An in vitro system for Doppler ultrasound flow studies in the stenosed carotid artery bifurcation

Ultrasound in Med. & Biol., Vol. 28, No. 4, pp. 495–506, 2002 Copyright © 2002 World Federation for Ultrasound in Medicine & Biology Printed in the USA. All rights reserved 0301-5629/02/$–see front matter

PII: S0301-5629(02)00479-9

● Original Contribution AN IN VITRO SYSTEM FOR DOPPLER ULTRASOUND FLOW STUDIES IN THE STENOSED CAROTID ARTERY BIFURCATION TAMIE L. POEPPING,*† HRISTO N. NIKOLOV,* RICHARD N. RANKIN,‡ MARK LEE* and DAVID W. HOLDSWORTH*†‡ *The John P. Robarts Research Institute, London, ON, Canada; and Departments of †Medical Biophysics and ‡ Diagnostic Radiology and Nuclear Medicine, The University of Western Ontario, London, ON, Canada (Received 28 August 2001; in final form 9 January 2002)

Abstract—To investigate the correlation between disease severity and Doppler spectral measurements in the carotid artery bifurcation, a unique in vitro system has been developed that mimics the human vasculature with respect to both anatomy and flow perfusion. Agar-based carotid phantoms are perfused with a blood-mimicking fluid using a computer-controlled pump and realistic pulsatile flow waveform. A three-axis translational stage allows the lumen to be interrogated with a 0.6-␮L Doppler sample volume at the desired spatial intervals using a semiautomated acquisition system, to collect 10 cardiac cycles of gated quadrature data at each site. Off-line analysis, including a 1024-point FFT, produces a 4-D (i.e., time-varying 3-D) Doppler velocity data set with 1.3-cm/s velocity resolution and 12-ms temporal resolution. Using this system, in vitro flow in bifurcations with both normal and stenosed lumen geometry (from 30% to 80% stenosis by NASCET criteria) can be studied, along with the effect of factors, such as stenosis geometry (concentric vs. eccentric) and flow rate, on the observed Doppler ultrasound (US) spectra and haemodynamic patterns. (E-mail: [email protected]) © 2002 World Federation for Ultrasound in Medicine & Biology. Key Words: Carotid artery bifurcation, In vitro, Blood flow velocity, Stenosis, Duplex ultrasound, Doppler sample volume power response, Spectral analysis, Turbulence intensity, Spectral broadening index.

atic patients with severe stenosis (European Carotid Surgery Trialists’ Collaborative Group 1998; NASCET Collaborators 1991). Although angiography is historically the “gold standard” for assessing stenosis severity, Doppler ultrasound (US) has become increasingly popular as a noninvasive method to screen possible candidates for carotid endarterectomy. Doppler US can be used to obtain velocity spectra from a sample volume carefully positioned within the vessel lumen. Several parameters can be derived from the Doppler spectral data, although generally diagnosis is based simply on derived peak or mean velocities or some ratio of these velocities. Typically, the maximum velocity through the stenosis neck is used to predict the stenosis severity based on the fact that, for a constant flow rate, a tighter constriction leads to higher velocities through the stenosis, much like the jet stream through a pinched hose. Two-dimensional (2-D) flow information is often presented using colour Doppler imaging. However, in colour Doppler imaging, the spectral data are reduced to a single parameter, such as mean or mode velocity, that is colour-encoded and assigned to the

INTRODUCTION The carotid artery bifurcation, where the common carotid artery (CCA) branches into the internal (ICA) and external (ECA) carotid arteries, is a common site of atherosclerotic disease. Stenosis or narrowing of the ICA, one of the major arteries that supplies blood to the brain, has long been known to be related to the incidence of ischaemic stroke (Barnett et al. 2000; Fisher 1951). Hence, the use of the stenosis severity or degree of constriction has evolved as a surrogate measure of stroke risk. Clinical trials were, thus, planned to assess the risk of stroke and subsequent benefit from various treatments, including surgery or drug therapy, based on categorisation according to stenosis severity. Indeed large, multicentre clinical trials have demonstrated the benefit of carotid endarterectomy, a surgical procedure to remove atherosclerotic plaque within the carotid arteries, for symptom-

