model was Compound model for age and
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ADv (R 2=0.698), ACv (R2=0.652), ADs (R2=0.710), ACs (R2=0.669) and cubic model for age and βv (R2=0.742), βs (R2=0.769) (Fig. 3). In order to further evaluate the relationship between age and elastic parameters, the participants were divided into four age groups (A–D). Table 1 shows the clinical characteristics and elasticity parameters of normotensive patients in A–D groups. There were no difference between groups in respect to clinical characteristics including continuous and categorical variables except age and systolic BP (SBP). The elasticity parameters among the age groups were
Fig. 3. Fitting curves of curve estimation between age and aortic elastic parameters. The relationship between age and parameters showed that ADv (a), ADs (b), ACv (c), ACs (d) deceased and βv (e), βs (f) increased with age. The best fitting model was Compound model for age and ADv (a), ACv (b), ADs (c), ACs (d) and Cubic model for age and βv (e), βs (f) (ADv, ADs=aortic distensibility (×10−3mmHg−1); ACv, ACs=aortic compliance (mm2/mmHg); βv, βs=aortic stiffness index.
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Table 1 Clinical characteristics and elasticity parameters of normotensive participants grouped by age Age groups
Age (years) Male gender (%) BMI (kg/m2) SBP (mmHg) DBP (mmHg) HR (beats/min) Smoking (%) Drink (%) Hyperlipidemia (%) ADv (×10−3mmHg−1) ACv (mm2/mmHg) βv ADs (×10−3mmHg−1) ACs (mm2/mmHg) βs
P value
b45(A) (n=32)
45~54(B) (n=37)
55–64(C) (n=25)
N64(D) (n=24)
40.44±4.18+Δ# 75% 23.90±2.69 117.28±8.36# 78.69±6.98 68.69±8.50 21.9% 21.9% 25% 4.23±1.37+Δ# 2.75±0.72+Δ# 5.62±1.82Δ# 4.55±1.56+Δ# 2.81±0.79+Δ# 5.35±1.99Δ#
50.11±2.246⁎Δ# 83.8% 25.26±2.70 119.84±9.33⁎# 79.76±6.40 72.19±9.62 29.7% 29.7% 29.7% 2.97±1.15⁎Δ# 2.24±0.65⁎Δ#
58.24±2.70⁎+# 68% 23.74±2.40 120.60±9.01 77.20±8.59 69.48±9.82 24% 16% 24% 2.12±0.51⁎+# 1.79±0.48⁎+# 10.46±2.29⁎# 2.07±0.55⁎+# 1.66±0.52⁎+# 10.82±2.59⁎+#
70.13±4.25⁎+Δ 62.5% 23.51±3.71 125.17±9.92⁎+ 76.88±8.86 72.17±10.59 29.2% 16.7% 33.3% 1.10±0.47⁎+Δ 1.03±0.37⁎+Δ 22.52±10.74⁎+Δ 1.05±0.52⁎+Δ 0.93±0.41⁎+Δ
7.83±2.26# 3.03±1.26⁎Δ# 2.17±0.69⁎Δ#♦ 7.76±2.35Δ#
24.38±11.97+Δ
0.000 0.268 0.069 0.019 0.424 0.356 0.869 0.530 0.865 0.000 0.000 0.000 0.000 0.000 0.000
Abbreviations: HR=heart rate; ADv, ADs=aortic distensibility (×10−3mmHg−1); ACv, ACs=aortic compliance (mm2/mmHg); βv, βs=aortic stiffness index. *Pb0.05 between group A; +Pb0.05 between group B; ΔPb0.05 between group C; # Pb0.05 between group D.
compared. Statistically significant differences in elastic parameters were found among all age groups, ADv, ACv, ADs, ACs decreased while βv and βs increased with the increase of age. There were significant differences of every different elastic parameter between each pair of age groups in multiple comparisons except βv and βs between group A and B and βv between group B and C. 3.3. Factors associated with aortic elasticity in normotensive subjects To identify the independent factors associated with the aortic elasticity, the relationships between every clinical characteristics and elastic parameters were analyzed separately in all participants, which were showed in Table 2. In normotensive subjects, the further multiple regression analysis showed ADv, ADs were independently associated with age, SBP, heart rate, smoking status and hyperlipidemia (R 2=0.740, P=.000;R 2=0.740, P=.000). ACv was independently associated with age, SBP, diastolic BP (DBP), heart rate, hyperlipidemia (R 2=.786, P=.000). ACs was independently associated with age, SBP, DBP, heart rate, smoking status and hyperlipidemia (R2=0.768, P=.000). βv and βs were independently associated with age, DBP, smoking status and hyperlipidemia (R 2=0.688, P=.000; R 2=0.681, P=.000). 4. Discussion Aortic elasticity is an important functional property of aorta and serves as an indicator of serious diseases [1–4]. Multisliced
Table 2 Relationship of aortic elasticity parameters to clinical characteristics from the simple linear regression analysis Variable
Standardized regression coefficients ADv
Age (years) Gender (M/F) BMI (kg/m2) SBP (mmHg) DBP (mmHg) HR (beats/min) Smoking (N/Y) Drink (N/Y) Hyperlipidemia (N/Y)
ACv
βv
−0.752⁎ −0.033 −0.002 −0.553⁎ 0.092 −0.269⁎ −0.236⁎
−0.731⁎ 0.764⁎ −0.104 0.053 0.104 −0.164 −0.496⁎ 0.236⁎ 0.234⁎ −0.284⁎ −0.200⁎ 0.127 −0.229⁎ 0.235⁎ −0.089 −0.093 0.029 −0.256⁎ −0.259⁎ 0.238⁎
⁎ Pb0.05 See Table 1 for the abbreviation.
