To date, the main cardiovascular application of TDI (tissue Doppler imaging) has been in myocardial evaluation. In the present study, we investigated the feasibility and reproducibility of assessing arterial elasticity using the DC (distensibility coefficient) measured by TDI, the correlation of this with the DC obtained by other methods and the DC in patients with various degrees of cardiovascular risk. We studied 450 subjects (256 men; age, 51±10 years) with and without risk factors of cardiovascular disease. Arterial displacement was measured from TDI, and B-mode and M-mode images of the common carotid artery in the longitudinal plane, and the DC with each method was compared. Linear regression showed a good correlation between all three methods. The results for TDI and B-mode were comparable [(21±10) compared with (21±10)×10−3/kPa respectively; P=not significant], but there were significant differences between TDI and M-mode [(21±10) compared with (31±13)×10−3/kPa respectively; P<0.0001] and between B-mode and M-mode [(21±10) compared with (31±13)×10−3/kPa respectively; P<0.0001]. Similarly, Bland–Altman analysis showed the least variability in the DC between TDI and B-mode, and there were no significant differences between the average measurements. The TDI DC also had the lowest paired difference for inter-observer variability [(−0.1±1.1)×10−3/kPa; P=not significant]. In conclusion, the results of the present study suggest that TDI of the carotid arteries is feasible, comparable with B-mode measurements, more robust than M-mode and less variable than the other methods.

INTRODUCTION

Assessment of IMT (intima-medial thickness) in the carotid arteries is a valuable test for the identification of subclinical atherosclerotic burden in patients at risk of vascular disease and one that has prognostic value [1,2]. Increased arterial stiffness is an important contributor to increased SBP [systolic BP (blood pressure)] and PP (pulse pressure) [3,4], which are important determinants of the risk of cardiovascular disease [5,6]. However, although the measurement of arterial stiffness might be used to characterize arterial health [4], the plethora of approaches to this problem attests to its difficulty. The most widely applied approach has been the evaluation of arterial waveforms, obtained by applanation tonometry of compressible arteries (usually the radial artery). A central pressure waveform can be generated using a transfer function [79], although this may present potential problems [10], and the adoption of this technique has been adversely impacted by a lack of familiarity with tonometry and the need to obtain specialized equipment.

The incorporation of determining vascular stiffness with measurement of carotid IMT could solve issues of both familiarity and availability. In previous studies, echo-tracking was used as the ‘gold standard’ for measurement of the diameter change of arteries by automatically tracking the vessel diameter over the cardiac cycle using either B-mode or M-mode ultrasound or both [1113]. This technique is based on the acquisition of RF (radio frequency) data from ultrasound images, but it has lacked appeal clinically, due to the need for specialized equipment and software, and the difficulty in exporting RF data from commercially available ultrasound systems.

Doppler ultrasound, used traditionally to evaluate the velocity and direction of blood flow in the heart and vessels, can be used to evaluate low-velocity high-amplitude signals which come from tissue by reduction of the wall filters and scale [14,15]. The use of colour TDI (tissue Doppler imaging) permits the rapid simultaneous visualization of multiple structures in a single view. To date, the main cardiovascular application of this technique has been in myocardial evaluation [16]. In the present study, we investigated the feasibility and reproducibility of assessing arterial elasticity using displacement measured by TDI, the correlation of this with measures of arterial diameter change described previously and the measured displacement of the carotid artery in patients at various degrees of cardiovascular risk.

MATERIAL AND METHODS

Patient selection

Primary prevention patients (n=450; 256 men; age 51±12 years) with and without cardiovascular risk factors were recruited from community or hospital-based prevention clinics. Clinical data, BPs, biochemical profiles and medication details were obtained to identify the following risk factors: age (>50 years in men and >60 years in women), hypertension (on treatment for diagnosed hypertension or BP >140/80 mmHg), total cholesterol (on treatment for diagnosed hyperlipidaemia or total cholesterol >5.5 mmol/l), diabetes [previously diagnosed diabetes or fasting blood sugar >7.0 mmol/l (>126 mg/dl)], current smoking and renal impairment [(previously diagnosed or creatinine >120 μmol/l (>1.36 mg/dl)]. Patients were divided into three groups: low (no risk factors; n=124), medium (1–2 cardiac risk factors; n=123) and high (three or more cardiac risk factors; n=203).