Address correspondence to: D. W. Holdsworth, Imaging Research Laboratories, The John P. Robarts Research Institute, P.O. Box 5015, 100 Perth Drive, London, Ontario N6A 5K8 Canada. E-mail: [email protected] 495

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corresponding pixel in a 2-D image. Hence, with colour Doppler, the majority of the spectral information is, again, ignored. Clinical diagnosis of carotid disease with Doppler US has been shown to suffer from poor specificity (Bain et al. 1998; Johnson et al. 2000; New et al. 2001; Qureshi et al. 2001) and reproducibility (Mikkonen et al. 1997). This may be due, in part, to the fact that it is underutilised to determine only the maximum peak systolic velocity as an estimate of stenosis severity. Furthermore, studies have shown that stenosis severity itself is an incomplete indicator of stroke risk. Barnett et al. (2000) showed that, for medically treated, symptomatic patients, although the risk of ipsilateral large-artery stroke increased with stenosis severity, the risk at 5 years was still only 25% in those with a severe (70% to 99%) symptomatic stenosis. In comparison, symptomatic patients with a severe contralateral, asymptomatic stenosis showed only a 10% risk of stroke in the territory of the asymptomatic artery at 5 years. In another study, Bornstein and Norris (1993) found that asymptomatic adults with ⬎75% carotid stenosis only have a 3.3% annual stroke rate. Hence, stenosis severity itself can be considered only a surrogate measure of stroke risk with relatively poor specificity. Clearly, other focal factors must play a role, and the use of additional information from Doppler US, other than peak systolic velocity in the stenosis, has been suggested to elucidate the local haemodynamics (Brown et al. 1982; Cloutier et al. 1996; Kalman et al. 1985; Krause et al. 1984; Rittgers et al. 1983). Preservation and utilisation of the complete Doppler spectral data may potentially lead to a more complete diagnosis of disease severity and stroke risk, by reflecting the complex haemodynamics of flow near a plaque and stenosis, rather than simply determining the stenosis severity. One example of a focal factor that is not completely described by stenosis severity is the plaque eccentricity. It has been shown with computational fluid dynamics and digital particle imaging (Steinman et al. 2000) that stenoses of equal severity, but different eccentricity (symmetry), exhibit very different flow patterns with respect to poststenotic recirculation zones and regions of elevated wall shear stress. Also, turbulence has been found to be associated with severe stenosis (Gach and Lowe 1998) and increased coagulation (Stein and Sabbah 1974). Although the role that such haemodynamic effects play in thromboembolic stroke is still controversial, shear and turbulence are certainly important considerations for thrombus formation and plaque rupture. Ideally, it would be useful to quantify these haemodynamic effects for diagnostic purposes using the common diagnostic tool of Doppler US. For this reason, we have developed a unique in vitro system that mimics the human vasculature with respect to both anatomy and