ADs −0.755⁎ −0.055 0.021 −0.532⁎ 0.072 −0.296⁎ −0.246⁎
ACs
βs
−0.739⁎ 0.773⁎ −0.115 0.070 0.108 −0.169 −0.480⁎ 0.245⁎ 0.202⁎ −0.264⁎ −0.233⁎ 0.161 −0.231⁎ 0.210⁎ −0.101 −0.097 0.038 −0.254⁎ −0.255⁎ 0.226⁎
(MS)CT as a noninvasive tool allows the precise assessment of aortic elasticity and the detection of structural lesions in a single image acquisition. Previous studies have demonstrated that aortic elasticity estimated through MSCT is feasible [11–18]. In our study, retrospective ECG-gated CCTA was used to evaluate the functional change of aorta without additional examination and radiation. By reconstructing the original data of CCTA at different phases, we used two different methods to measure the changes of ascending aorta during cardiac cycle. The elasticity of ascending aorta was evaluated by converting the results measured by two methods into two groups of elastic parameters. As we know, heart and great vessels have regular movements during cardiac cycle in different directions, so the shape and location of ascending aorta are affected by heartbeat [22]. In order to identify the same region of the ascending aorta during its movement in cardiac cycle, we choose the beginning of left coronary artery as reference point. It is a reliable and expedient point according to our experience during the study. Furthermore, our study showed the ascending aorta achieved the maximum volume and area at different RR intervals in different participants. Since the movement of ascending aorta in every participant is diverse, our study is more reliable than the previous study using 35% and 95% as the fixed maximum and minimum measurement phases in every subject, respectively [15]. We used two methods to determine the changes in CSA of the ascending aorta during cardiac cycle. First, by using Volume software, the volume of ascending aorta was detected to calculate the average CSA which was parallel to the axis of human body. Second, by using Vessel Analysis software, we detected the CSA which was perpendicular to the center curve of the ascending aorta vascular. In our study, we found that the first method is more stable and convenient to identify the same region in different phases which is very essential for measurement of the change of aorta during cardiac cycle. However, the parameters calculated by the volume changes of ascending aorta reflect the elasticity of vascular area which is parallel to the axis of human body. Unlike the method using in most recent studies that can only reflect the elasticity of certain vascular plane [15–18], our method can reflect the average elasticity of certain vascular tube. On the other hand, the second method can reflect the actual vascular area which perpendicular to the whole vessel, nevertheless it rely on the automatically generated line along the centre of the vessel. This kind of line was generated by computer and appeared to be different every single time. In this way, it is very hard to identify the same area in different phases. Therefore, these two methods were both used to detect the changed areas of ascending aorta, and then we calculated two groups of parameters (ADv, ACs, βv, ACv, ADs, βs) to analyze the vascular elasticity in our study, obtaining a
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more objective and comprehensive insight in assessing aortic elasticity using ECG-gated CT. Age is an important determinant that influence aortic elasticity, since it has been shown that arterial elasticity diminishes with advanced age. Pathologic studies in animals have shown that these declines can be explained by structural alteration of the arterial wall with elastic fibers that are damaged during aging [23,24]. The association between age and aortic elasticity has been reported in previous studies using different methods of assessment [16,25–27]. Our present results agree with those findings. The relationships between age and every elastic parameter can be described by Curve estimation. The findings of best fitting model between age and ADv, ACv, ADs, ACs which were shown in Fig. 3a–d indicate that greater slopes were seen in younger participants than in older participants. This result is consistent with reported study [14] and suggests that age influence aortic elasticity in younger people more than the older ones. The best fitting model between age and βv, βs displayed in Fig. 3e–f, which mean the curve flatten out from age 40–60years old. This finding suggests that stiffness index (β) as a parameter to evaluate arterial elasticity is less sensitive than others in middle-aged population. In further compares between elasticity parameters of various age groups (Table 1), the baseline clinical characteristics were similar among groups expect age and SBP which is supposed to increase with age [28]. Therefore, the results are independent of other potential influence factors that would relate with elasticity. We found that there were significant differences of elastic parameters between age groups except βv and βs between group A and B and βv between group B and C. This result is another evident to support the statement that β is less sensitive than other parameters when evaluating the elasticity in middle-aged people. The age-dependent decrease of elasticity demonstrated a natural process of aging of the aorta. However, the measurement of aortic elasticity integrates the cumulative effects of some other factors on the aortic wall. We analysed the relationship of elastic parameters and the clinical characteristics of normotensive subjects, including continuous and categorical variables. When we compared categorical variables, the other variables which could have influences in aortic elasticity also have been compared and proved to be not different. In our study, the factors independently related to decreased aortic elasticity were age, SBP, DBP, heart rate, smoking and hyperlipidemia. Although previous studies have shown that heart rate may be associated with aortic elasticity in healthy subjects, the relationship between them is still under investigation [29,30]. We can’t control other variables when accessed the relationship between heart rate and each elasticity parameters. So we are not sure whether the result reached in our study about heart rate and elasticity is solid. Regarding to smoking status, the significant decreased aortic elasticity occurred in smoking subjects and was independent of other factors. In pathologic aspect, smoking can impair endothelial progenitor cell and therefore influence the arterial function [31,32]. Fortunately, this process is reversible which means we can improve our impaired arterial function by abandon smoking [33,34]. In addition, the relationship of hyperlipidemia and aortic elasticity also can be observed, it was in line with what has been reported [35]. This result indicates that we should take more attention in hyperlipidemia which is more prevalent for the high-fat diet today than it used to be. Therefore, those potential factors that we’ve discussed above should be taken into account when the aortic elasticity of subject is evaluated. Unlike MRI and Echo, CCTA uses ionizing radiation. However, the radiation dose from CTA is no greater than that of comparable tests that use radiation, specifically as myocardial perfusion imaging or cardiac catheterization, it also has been extensively used in clinical as an efficient noninvasive tool for the detection of CAD with reducing downstream cost of testing [36]. The statement that thoracic ECGgated CT can be used to distinguish between pre- and post- capillary pulmonary hypertension has been reported in a latest study [37].