The studies were approved by the Human Research Ethics Committee of Princess Alexandra Hospital, and all patients gave informed written consent.

Carotid imaging

All patients were studied with TDI and grey-scale imaging of the carotid arteries. The carotid arteries were scanned longitudinally in the anterior, lateral and posterior aspects 2–10 cm prior to the bifurcation, and digital grey scale cine loops were acquired for offline analysis (Philips 5000; Philips Medical Systems). BP was measured once in the supine position using a standard sphygmomanometer on the right brachial artery after the patient had been allowed to rest for 5–10 min, and the same BP was used to determine the DC (distensibility coefficient) for all three methods.

Carotid Doppler imaging

Longitudinal images of the carotid arteries giving the best quality lumen/vessel interface 2–10 cm prior to the bifurcation were obtained. The image was optimized for both B-mode imaging (150 dB dynamic range; optimized for penetration, low persistence and at maximum frame rate) and colour Doppler (gain set at 100%; pulse repetition frequency >200Hz), using the smallest possible ROI (region of interest) box to achieve the highest frame rate (usually 160–220 frames/s). Care was taken not to include any discreet plaques in the tissue Doppler measurements. Loops of 3–5 cardiac cycles with TDI were acquired digitally for offline analysis.

Derivation of the DC (distensibility coefficient)

The tissue Doppler images were analysed offline using custom-written software (AWM v1.05; Philips Medical Systems), which extracts the velocity information for the ROI area over the cardiac cycle, and with a processing algorithm that generates values for vessel wall displacement (in microns) over time [17,18]. These data were then saved and exported in numerical format for analysis (Figure 1). The DC was calculated from the extracted displacement data as 2× [(net displacement/maxD)/PP], where net displacement is the maximum−minimum displacement (in μm), maxD is the maximum arterial diameter (obtained from B-mode) and PP was calculated from SBP−DBP (diastolic BP). Two-dimensional measurements of the artery were then performed in the same segment of the same cine loop with the tissue Doppler image hidden in the background. The artery was measured offline (HDI Lab v1.91d; Philips Medical Systems) at end-systole and end-diastole, from the leading edge of the adventitia/media interface in the anterior wall to the leading edge of the lumen/endothelial interface in the posterior wall, according to the American Society of Echocardiography standards [19], to obtain maximal and minimal dimensions by B-mode ultrasound. M-mode ultrasound was then created by post-processing the same cine loop offline (HDI Lab v1.91d; Philips Medical Systems) in the same arterial segment and measured in the same manner (Figure 2). DC (expressed as 10−3/kPa) was calculated as 2×[(ΔD/minD)/PP], where ΔD is the maximum−minimum diameter and minD is the minimum diameter [11,20]. Because the same cine loop was used for all measurements, PP was calculated from the BP taken at rest.

Arterial wall motion analysis window showing (A) the extracted arterial displacement over the cardiac cycle, (B) individual displacement curves over time for each cardiac cycle, and (C) the mean displacement for all of the cardiac cycles

Figure 1
Arterial wall motion analysis window showing (A) the extracted arterial displacement over the cardiac cycle, (B) individual displacement curves over time for each cardiac cycle, and (C) the mean displacement for all of the cardiac cycles
Figure 1
Arterial wall motion analysis window showing (A) the extracted arterial displacement over the cardiac cycle, (B) individual displacement curves over time for each cardiac cycle, and (C) the mean displacement for all of the cardiac cycles

B-mode (left-hand panels) and M-mode (right-hand panels) images of the common carotid artery with measurement at end-systole and end-diastole

Figure 2
B-mode (left-hand panels) and M-mode (right-hand panels) images of the common carotid artery with measurement at end-systole and end-diastole

Arteries were measured from leading edge to leading edge, i.e. the media/adventitia interface in the anterior wall to the lumen/endothelial interface in the posterior wall to obtain minimum and maximum diameters.

Figure 2
B-mode (left-hand panels) and M-mode (right-hand panels) images of the common carotid artery with measurement at end-systole and end-diastole

Arteries were measured from leading edge to leading edge, i.e. the media/adventitia interface in the anterior wall to the lumen/endothelial interface in the posterior wall to obtain minimum and maximum diameters.