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flow perfusion. With this system, the entire spectral data set is preserved, allowing any number of parameters to be investigated and visualised. The system produces a complete set of 4-D Doppler velocity data along with video images, where numerous geometric, flow and sampling parameters are controlled and investigated. Using a subset of the data, we can emulate typical colour Doppler images or colour-encode more advanced parameters, such as spectral broadening index (Brown et al. 1982) or turbulence intensity (Casty and Giddens 1984; Cloutier et al. 1996; Sigel et al. 1970; Stein and Sabbah 1974). In this paper, we describe the in vitro system that has been developed to obtain time-varying 3-D Doppler US data in carotid artery bifurcation phantoms. MATERIALS AND METHODS Phantoms Realistic anthropomorphic vascular phantoms have been fabricated using idealised model geometries from Smith et al. (1996), based on a quantitative analysis of biplane, x-ray, arterial angiograms of diseased carotid bifurcations. The geometries were derived from 62 patients with ICA stenosis. These models thus, provide more realistic approximations of stenosis shape than simpler pinched-tube or Y-bifurcation models and, hence, hemodynamic effects that are more representative of human physiology. Eleven different geometries have been constructed with either a normal (disease-free) bifurcation or varying degrees of stenosis in the ICA including: 30, 50, 60, 70 and 80% stenoses, based on the NASCET criteria (Fox 1993). Also, for each stenosis severity, there are two geometric configurations or eccentricities: concentric (axisymmetric) and eccentric (asymmetric). A concentric stenosis simulates equal deposition of plaque on both walls, and an eccentric stenosis simulates preferential deposition of plaque on the outer wall. Both geometries have a circular cross-section throughout. The life-size models have an 8-mm CCA diameter, which is representative of humans 60 years old or older (Hansen et al. 1995). Figure 1 shows a range of geometries, including 70% concentrically and eccentrically stenosed models of the carotid bifurcation. Wall-less vessel agar phantoms (Rickey et al. 1995) have been fabricated with these models using a lostmaterial casting technique as detailed by Smith et al. (1999). Briefly, the models were cast from a low-melting-point (47.2°C) metal (Cerrolow 117, Cerro Metal Products, Bellafonte, PA) in aluminum moulds. The metal cores were mounted in an acrylic box with plastic connectors at the CCA inlet and the ICA and ECA outlets. The box was filled with agar gel that contains 50-␮m cellulose as scattering particles. Formaldehyde (1.4% by mass) was added for bacterial resistance. Form-

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Fig. 1. Carotid bifurcation models with varying stenosis severity and eccentricity showing the common carotid (C) branching into the internal (I) and external (E) carotid arteries. A scale marker shows the diameter of the CCA.

aldehyde also produces cross-linking of the gel molecules, which decreases the compliance and increases the melting point of the agar (Astrahan 1979; Blechinger et al. 1988; Smith et al. 1999). The gel was allowed to solidify before the low-melting-point metal was removed by submersing the phantom in a 52°C temperature-controlled water bath until the core had melted and the water was then flushed through the lumen to remove the remaining metal, leaving a smooth surface throughout the lumen. The agar phantoms exhibit an approximate US attenuation of 3.5 dB/cm at 5 MHz and speed of sound of 1540 m/s, which provide an appropriate level of attenuation over the approximately 2-cm beam path length and good US coupling. The change in lumen diameter over the cardiac cycle was determined using an M-mode image by measuring the systolic and diastolic diameters and calculating the relative difference. The resulting 6% relative distension of the agar phantoms is representative of the vessel compliance of a 60-year-old human (Hansen et al. 1995). The phantoms are capped with a polyethylene lid and o-ring seal and coupled to the transducer via the water bath with a maximum standoff from the phantom of 4 mm. To alleviate the pressure difference between the lumen and atmosphere, which can lead to fractures in the agar at the bifurcation apex with high flow rates, two holes are made approximately 4 cm downstream of the apex in the ICA and ECA. These holes allow a small amount of fluid to pass up into the space between the lid and agar until the pressure is equalised. Flow A computer-controlled pump (Holdsworth et al. 1991)(UHDC Flow System, Shelley Medical Imaging Technologies, London, ON, Canada) provides the in vitro flow. The pump can be programmed with steady or arbitrary, pulsatile flowrate waveforms with variable peak or mean flow rates. In these investigations, a real-

Fig. 2. Flowrate waveforms representing idealized or average human carotid flow, the modified pump waveform to compensate for damping effects, and the resulting flowrate waveform measured at the CCA inlet.