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Considering the wide range of application and prevalence of CCTA, it is very promising and significant if the same data could be used to gain additional information about elasticity of ascending aorta. Moreover, the participants in our study were free of established cardiovascular disease and were found not to have significant CAD by CCTA. Even at the early stage of atherosclerosis in which no major structural change was observed, aortic stiffness was significantly increased [38,39], which means the functional damages in arteries precede the structural changes. Recent studies also have shown that functional damages can be reversed quickly by therapies [40,41]. Therefore, it is crucial to detect the functional changes of arteries as soon as possible. Our findings indicated that CCTA provides an accurate evaluation for the function of aortic elasticity rather than just anatomical information. The capability of CCTA to acquire structural and functional information simultaneously in patients suspected of CAD enhances the clinical application of this method. Furthermore, the improvements of CT technology and post-processing software will benefit the proposed method in the future. There are some limitations in our study. First, the subjects involved in our study were suspected to have CAD, so it can not represent the general population of normotension. Second, noninvasive pressure measurements at the brachial artery could not match exactly the pressure at the ascending aorta. Considering amplification of pressure in the upper arm is obvious in young adults [42], so the subjects enrolled in our study are middle-age to elder. Third, we defined the origin of the left coronary as reference, however, there is some uncommon anatomic variation in its position relative to the sinotubular junction. We need to make some adjustment for some special subjects. 5. Conclusion The data of CCTA not only can detect the structural of coronary artery but also provide functional information of the ascending aortic elasticity. Our study findings suggest that the method of measuring the volume changes of ascending aorta is also a feasible way for evaluation of aortic elasticity. Age, BP levels, and several potential factors, which are associated with decreased aortic elasticity, should be taken into consideration when the aortic elasticity of subject is evaluated. Acknowledgments The authors thank Professor Rangke Wu, institute of Foreign Languages in the Southern medical University, for his language assistance and the engineers in Siemens Company for their software technical help. References [1] Mattace-Raso FU, van der Cammen TJ, Hofman A, van Popele NM, Bos ML, Schalekamp MA, Asmar R, Reneman RS, Hoeks AP, Breteler MM, Witteman JC. Arterial stiffness and risk of coronary heart disease and stroke: the Rotterdam Study. Circulation 2006;113:657–63. [2] Dernellis J, Panaretou M. Aortic stiffness is an independent predictor of progression to hypertension in nonhypertensive subjects. Hypertension 2005;45:426–31. [3] Laurent S, Boutouyrie P, Asmar R, Gautier I, Laloux B, Guize L, Ducimetiere P, Benetos A. Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients. Hypertension 2001;37:1236–41. [4] Boutouyrie P, Tropeano AI, Asmar R, Gautier I, Benetos A, Lacolley P, Laurent S. Aortic stiffness is an independent predictor of primary coronary events in hypertensive patients: a longitudinal study. Hypertension 2002;39:10–5. [5] Boese JM, Bock M, Schoenberg SO, Schad LR. Estimation of aortic compliance using magnetic resonance pulse wave velocity measurement. Phys Med Biol 2000;45:1703–13. [6] Vulliemoz S, Stergiopulos N, Meuli R. Estimation of local aortic elastic properties with MRI. Magn Reson Med 2002;47:649–54. [7] Wilson KA, Hoskins PR, Lee AJ, Fowkes FG, Ruckley CV, Bradbury AW. Ultrasonic measurement of abdominal aortic aneurysm wall compliance: a reproducibility study. J Vasc Surg 2000;31:507–13.