Statistical analysis

Linear regression was used to determine the relationship between DC by each method, paired Student's t tests were used to determine paired differences in DC between the methods and, finally, Bland–Altman analysis was used to determine the differences from the mean between methods. ANOVA with Bonferoni's post-hoc test was then used to compare subgroups of patients defined by cardiovascular risk. In a subset of patients, paired Student's t tests and Bland–Altman analysis were used to determine inter- and intra-observer variability.

RESULTS

Patient characteristics

Table 1 summarizes the clinical data, medication details and haemodynamics for the whole patient cohort. Approx. half of the patients were men, with moderately increased BMI (body mass index); however, BP and cardiac output were within the normal range. There were 54% of men over 50 years of age but only 24% of women over 60 years of age. There was an average of two cardiac risk factors as well as a high prevalence for the use of antihypertensive medication.

Table 1
Clinical characteristics of the study subjects

Values are means±S.E.M., or numbers (percentage). ACE, angiotensin-converting enzyme; MAP, mean arterial pressure; CO, cardiac output, TAC, total arterial compliance.

Characteristic
n 450 
Age 51±12 
Gender (male) (n256 (56%) 
Height (cm) 169±10 
Weight (kg) 88±22 
BMI (kg/m230±6.6 
SBP (mmHg) 127±18 
DBP (mmHg) 79±10 
MAP (mmHg) 97±39 
PP (mmHg) 48±14 
CO (litres/min) 5.07±1.4 
Smoking (n134 (29%) 
Patients with  
 Hypertension (n217 (48%) 
 Diabetes (n200 (44%) 
 Increased lipids (n157 (33%) 
Males >50 years of age (n141 (54%) 
Females >60 years of age (n46 (24%) 
Medication (n 
 β-Blocker 50 (11%) 
 ACE-inhibitor 77 (17%) 
 Calcium-channel blocker 120 (26%) 
 Statin 130 (28%) 
Number of risk factors 2±1 
Number of medications 1±1 
Characteristic
n 450 
Age 51±12 
Gender (male) (n256 (56%) 
Height (cm) 169±10 
Weight (kg) 88±22 
BMI (kg/m230±6.6 
SBP (mmHg) 127±18 
DBP (mmHg) 79±10 
MAP (mmHg) 97±39 
PP (mmHg) 48±14 
CO (litres/min) 5.07±1.4 
Smoking (n134 (29%) 
Patients with  
 Hypertension (n217 (48%) 
 Diabetes (n200 (44%) 
 Increased lipids (n157 (33%) 
Males >50 years of age (n141 (54%) 
Females >60 years of age (n46 (24%) 
Medication (n 
 β-Blocker 50 (11%) 
 ACE-inhibitor 77 (17%) 
 Calcium-channel blocker 120 (26%) 
 Statin 130 (28%) 
Number of risk factors 2±1 
Number of medications 1±1 

Comparison of the DC by TDI with B-mode and M-mode

The results for the TDI and B-mode DCs were comparable [(21±10) compared with (21±10)×10−3/kPa; P=not significant], but there were significant differences between the TDI and M-mode DCs [(21±10) compared with (31±13)×10−3/kPa; P<0.0001] and between the B-mode and M-mode DCs [(21±10) compared with (31±13)×10−3/kPa; P<0.0001].

Linear regression showed a good correlation between all three methods, with the correlation between the TDI and B-mode DCs being the best. Similarly, Bland–Altman analysis showed the least variability in the DCs between TDI and B-mode, and there were no significant differences between the average measurements (Figure 3).

Linear regression (left-hand panels) and Bland–Altman plots (right-hand panels) for the relationships between DCs determined by TDI, B-mode and M-mode

Figure 3
Linear regression (left-hand panels) and Bland–Altman plots (right-hand panels) for the relationships between DCs determined by TDI, B-mode and M-mode

The regression fit is shown as a solid line, and the line of identity is shown as the broken line. Axis label values are 10−3/kPa. The Bland–Altman plots show the mean and differences from the mean for the three analyses, with the y axis values as means±2S.D. NS, not significant.