istic flowrate waveform, derived from spectral analysis of in vivo Doppler US velocity spectra (Holdsworth et al. 1999), is used. The programmed flowrate waveform is modified to achieve the desired waveform downstream at the inlet to the CCA. This modification is necessary to compensate for damping effects due to the transfer function of the flow system (Frayne et al. 1992). Figure 2 shows the average human carotid flowrate waveform, the modified waveform programmed into the pump, and the resulting waveform obtained downstream at the inlet to the CCA as measured with an in-line electromagnetic flowmeter (Model 322, Carolina Medical Electronics, King, NC). The waveforms have a peak-to-mean ratio of approximately 4:1 and a cycle length of 0.92 s. In this study, a peak flow rate of 20 mL/s (Re ⫽ 400 in CCA), with a corresponding mean flow rate of 5.2 mL/s (Re ⫽ 104 in CCA), was used. However, with this in vitro system, mean flow rates of up to 9 mL/s are possible. Higher flow rates are problematic because they can cause the agar to split where the CCA flow impinges on the apex of the bifurcation. Normal human mean blood flow rates have been found to average between 6.2 and 7.8 mL/s in the common carotid artery (Bogren et al. 1994; Demolis et al. 1991; Holdsworth et al. 1999; Schoning et al. 1994). A TTL (transistor-transistor logic) signal provided by the computer-controlled pump allows for data acquisition to be prospectively gated. Data collected are, hence, synchronised with respect to the cardiac cycle, allowing for ensemble averaging. Approximately once every 100 cardiac cycles, the computer-controlled pump produces a slightly erroneous waveform due to the reversal of the motor-driven piston. An additional TTL signal from the pump, indicating the reversal point of the pump piston, is monitored through software to ensure that only valid data are collected.

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Distal flow resistors, made of 2-mm i.d. IV tubing, are used to achieve a flow division between the ICA and ECA that mimics that of the human cerebral vasculature. The flow resistance from the IV tubing is greater than that from the constriction of the stenosis and, hence, the appropriate flow division is maintained independent of the stenosis severity. This mimics the in vivo situation where the dominant resistance is due to the downstream vascular bed except for very severe stenoses (Spencer and Reid 1979). An appropriate cycle-average flow division (Bogren et al. 1994; Schoning et al. 1994) of 65:35 between the ICA and ECA can be achieved by attaching approximately 1 m of tubing distal to the ICA and 2 m distal to the ECA (Smith et al. 1999). The flow division can be varied by changing the relative lengths of the exit tubing. The US blood-mimicking fluid (BMF) (Ramnarine et al. 1998) is composed (by weight) of: 83.86% distilled water, 10.06% glycerol, 3.36% dextran (D4876, Sigma Chemical Co., St. Louis, MO), and 0.9% surfactant (ICI Synperonic N, BDH Laboratory Supplies, Poole, U.K.), with 1.82% of 5 ⫾ 2 ␮m nylon particles (Orgasol 2001 UD NAT 1, Elf Atochem, Paris, France) added to provide Doppler scattering sites. The attenuation of the fluid is 0.26 dB/cm at 5 MHz, the speed of sound is 1547 m/s, and the viscosity is 4.1 ⫾ 0.1 mPa 䡠 s as described by Ramnarine et al. (1998). Ultrasound acquisition Duplex Doppler US measurements are acquired using a clinical US machine (Ultramark 8, ATL, Bothell, WA) using B-mode images for initial positioning and alignment. A mechanical probe (Access 10-PV, ATL) is mounted in a holder that has one axis of rotation and three axes of translation. The Doppler sample volume is positioned at a depth corresponding to the focal point of the selected transducer and kept fixed while the entire probe is translated. The demodulated in-phase and quadrature analog signals are available for recording, along with the video output from the B-mode and Doppler displays. Figure 3 shows the agar phantom in a water bath beneath the US probe mounted on the three-axis stage. The probe can be translated along the three axes indicated and, additionally, rotated about the y-axis to obtain multiple vector components. However, the data collected here represent a single vector axial component of the flow. Figure 4 is a schematic with the various components of the in vitro system. The entire system is controlled from a UNIX workstation (Indy, Silicon Graphics Inc., Mountainview, CA) that drives the three-axis translational stage, controls mode switching (B-mode/Doppler, freeze/refresh) on the clinical US unit, polls the ECG

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Fig. 3. Carotid bifurcation phantom mounted in a water bath with the US probe attached to the three-axis translational stage.