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The ascending aortic elasticity feature in normotensive subjects: evaluation with coronary CT angiography Wenqian Liang, Dandan Chen, Weicui Chen, Guanxun Cheng ⁎ Department of Imaging Center, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Ave. N., Guangzhou, Guangdong 510515, China
a r t i c l e
i n f o
Article history: Received 3 February 2014 Received in revised form 27 May 2014 Accepted 2 June 2014 Keywords: Aortic elasticity Computed tomography Risk factors Coronary CT angiography
a b s t r a c t To evaluate the ascending aortic elasticity feature, 118 normotensive subjects who underwent coronary computed tomography angiography (CCTA) were enrolled. Two groups of parameters assessing elasticity were calculated based on the measurements of volume and area of ascending aorta. Multivariate analysis revealed that some factors including age, systolic BP, diastolic BP, heart rate, smoking status and hyperlipidemia independently related to decreased aortic elasticity. Both measuring methods are applicable for evaluation of aortic elasticity. As the prevalence of CCTA, it is meaningful that CCTA can provide not only the structural details of ascending aorta but also functional information of the vessel elasticity. © 2014 Elsevier Inc. All rights reserved.
1. Introduction Aortic elasticity has been identified as an independent predictor of adverse cardiovascular diseases including hypertension, coronary events and stroke at the early stage in healthy subjects [1,2]. Furthermore, it is also an independent predictor of all-cause and cardiovascular mortality in hypertensive patients [3,4]. Therefore, early and accurate evaluation of the functional abnormality of aortic elasticity with non-invasive methods in normotensive patients becomes very significant. There are several methods and parameters for evaluating aortic elasticity. In recent years, the potential of a combined morphology and functional image protocol has made the imaging methods more attractive, such as magnetic resonance (MR), ultrasonography and computed tomographic (CT). However, MR measurements are unreliable in situations with complex flow patterns, limited by the long scan time and unavailable for patients with instable situation [5,6]. As for Ultrasound, it suffers inconstant applicability because of its result which is easy influenced by other factors [7,8]. The improvement of CT technology has made possible cardiac images with high temporal resolution [9,10]. It has been reported that vessel distensibility measurements obtained by using electrocardiographically gated multidetector-row (MD)CT was feasible in phantom, porcine specimen and aortic model of polydimethylsiloxane [11–13]. In addition, Ning Li et al. showed a negative correlation between age and ascending aortic elasticity by using electrocardiographically
⁎ Corresponding author. Department of Imaging Center, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Ave. N., Guangzhou, Guangdong 510515, China. E-mail address: [email protected] (G. Cheng). http://dx.doi.org/10.1016/j.clinimag.2014.06.003 0899-7071/© 2014 Elsevier Inc. All rights reserved.
(ECG)-gated dual-source (DS) CT [14]. However, the methods and procedures of assessment of arterial elasticity are different in studies using CT [14–18]. Some measured certain cross-sectional area (CSA) that paralleling to the axis of human body for evaluation of aortic distensibility or stiffness [15–18], while some measured certain CSA that perpendicular to the trace that passed through the centre of the vessel [14]. Also, which method is more appropriate and applicable has not been studied. In this study, we evaluated the elasticity of ascending aorta in normotensive patients with two different measuring methods using ECG-gated DSCT. The method of measuring the volume changes of vessel has not been reported. Thus, we tested the hypothesis that the volume method is also an applicable method for evaluation of aortic elasticity. The aim of the present study was to evaluate the ECG-gated DSCT in the assessment of ascending aortic elasticity in normotensive subjects and demonstrate the risk factors associated with reduced aortic elasticity.
2. Materials and methods 2.1. Subjects From February 2012 to January 2013, several hundreds of people who are suspected coronary artery disease (CAD) have performed CCTA in our institution Subjects were excluded if CCTA image quality was poor for the detection due to motion artifacts. Subjects with aortic diseases, CAD, secondary hypertension, diabetes, renal dysfunction or history of any known systemic illness were also excluded. Finally, 118 patients with normotension (87 males, 31 females, aged 26–80 years with a mean of 53.28±11.09 years) were enrolled. Normotensive subjects were divided into four groups according to their age: group A
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b45 years (32 cases), group B 45–54 years (37 cases), group C 55–64 years (25 cases), group D N64 years (24 cases). The lifestyle and clinical characteristics of all participants were obtained by completion of a questionnaire and checked by interview. We collected information including age, sex, body mass index (BMI), heart rate during scanning (without any drugs to reduce heart rate), blood press (BP) before image acquisition, smoking status, alcohol consumption and hyperlipidemia. The BMI was calculated by dividing body weight (kg) by the square of height (m). BP was measured twice before examination using a validated automated Omron 705CP (Omron 705cp, Kyoto, Japan) and the mean value of the measurements were recorded. Smoking status was defined as current smoking (quit smoking less than half a year and smoking more than two cigarettes a day) and nonsmoking. Alcohol consumption was assessed by the alcohol intake ≥2 times per week by the subjects. Hyperlipidemic subjects were defined as people with abnormal lipid levels or receiving lipid-lowing therapy. The study was approved by the ethical committee, and informed consents including coronary examination and our special study protocol were obtained from all enrolled subjects before examination.