Figure 3
Linear regression (left-hand panels) and Bland–Altman plots (right-hand panels) for the relationships between DCs determined by TDI, B-mode and M-mode

The regression fit is shown as a solid line, and the line of identity is shown as the broken line. Axis label values are 10−3/kPa. The Bland–Altman plots show the mean and differences from the mean for the three analyses, with the y axis values as means±2S.D. NS, not significant.

Determination of differences by cardiovascular risk

All three methods showed similar differences in the DC between the subgroups divided according to cardiovascular risk (all P<0.0001). Once again, there were no differences between the DC determined by TDI and B-mode, but the DC determined by M-mode was significantly higher (Figure 4). The DC determined by TDI also had the smallest S.D. from the mean, suggesting that it may be the least variable of the measures.

Error bars for the DCs determined by TDI, B-mode and M-mode in patients at low, medium and high cardiovascular risk

Figure 4
Error bars for the DCs determined by TDI, B-mode and M-mode in patients at low, medium and high cardiovascular risk

Error bars represent 95% confidence intervals. 2D, B-mode; Med, medium.

Figure 4
Error bars for the DCs determined by TDI, B-mode and M-mode in patients at low, medium and high cardiovascular risk

Error bars represent 95% confidence intervals. 2D, B-mode; Med, medium.

Inter- and intra-observer variability

Tables 2 and 3 summarize the results of paired Student's t tests in a subgroup of 25 patients for the inter- and intra-observer variability respectively. For the inter-observer variability, there were no significant differences between observers for the DC determined by M-mode or TDI, and the TDI DC had the smallest difference between observers [(−0.07±1.1)×10−3/kPa]. However, there was a significant difference in the DC determined by B-mode between the observers. For the intra-observer variability, there were no significant differences between any of the methods between reads, and once again the TDI DC had the smallest paired difference [(−0.3±0.8)×10−3/kPa].

DISCUSSION

The results of the present study show a good correlation between the DC determined by TDI with B-mode and M-mode ultrasound. Ultrasonic imaging of the carotid arteries is widely used and very feasible, and DC determined by TDI has the potential for simplifying the assessment of arterial elasticity. The results also indicate that the TDI DC is a sensitive marker of between-group differences in patients at varying risk of cardiovascular disease.

Imaging approaches to the measurement of arterial characteristics

Although measurement of carotid IMT is a good indicator of total atherosclerotic burden, it does not yield information regarding arterial function, which is closely related to cardiac function. Reduced arterial compliance increases cardiac load and myocardial oxygen consumption, and a loss of arterial compliance is a contributor to increased PP, resulting from an increase in SBP and a decrease in DBP [21,22]. This decrease in DBP reduces coronary perfusion [22,23].

Although there are many results using applanation tonometry to determine central pressure and arterial compliance, the advantage of using the DC is that it is determined non-invasively from imaging techniques (B-mode, M-mode and TDI) and, therefore, eliminates the need for additional equipment, software, training and transfer functions, which may have potential problems in certain patient populations [10]. Brachial PP has been shown to correlate well with cardiovascular risk and, as seen in the present study, its use in determining the DC appears to be robust in showing differences between risk groups (Figure 4).

Echo-tracking and M-mode ultrasound have been used in several previous studies to measure IMT and arterial distensibility, and the coefficients of variation for this technique are in the range of 10% [2426]. Previous imaging studies of arterial displacement using other techniques have shown rather high variation both between and within observers [13,27]. These imaging techniques also require specialized equipment, i.e. echo-tracking. The major limitation of the M-mode measurement is the difficulty in distinguishing the media-adventitia interface, resulting in a significantly higher measurement than by B-mode. As observed in the present study, M-mode measurement of the DC was systematically higher than B-mode and TDI, reflecting the increased diameters obtained. A low signal-to-noise ratio and angle dependency are also pitfalls of M-mode ultrasound. As the TDI technique is based on Doppler, rather than B-mode imaging, it is relatively independent of grey-scale image quality.

The use of TDI for cardiac imaging is based on extracting the tissue velocities for tissue characterization and regional function [15]. Although tissue velocity could be measured with carotid TDI, velocity may be influenced more by variable stroke volume and pressure and, hence, displacement by the TDI DC may be a more robust method than velocity. In contrast, arterial wall motion analysis for extracting tissue velocity information is semi-automated. The decrease in observer input reduces inter- and intra-observer variation, and the results of the test–retest concordance support using TDI displacement to serially follow patients at risk of, or with subclinical, cardiovascular disease.