signal and invalid-data signal from the pump, and allows manipulation of the data acquisition parameters through a graphic user interface. Variable acquisition parameters include audio and image file formats, recording time, digitisation rate and the use of ECG-gating. The prescribed 3-D sampling sites are read from a coordinate file with the desired sampling interval throughout the vessel volume. These files are computer-generated from the 3-D model geometry. Hence, the vessel lumen can be efficiently interrogated at any spatial resolution to build a 4-D data set (i.e., 3-D, single-vector data as a function of time). The results to be shown provide a representative example of the measurements that can be derived from the data. These data were collected at sites with a 1-mm isotropic spacing in an agar phantom with a 30% concentric stenosis. The Doppler pulse axis was pointed along the y–z plane at 60° relative to the z-axis and, hence, only the axial component of the true velocity vector was derived. A 5-MHz Doppler frequency was used with a 60° Doppler angle (␪), 50-Hz wall filter, 8kHz pulse repetition frequency (PRF), and the smallest (1.5 mm) sample volume setting. At each sample site, 10 s of cardiac-gated Doppler data were collected, and B-mode and Doppler display images were collected at selected sites. The data were collected from within one half of the artery lumen, using the assumption of mirror symmetry about the x–z plane to complete the volume of interest. This approach reduces the acquisition time by nearly a factor of two. Signal processing The demodulated in-phase and quadrature analog signals from the US machine are input to the UNIX

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v⫽

Fig. 4. Schematics of (a) the in vitro Doppler US system, and (b) the setup for the Doppler-sensitivity mapping using the vibrating point source.

workstation, digitised at 44.1 kHz, and recorded for off-line analysis. Postprocessing consists of a 1024-point FFT with a 1024-point Hanning window and a 50% overlap between consecutive windows. This produces an instantaneous Doppler spectrum (power vs. frequency) every 12 ms with 43-Hz frequency resolution. Both the FFT size and window overlap are user-defined and can be varied; this, in turn, determines temporal and frequency resolution. For each Doppler spectrum, corresponding to a single time bin or FFT, the average noise level (No) is estimated from the 50 frequency bins below the Nyquist frequency, as determined by the PRF selected on the clinical US unit. Subsequently, we subtract 3 times the average noise level from all frequency bins and any bins with negative power are set to zero. Using a multiplication factor of 3 for the noise correction was found to satisfactorily remove spurious outliers of high-power noise, but this factor may be machine and parameter specific and can easily be modified. The Doppler shift frequencies, fd, are converted to velocities, v, using the Doppler equation:

fd 䡠 c , 2 䡠 f t 䡠 cos␪

(1)

where ft is the transducer frequency, c is the speed of sound in the tissue-mimicking material (1540 m/s), and ␪ is the angle between the transducer and the axis of the vessel. The resulting Doppler spectrum now represents power as a function of velocity, where the 43-Hz frequency resolution translates to a velocity resolution of 1.33 cm/s for ␪ equal to 60°. Each spectrum is then characterised with respect to its mean velocity (vmean), integrated power, peak velocity (vpeak), and spectral broadening index (SBI). This is repeated for each spectrum so that each of the above spectral parameters is traced out as a function of time over 10 s (i.e., ⬎10 full cardiac cycles). For the given temporal resolution, one cardiac cycle consists of 79 spectra or time bins. The ensemble average (one average cardiac cycle) is then determined from 10 complete cardiac cycles (9.2 s) for each of the spectral parameters (vpeak, vmean, SBI and integrated power). A number of peak-velocity tracing techniques exist (D’Alessio 1985; Marasek and Nowicki 1994; Mo et al. 1988; Routh et al. 1994; Steinman et al. 2001) and the earlier ones have been well summarised by Evans and McDicken (2000). Here, we have defined the peak velocity as the velocity within each spectrum corresponding to 90% of the total integrated power after noise subtraction, based on the percentile method (Mo et al. 1988; Steinman et al. 2001), but with the subtraction of noise as described earlier. The percentile method was used because of its ease of implementation and minimum number of user-input parameters. The spectral broadening index (Brown et al. 1982) is calculated as 1 ⫺ vmean/vpeak and is an indication of the relative spectral width. Turbulence intensity (TI) is an indicator of turbulence, which can be defined as uncorrelated temporal fluctuations in the flow field. The absolute TI is the root-mean-square (RMS) deviation in vmean (Hinze 1959) and reflects the incoherent portion of the velocity fluctuations (i.e., random fluctuations not associated with physiologic pulsatile flow). For constant flow, TI is simply the RMS deviation in vmean over all time. However, for pulsatile flow this is meaningless because the cyclic fluctuation in vmean over a single cardiac cycle would dominate over random fluctuations in vmean. For pulsatile flow, TI can be determined as a function of time in the cardiac cycle using the ensemble of 10 cardiac cycles of data. That is, TI is given by the fluctuation from cycle to cycle at each timepoint in the cardiac cycle, calculated as the RMS deviation observed for each corresponding