2.2. Image acquisition Image acquisition was performed with CCTA using ECG-gated DSCT (Somatom Definition, Siemens Healthcare, Forchheim, Germany). Routine CCTA protocol was performed to determine a scanning range from tracheal juga to the diaphragm by the calcium score scan. Then, 75–85 ml of contrast medium (Iopamiron 370, Bayer Schering Pharma AG, Berlin, Germany), according to the patient’s weight, was injected through a dual-head injector into cubital vein at a rate of 5.0–5.5 ml/s, followed by 30 ml of saline solution chaser, using a bolus tracking technique at the slice of aortic root to determine the trigger time. When the density reached a predefined threshold of 90 Hounsfield units (HU), the scan started automatically with a 6-s scan delay during one breath-hold with simultaneous recording of the ECGtracing. The image parameters were a slice collimation of 32×0.6 mm, slice acquisition 64×0.6 mm by means of a z-axis flying focal spot, 120 KV, 270 mA, 0.33 s rotation time, with an 83-ms temporal resolution.
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2.3. Reconstruction and measurements From the raw data, images were reconstructed every 5% (5–100%) of the RR interval with an effective slice thickness of 0.75-mm, an increment of 0.5-mm, and B26f reconstruction kernel. For every subject, it can be obtained 20 different phases of time-resolved image series during cardiac cycle. 2.3.1. Volume measurement process We used Volume software (Siemens AG, Germany) to measure the volume of ascending aorta under different phases. The contour of ascending aorta was manually delineated. The region of interest (ROI) was chosen from the level of the beginning of left coronary artery to 61 slices (3.08 cm) above it. The window level and width of 200 and 800 HU was used. The volume of ROI in ascending aorta was automatically detected (Fig. 1). 2.3.2. Area measurement process We used Vessel Analysis software (Siemens AG, Germany) to measure the area of ascending aorta under different phases. Makers were placed at the sinotubular junction and the ascending aorta. A line that passed through the centre of the vessel was automatically generated. We chose the plane of the beginning of left coronary artery as the reference and defined 25 mm above it as the ROI. The window level and width of 200 and 800 HU was also used. The CSA of ROI which was perpendicular to the trace of vessel was automatically measured. (Fig. 2) The aorta is a tube with geometric curvature and changes its shape regularly during the cardiac cycle. We measured the volumes and areas of ascending aorta among 20 different phases, and wrote down the maximum and minimum luminal volume/area. The volumes and areas were measured by two independent observers at the same time and by one experienced observer twice at two separate times. The average value of the three measurements was used as the reference data(Vs, Vd, cm 3; Ss, Sd, mm 2). 2.4. Aortic elasticity evaluation In our study, there were several different parameters to evaluate the elastic properties of the ascending aorta, including aortic distensibility
Fig. 1. Measurement of volume. (a–b) Example of measurement at the same region 3.08cm above the beginning of left coronary artery observed in a 42-year-old man. The maximum and minimum volume of the ROI occurred at 20% interval (a) and 100% interval (b) respectively.
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Fig. 2. Measurement of area. (a–b) Example of measurement at the same cross-section 2.5cm above the beginning of left coronary artery observed in a 42-year-old man. The maximum and minimum area of the ROI occurred at 20% interval (a) and 100% interval (b) respectively.
(AD, ×10 −3mmHg−1), aortic compliance (AC, mm2/mmHg) and aortic stiffness (β) which were reported by previously publications [19–21]: AD ¼ ½ðSs−SdÞ=Sd=ðSBP−DBPÞ; AC ¼ ðSs−SdÞ=ðSBP−DBPÞ; β ¼ ln ðSBP=DBPÞ=½ðDs−DdÞ=Dd Ss and Sd were defined as maximum luminal area and minimum luminal area, respectively. Ds and Dd were defined as maximum lumen diameters and minimum lumen diameters which were calculated from those areas with the assumption that the crosssection was circular (D=2×[area/π] 1/2). The average areas were obtained according to the volume of the ascending aorta using the formula S=volume/height. Therefore, the average cross-sectional areas which parallel to the axis of human body were converted by maximum and minimum volume of the ROI, the parameters (ADv, ACv, βv) of the ascending aortic elasticity were calculated; Another group of aortic elastic parameters (ADs, ACs, βs) were calculated by the cross-sectional areas which perpendicular to the center curve of the ascending aorta. 2.5. Statistical analysis Statistical analysis was performed using SPSS13.0 statistical package (SPSS, Chicago, IL, USA). All continuous variables were expressed as means+standard deviations. Paired-Samples t test was
used to identify the interobserver differences and intraobserver differences. Differences among groups were tested using independent t test or one-way analysis of variance (ANOVA), Least-significant difference (LSD) or Dunnett’s T3 was used for multiple comparisons. Categorical variables were reported as percentage and compared by chi-square test. Linear regression models were used to determine the association of elastic parameters with clinical characteristics including continuous and categorical variables. Multivariate linear regression models were used to assess the independent association of these factors with aortic elasticity parameters. Curve estimation was used to express the relationship between age and every elastic parameter. A statistically significant difference was defined as a two-sided P less than 0.05. 3. Results 3.1. Regulation and accuracy of measurement The images showed a high contrast between the vessel full of contrast medium and the surrounding fatty tissue. The volumes and cross-sectional areas of the ROI changed regularly during the cardiac cycle. Monitoring different phases throughout the cardiac cycle of all participants, the ascending aorta were found to achieve the maximum volume and area at an RR interval of 23.09±3.51% and the minimum at an RR interval of 97.03±2.55%. There were excellent interobserver and intraobserver agreement in measurements.