Table 2
Inter-observer variability for the detmerination of B-mode, M-mode and TDI DCs

The results are from a subgroup of 25 patients. P values were determined by paired Student's t tests.

Method of determining DCObserver 1Observer 2rDifferenceP value
B-mode (×10−3/kPa) 25±9.8 38±10 0.39 −13±10 <0.0001 
M-mode (×10−3/kPa) 33±9.6 35±12 0.70 −2±8.8 0.29 
TDI (×10−3/kPa) 25±7.6 25±9.8 0.99 −0.07±1.1 0.79 
Method of determining DCObserver 1Observer 2rDifferenceP value
B-mode (×10−3/kPa) 25±9.8 38±10 0.39 −13±10 <0.0001 
M-mode (×10−3/kPa) 33±9.6 35±12 0.70 −2±8.8 0.29 
TDI (×10−3/kPa) 25±7.6 25±9.8 0.99 −0.07±1.1 0.79 
Table 3
Intra-observer variability for the detmerination of B-mode, M-mode and TDI DCs

The results are from a subgroup of 25 patients. P values were determined by paired Student's t tests.

Method of determining DCRead 1Read 2rDifferenceP value
B-mode (×10−3/kPa) 25±9.8 22±14.2 0.84 2.8±8.0 0.13 
M-mode (×10−3/kPa) 33±9.6 33±10.2 0.96 0.4±2.6 0.53 
TDI (×10−3/kPa) 25±7.6 26±7.7 0.99 −0.3±0.8 0.15 
Method of determining DCRead 1Read 2rDifferenceP value
B-mode (×10−3/kPa) 25±9.8 22±14.2 0.84 2.8±8.0 0.13 
M-mode (×10−3/kPa) 33±9.6 33±10.2 0.96 0.4±2.6 0.53 
TDI (×10−3/kPa) 25±7.6 26±7.7 0.99 −0.3±0.8 0.15 

The results of the present study suggest that TDI assessment is a feasible method for assessing DC, comparable with other techniques, and reproducible for the determination of between-group differences in patients at cardiovascular risk. Image quality is less important than with B-mode, because the signal–noise relationship of Doppler methods is more favourable for quantification than those obtainable using imaging methods for edge detection. Patients referred for carotid imaging for measurement of IMT could have TDI displacement incorporated as part of their examination.

Limitations

A direct comparison with echo-tracking would be desirable, but was not possible in the present study. However, the measurements were performed by experienced observers and the lack of significant differences between the B-mode DC and TDI DC suggest that the TDI DC is robust and feasible for the determination of arterial elasticity.

PWV (pulse wave velocity) is considered as the optimal parameter for assessment of arterial stiffness, but was not measured in the present study because we sought to compare indices of arterial displacement. However, PWV has been shown to be a valuable prognostic marker, and its measurement could have provided an independent measure of arterial function.

Peripheral BP was used to determine the DC. Although these waveforms can be transformed into central pressure, allowing distensibility to be derived from central displacement and central pressure, we did not do this for several reasons. First, our goal was to develop a technique derived from imaging and standard BP, and to obviate the need for tonometry, which may be adversely impacted by lack of familiarity and the need to obtain specialized equipment and training. Secondly, we sought to compare techniques for the measurement of distensibility, all of which would be changed similarly by altering the pressure measurement. Finally, in previous studies on distensibility, peripheral pressure has been used.

Conclusions

Imaging of the common carotid arteries with tissue Doppler is simple and feasible; the DC can be determined readily from the displacement values. The TDI DC also parallels the progressive worsening of arterial compliance with more established arterial disease and appears to be highly reproducible between observers.

Abbreviations

     
  • BMI

    body mass index

  •  
  • BP

    blood pressure

  •  
  • DBP

    diastolic BP

  •  
  • DC

    distensibility coefficient

  •  
  • IMT

    intima-medial thickness

  •  
  • PP

    pulse pressure

  •  
  • PWV

    pulse wave velocity

  •  
  • RF

    radio frequency

  •  
  • ROI

    region of interest

  •  
  • SBP

    systolic BP

  •  
  • TDI

    tissue Doppler imaging

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