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timepoint in the ensemble of 10 successive cardiac-gated waveforms (Casty and Giddens 1984). The subset of spectral parameters described here represents the mean, maximum and width of the distribution of blood velocities, along with the incoherent fluctuation of the mean velocity. However, because the raw demodulated Doppler signal is recorded off-line, any number of additional spectral parameters or new signal processing techniques may be tested or incorporated at a later time. System calibration To determine the spatial resolution of the Doppler US sample volume and the accuracy of the velocity measurements, two system calibrations were performed. The Doppler sample-volume sensitivity was measured by using a vibrating point source (Hoeks et al. 1984), as shown in Fig. 4b, to map out the power response in 3-D. The vibrating point source consisted of a 280 ␮m metal bead attached to a horizontal strand of fine human hair. This strand was connected at a distal perpendicular intersection to a vertical strand that was attached to a small speaker. The speaker was driven at approximately 72 Hz with sufficient amplitude to vibrate the small bead within the water bath so that the peak Doppler velocity of the bead was well above the wall filter. Note that the amplitude of the bead oscillation was imperceptible, ensuring that it would not artificially increase the measured volume. The Doppler sample volume (gating range) was centred approximately within the beam focal point (at 2.1 cm) and the US probe was physically stepped in 0.25-mm increments through a 3 ⫻ 3 ⫻ 3 mm3 volume around the point source. Translating the entire probe, rather than electronically adjusting the sample volume, avoids the problem of overestimation of the sample volume size due to the diverging beam shape. The Doppler signal was recorded for 1 s at each sample site and the total integrated power determined from each acquisition, resulting in a 3-D map of the Doppler sample-volume power sensitivity. A velocity calibration was performed by pumping 5 to 30 mL/s steady flow through a tube of known diameter (1.26 cm). A polyethylene tube with large i.d., relative to the Doppler sample volume, was used to ensure a relatively flat flow profile (narrow velocity spectrum) through the Doppler sample volume. Doppler velocity measurements were made in a region of fully developed laminar flow after a 1 m entrance length. A total of 10 measurements of 2 s each were made at each flow rate. Assuming a parabolic flow profile in this case, for which the maximum velocity is twice the mean, the theoretical vpeak at the centre line (corresponding to the maximum velocity) can be calculated from the flow rate and cross-

Fig. 5. 3-D rendering of (a) the Doppler sample-volume power response with the orthogonally sliced half volumes shown in (b), (c) and (d). The US model (xyz) and beam (x⬘ y⬘ z⬘) coordinate systems are shown. The electronic-gating range is along the y⬘ axis and the imaging plane lies in the y⬘–z⬘ (or equivalently y–z) plane. The grey scale demarcates the half-, quarter-, and tenth-maximum power volumes, indicated by black, grey, and white, respectively. Note that each tick mark corresponds to 0.25 mm.

sectional area and compared with the measured value of vpeak. RESULTS The results presented here demonstrate the utility of the developed in vitro system, providing both technical specifications and a representative sample of data collected from a 30% concentrically stenosed carotid phantom. Doppler spatial sensitivity The spatial sensitivity of the Doppler sample volume obtained with the vibrating point source is shown in Fig. 5 as a 3-D spatial map of the total integrated spectral power response. The coordinate system of the sample volume (x⬘, y⬘, z⬘) corresponds to an approximately 20° rotation about the x (or equivalently the x⬘) axis relative to the model coordinate system (x, y, z). This angle was dictated by the physical limitations of the setup and any angle would have sufficed. However, note that for the in vitro measurements, the sample volume was oriented with a 60° rotation about the x-axis, corresponding to a 60° Doppler angle, as is typically used clinically. The 20° and 60° angles were achieved simply by mechanically steering the transducer within the scanhead.