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3.2. Association between age and aortic elasticity in normotensive subjects Age showed significant correlation with elasticity parameters in normotensive participants. There was an age-dependent decrease of ADv (rp=−0.816, P=.000), ACv (rp=−0.763, P=.000), ADs (rp=−0.820, P=.000), ACs (rp=−0.769, P=.000) and increase of βv (rp=0.822, P=.000), βs (rp=0.826, P=.000). Curve estimation showed the best fitting model was Compound model for age and
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ADv (R 2=0.698), ACv (R2=0.652), ADs (R2=0.710), ACs (R2=0.669) and cubic model for age and βv (R2=0.742), βs (R2=0.769) (Fig. 3). In order to further evaluate the relationship between age and elastic parameters, the participants were divided into four age groups (A–D). Table 1 shows the clinical characteristics and elasticity parameters of normotensive patients in A–D groups. There were no difference between groups in respect to clinical characteristics including continuous and categorical variables except age and systolic BP (SBP). The elasticity parameters among the age groups were
Fig. 3. Fitting curves of curve estimation between age and aortic elastic parameters. The relationship between age and parameters showed that ADv (a), ADs (b), ACv (c), ACs (d) deceased and βv (e), βs (f) increased with age. The best fitting model was Compound model for age and ADv (a), ACv (b), ADs (c), ACs (d) and Cubic model for age and βv (e), βs (f) (ADv, ADs=aortic distensibility (×10−3mmHg−1); ACv, ACs=aortic compliance (mm2/mmHg); βv, βs=aortic stiffness index.
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Table 1 Clinical characteristics and elasticity parameters of normotensive participants grouped by age Age groups
Age (years) Male gender (%) BMI (kg/m2) SBP (mmHg) DBP (mmHg) HR (beats/min) Smoking (%) Drink (%) Hyperlipidemia (%) ADv (×10−3mmHg−1) ACv (mm2/mmHg) βv ADs (×10−3mmHg−1) ACs (mm2/mmHg) βs
P value
b45(A) (n=32)
45~54(B) (n=37)
55–64(C) (n=25)
N64(D) (n=24)
40.44±4.18+Δ# 75% 23.90±2.69 117.28±8.36# 78.69±6.98 68.69±8.50 21.9% 21.9% 25% 4.23±1.37+Δ# 2.75±0.72+Δ# 5.62±1.82Δ# 4.55±1.56+Δ# 2.81±0.79+Δ# 5.35±1.99Δ#
50.11±2.246⁎Δ# 83.8% 25.26±2.70 119.84±9.33⁎# 79.76±6.40 72.19±9.62 29.7% 29.7% 29.7% 2.97±1.15⁎Δ# 2.24±0.65⁎Δ#
58.24±2.70⁎+# 68% 23.74±2.40 120.60±9.01 77.20±8.59 69.48±9.82 24% 16% 24% 2.12±0.51⁎+# 1.79±0.48⁎+# 10.46±2.29⁎# 2.07±0.55⁎+# 1.66±0.52⁎+# 10.82±2.59⁎+#
70.13±4.25⁎+Δ 62.5% 23.51±3.71 125.17±9.92⁎+ 76.88±8.86 72.17±10.59 29.2% 16.7% 33.3% 1.10±0.47⁎+Δ 1.03±0.37⁎+Δ 22.52±10.74⁎+Δ 1.05±0.52⁎+Δ 0.93±0.41⁎+Δ
7.83±2.26# 3.03±1.26⁎Δ# 2.17±0.69⁎Δ#♦ 7.76±2.35Δ#
24.38±11.97+Δ
0.000 0.268 0.069 0.019 0.424 0.356 0.869 0.530 0.865 0.000 0.000 0.000 0.000 0.000 0.000
Abbreviations: HR=heart rate; ADv, ADs=aortic distensibility (×10−3mmHg−1); ACv, ACs=aortic compliance (mm2/mmHg); βv, βs=aortic stiffness index. *Pb0.05 between group A; +Pb0.05 between group B; ΔPb0.05 between group C; # Pb0.05 between group D.
compared. Statistically significant differences in elastic parameters were found among all age groups, ADv, ACv, ADs, ACs decreased while βv and βs increased with the increase of age. There were significant differences of every different elastic parameter between each pair of age groups in multiple comparisons except βv and βs between group A and B and βv between group B and C. 3.3. Factors associated with aortic elasticity in normotensive subjects To identify the independent factors associated with the aortic elasticity, the relationships between every clinical characteristics and elastic parameters were analyzed separately in all participants, which were showed in Table 2. In normotensive subjects, the further multiple regression analysis showed ADv, ADs were independently associated with age, SBP, heart rate, smoking status and hyperlipidemia (R 2=0.740, P=.000;R 2=0.740, P=.000). ACv was independently associated with age, SBP, diastolic BP (DBP), heart rate, hyperlipidemia (R 2=.786, P=.000). ACs was independently associated with age, SBP, DBP, heart rate, smoking status and hyperlipidemia (R2=0.768, P=.000). βv and βs were independently associated with age, DBP, smoking status and hyperlipidemia (R 2=0.688, P=.000; R 2=0.681, P=.000). 4. Discussion Aortic elasticity is an important functional property of aorta and serves as an indicator of serious diseases [1–4]. Multisliced
Table 2 Relationship of aortic elasticity parameters to clinical characteristics from the simple linear regression analysis Variable
Standardized regression coefficients ADv
Age (years) Gender (M/F) BMI (kg/m2) SBP (mmHg) DBP (mmHg) HR (beats/min) Smoking (N/Y) Drink (N/Y) Hyperlipidemia (N/Y)
ACv
βv
−0.752⁎ −0.033 −0.002 −0.553⁎ 0.092 −0.269⁎ −0.236⁎
−0.731⁎ 0.764⁎ −0.104 0.053 0.104 −0.164 −0.496⁎ 0.236⁎ 0.234⁎ −0.284⁎ −0.200⁎ 0.127 −0.229⁎ 0.235⁎ −0.089 −0.093 0.029 −0.256⁎ −0.259⁎ 0.238⁎
⁎ Pb0.05 See Table 1 for the abbreviation.
ADs −0.755⁎ −0.055 0.021 −0.532⁎ 0.072 −0.296⁎ −0.246⁎
ACs
βs
−0.739⁎ 0.773⁎ −0.115 0.070 0.108 −0.169 −0.480⁎ 0.245⁎ 0.202⁎ −0.264⁎ −0.233⁎ 0.161 −0.231⁎ 0.210⁎ −0.101 −0.097 0.038 −0.254⁎ −0.255⁎ 0.226⁎
(MS)CT as a noninvasive tool allows the precise assessment of aortic elasticity and the detection of structural lesions in a single image acquisition. Previous studies have demonstrated that aortic elasticity estimated through MSCT is feasible [11–18]. In our study, retrospective ECG-gated CCTA was used to evaluate the functional change of aorta without additional examination and radiation. By reconstructing the original data of CCTA at different phases, we used two different methods to measure the changes of ascending aorta during cardiac cycle. The elasticity of ascending aorta was evaluated by converting the results measured by two methods into two groups of elastic parameters. As we know, heart and great vessels have regular movements during cardiac cycle in different directions, so the shape and location of ascending aorta are affected by heartbeat [22]. In order to identify the same region of the ascending aorta during its movement in cardiac cycle, we choose the beginning of left coronary artery as reference point. It is a reliable and expedient point according to our experience during the study. Furthermore, our study showed the ascending aorta achieved the maximum volume and area at different RR intervals in different participants. Since the movement of ascending aorta in every participant is diverse, our study is more reliable than the previous study using 35% and 95% as the fixed maximum and minimum measurement phases in every subject, respectively [15]. We used two methods to determine the changes in CSA of the ascending aorta during cardiac cycle. First, by using Volume software, the volume of ascending aorta was detected to calculate the average CSA which was parallel to the axis of human body. Second, by using Vessel Analysis software, we detected the CSA which was perpendicular to the center curve of the ascending aorta vascular. In our study, we found that the first method is more stable and convenient to identify the same region in different phases which is very essential for measurement of the change of aorta during cardiac cycle. However, the parameters calculated by the volume changes of ascending aorta reflect the elasticity of vascular area which is parallel to the axis of human body. Unlike the method using in most recent studies that can only reflect the elasticity of certain vascular plane [15–18], our method can reflect the average elasticity of certain vascular tube. On the other hand, the second method can reflect the actual vascular area which perpendicular to the whole vessel, nevertheless it rely on the automatically generated line along the centre of the vessel. This kind of line was generated by computer and appeared to be different every single time. In this way, it is very hard to identify the same area in different phases. Therefore, these two methods were both used to detect the changed areas of ascending aorta, and then we calculated two groups of parameters (ADv, ACs, βv, ACv, ADs, βs) to analyze the vascular elasticity in our study, obtaining a
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more objective and comprehensive insight in assessing aortic elasticity using ECG-gated CT. Age is an important determinant that influence aortic elasticity, since it has been shown that arterial elasticity diminishes with advanced age. Pathologic studies in animals have shown that these declines can be explained by structural alteration of the arterial wall with elastic fibers that are damaged during aging [23,24]. The association between age and aortic elasticity has been reported in previous studies using different methods of assessment [16,25–27]. Our present results agree with those findings. The relationships between age and every elastic parameter can be described by Curve estimation. The findings of best fitting model between age and ADv, ACv, ADs, ACs which were shown in Fig. 3a–d indicate that greater slopes were seen in younger participants than in older participants. This result is consistent with reported study [14] and suggests that age influence aortic elasticity in younger people more than the older ones. The best fitting model between age and βv, βs displayed in Fig. 3e–f, which mean the curve flatten out from age 40–60years old. This finding suggests that stiffness index (β) as a parameter to evaluate arterial elasticity is less sensitive than others in middle-aged population. In further compares between elasticity parameters of various age groups (Table 1), the baseline clinical characteristics were similar among groups expect age and SBP which is supposed to increase with age [28]. Therefore, the results are independent of other potential influence factors that would relate with elasticity. We found that there were significant differences of elastic parameters between age groups except βv and βs between group A and B and βv between group B and C. This result is another evident to support the statement that β is less sensitive than other parameters when evaluating the elasticity in middle-aged people. The age-dependent decrease of elasticity demonstrated a natural process of aging of the aorta. However, the measurement of aortic elasticity integrates the cumulative effects of some other factors on the aortic wall. We analysed the relationship of elastic parameters and the clinical characteristics of normotensive subjects, including continuous and categorical variables. When we compared categorical variables, the other variables which could have influences in aortic elasticity also have been compared and proved to be not different. In our study, the factors independently related to decreased aortic elasticity