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Fig. 6. Velocity calibration showing the measured peak velocity along the centre line of the tube vs. the theoretical centre line peak velocity assuming laminar flow. The slope and intercept of the data are 0.998 ⫾ 0.001 and 0.86 ⫾ 0.05 cm/s, respectively. The error bars denote the SD from 10 measurements of 2 s each and the solid line represents the line of identity.

Figure 5b, c and d show the sample volume sliced along the three perpendicular central planes, with the three shades of grey demarcating the boundaries corresponding to a half, quarter, and tenth of the maximum power. The full width at half maximum (FWHM) of the sample-volume power response along the x⬘ and z⬘ axes (in the transverse plane to the beam) is approximately symmetric at 1.3 mm, compared with a 0.75-mm FWHM along the beam (y⬘) axis (corresponding to the electronic gating). The effective sample volume defined by the FWHM, thus, approximates an oblate spheroid with a volume of 0.61 ␮L. Velocity accuracy and precision Figure 6 shows the measured vpeak along the centre line of the CCA vs. the calculated peak centre line velocity, assuming parabolic flow. The solid line represents the line of identity. The measured vpeak vs. calculated vpeak shows a linear relationship with slope and intercept of 0.998 ⫾ 0.001 and 0.86 ⫾ 0.05 cm/s, respectively. This indicates that, with our analysis, vpeak was systematically overestimated by 0.86 cm/s for a relative difference ranging from 10% to 2% over the range of tested peak velocities (8 to 48 cm/s). The precision, defined as the relative standard deviation (SD) in the mean velocity, was found to be approximately 4% under ideal conditions for the given analysis parameters. Temporal traces Using this system, we have the ability to record gated sequential cardiac cycles of pulsatile flow. After analysis, we are able to trace out the various spectral

Fig. 7. Doppler data from a site within the CCA. (a) A typical Doppler spectral display from the Ultramark 8; (b) peak and mean velocity traces over several cardiac cycles derived from off-line analysis; and (c) ensemble-average peak and mean velocity waveforms.

parameters as a function of the cardiac cycle. We can then calculate the ensemble average waveform from several cycles, plus the ensemble SD. Doppler spectral data obtained from a single acquisition site within the CCA are shown in Fig. 7. Figure 7a is a snapshot of a typical Doppler screen display on the clinical US unit. Figure 7b shows the results of off-line

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Fig. 8. Mean velocity profiles across the CCA at two stages in the cardiac cycle: (a) late systole and (b) postdichrotic notch.

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available software Vis5D (vers. 5.2, http://www.ssec. wisc.edu/⬃billh/vis5d.html). Colour Doppler imaging generally refers to colourencoded maps of the mean or mode velocity superimposed onto the B-mode US image. By colour-encoding the spectral information, we can emulate colour Doppler images, as shown in Fig. 9a, which is the colour-encoded map of vmean at peak systole. Similarly, we can also colour-encode other spectral parameters, such as vpeak, spectral broadening index, and turbulence intensity, as shown at peak systole in Fig. 9b, c and d, respectively. Note that the look-up table used for colour-encoding vmean was also used for vpeak. However, vpeak has a significantly higher minimum value and, hence, only uses the colours corresponding to higher velocity values. Although maps for only a single time point (i.e., peak systole) are shown, the same maps can be displayed for any 1 of the 79 time points per cardiac cycle, at each of several x–z planes within the model. DISCUSSION