were age, SBP, DBP, heart rate, smoking and hyperlipidemia. Although previous studies have shown that heart rate may be associated with aortic elasticity in healthy subjects, the relationship between them is still under investigation [29,30]. We can’t control other variables when accessed the relationship between heart rate and each elasticity parameters. So we are not sure whether the result reached in our study about heart rate and elasticity is solid. Regarding to smoking status, the significant decreased aortic elasticity occurred in smoking subjects and was independent of other factors. In pathologic aspect, smoking can impair endothelial progenitor cell and therefore influence the arterial function [31,32]. Fortunately, this process is reversible which means we can improve our impaired arterial function by abandon smoking [33,34]. In addition, the relationship of hyperlipidemia and aortic elasticity also can be observed, it was in line with what has been reported [35]. This result indicates that we should take more attention in hyperlipidemia which is more prevalent for the high-fat diet today than it used to be. Therefore, those potential factors that we’ve discussed above should be taken into account when the aortic elasticity of subject is evaluated. Unlike MRI and Echo, CCTA uses ionizing radiation. However, the radiation dose from CTA is no greater than that of comparable tests that use radiation, specifically as myocardial perfusion imaging or cardiac catheterization, it also has been extensively used in clinical as an efficient noninvasive tool for the detection of CAD with reducing downstream cost of testing [36]. The statement that thoracic ECGgated CT can be used to distinguish between pre- and post- capillary pulmonary hypertension has been reported in a latest study [37].
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Considering the wide range of application and prevalence of CCTA, it is very promising and significant if the same data could be used to gain additional information about elasticity of ascending aorta. Moreover, the participants in our study were free of established cardiovascular disease and were found not to have significant CAD by CCTA. Even at the early stage of atherosclerosis in which no major structural change was observed, aortic stiffness was significantly increased [38,39], which means the functional damages in arteries precede the structural changes. Recent studies also have shown that functional damages can be reversed quickly by therapies [40,41]. Therefore, it is crucial to detect the functional changes of arteries as soon as possible. Our findings indicated that CCTA provides an accurate evaluation for the function of aortic elasticity rather than just anatomical information. The capability of CCTA to acquire structural and functional information simultaneously in patients suspected of CAD enhances the clinical application of this method. Furthermore, the improvements of CT technology and post-processing software will benefit the proposed method in the future. There are some limitations in our study. First, the subjects involved in our study were suspected to have CAD, so it can not represent the general population of normotension. Second, noninvasive pressure measurements at the brachial artery could not match exactly the pressure at the ascending aorta. Considering amplification of pressure in the upper arm is obvious in young adults [42], so the subjects enrolled in our study are middle-age to elder. Third, we defined the origin of the left coronary as reference, however, there is some uncommon anatomic variation in its position relative to the sinotubular junction. We need to make some adjustment for some special subjects. 5. Conclusion The data of CCTA not only can detect the structural of coronary artery but also provide functional information of the ascending aortic elasticity. Our study findings suggest that the method of measuring the volume changes of ascending aorta is also a feasible way for evaluation of aortic elasticity. Age, BP levels, and several potential factors, which are associated with decreased aortic elasticity, should be taken into consideration when the aortic elasticity of subject is evaluated. Acknowledgments The authors thank Professor Rangke Wu, institute of Foreign Languages in the Southern medical University, for his language assistance and the engineers in Siemens Company for their software technical help. References [1] Mattace-Raso FU, van der Cammen TJ, Hofman A, van Popele NM, Bos ML, Schalekamp MA, Asmar R, Reneman RS, Hoeks AP, Breteler MM, Witteman JC. Arterial stiffness and risk of coronary heart disease and stroke: the Rotterdam Study. Circulation 2006;113:657–63. [2] Dernellis J, Panaretou M. Aortic stiffness is an independent predictor of progression to hypertension in nonhypertensive subjects. Hypertension 2005;45:426–31. [3] Laurent S, Boutouyrie P, Asmar R, Gautier I, Laloux B, Guize L, Ducimetiere P, Benetos A. Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients. Hypertension 2001;37:1236–41. [4] Boutouyrie P, Tropeano AI, Asmar R, Gautier I, Benetos A, Lacolley P, Laurent S. Aortic stiffness is an independent predictor of primary coronary events in hypertensive patients: a longitudinal study. Hypertension 2002;39:10–5. [5] Boese JM, Bock M, Schoenberg SO, Schad LR. Estimation of aortic compliance using magnetic resonance pulse wave velocity measurement. Phys Med Biol 2000;45:1703–13. [6] Vulliemoz S, Stergiopulos N, Meuli R. Estimation of local aortic elastic properties with MRI. Magn Reson Med 2002;47:649–54. [7] Wilson KA, Hoskins PR, Lee AJ, Fowkes FG, Ruckley CV, Bradbury AW. Ultrasonic measurement of abdominal aortic aneurysm wall compliance: a reproducibility study. J Vasc Surg 2000;31:507–13.
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