analysis of the demodulated signal, demonstrating the vpeak and vmean traces over several cardiac cycles, along with the ensemble-average waveform from 10 cardiac cycles in Fig. 7c. Similarly, the TI and the ensembleaverage integrated power and SBI can be traced out over time for each acquisition site. One-dimensional spatial profiles The large quantity of information contained within each data set requires simplification for ease of visualisation. Simple subsets can be extracted that demonstrate a single parameter varying across one dimension in space for a given time point in the cardiac cycle. Figure 8 shows 1-D profiles of vmean across the central plane of the CCA at 12 mm proximal to the apex for various time points in two stages of the cardiac cycle: (a) late systole and (b) postdichrotic notch. For reference, systole and the dichrotic notch are labelled in Fig. 7c. The profiles in Fig. 8a demonstrate the plug flow associated with peak systole, followed by negative velocities along the wall after the decelerating phase of late systole. In Fig. 8b, parabolic flow can be seen to redevelop. Such profiles are useful for following the development of the flow through the cardiac cycle and for the calculation of shear stress. Multidimensional (temporal- and spatial-varying) parameter maps More complex subsets can also be extracted to study dynamic variations in the spectral parameters over 2-D or 3-D. Multidimensional visualisation of the data sets, including 4-D, can be achieved using the freely

Our goal was to develop a realistic in vitro system for controlled investigations of various Doppler spectral indices that might lead to improved asessment of in vivo carotid disease. Previous in vitro work using Doppler US to look at flow patterns near stenoses and bifurcations have included more simplistic models of either Y-bifurcations, curved tubes, or pinched-tube stenoses with constant, sinusoidal or pulsatile flow (Allard et al. 1995; Bascom et al. 1993, 1997; Fei et al. 1988; Law et al. 1991; Vattyam et al. 1992). Such studies have revealed important flow patterns (e.g., recirculation regions) and Doppler spectral parameters (e.g., spectral broadening) associated with bifurcations and stenoses. This in vitro system will allow us to extend such studies by combining realistic geometry (curved bifurcation with stenosis) with realistic carotid flow waveforms to more closely imitate the true flow patterns resulting from a CCA bifurcation with an ICA stenosis. Thus, we can systematically study the effects of different variables, such as flow rate, stenosis severity, and stenosis eccentricity, on the flow patterns and resulting Doppler spectral parameters. Automatic acquisition By developing a semiautomated acquisition system, we are able to collect data at isotropically spaced sites over a large volume of interest (approximately 3200 mm3) for the necessary number of cardiac cycles to achieve the desired resolution and precision. To collect 10 s of Doppler data at 1-mm isotropic spacing throughout a half-lumen volume of each carotid phantom re-

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Flow patterns The colour-encoded maps shown in Fig. 9 provide excellent visualisation of the flow patterns and resulting Doppler spectral indices in carotid artery bifurcation models, noting that the data represent a single vector axial component of the flow. The expected high-velocity jet can be observed in the stenosis and a downstream recirculation zone near the inner wall (Fig. 9a and b). A smaller recirculation zone can also be seen just downstream of the stenosis along the outer wall proximal to where the jet rebounds from the outer wall. The larger recirculation zone forms during systole, dissipates around the time of the dichrotic notch, and then slowly reforms and dissipates again into diastole. This region of recirculation also corresponds to a high SBI and TI, as shown in Fig. 9c and d. Although TI reflects incoherent fluctuations and SBI reflects spectral width, both are associated with regions of recirculating flow.

Fig. 9. Colour-encoded maps in the central plane of the 30% concentrically stenosed model at peak systole corresponding to: (a) mean velocity, (b) peak velocity, (c) spectral broadening index, and (d) turbulence intensity. Note that the same colour look-up table has been used for (a) and (b).

quires over 13 h of data collection for 3200 acquisition sites, resulting in 5.6 GB of data. Such data sets would not be practical with manual acquisition.

Turbulence Regions of high TI are defined to have a mean velocity that fluctuates largely from cycle to cycle (as opposed to variation within a single cycle due to pulsatile flow) and are indicative of disturbed or turbulent flow. Turbulent flow is not generally seen in normal, healthy physiology and is generally indicative of disease or abnormalities (Nerem 1992). Figure 9d demonstrates the ability of Doppler US, using as few as 10 successive cardiac cycles, to quantify the transient turbulence observed at peak systole through a mild stenosis, where velocity fluctuations of about 25 cm/s are reported. This in vitro system provides a sensitive test for turbulent flow in the carotid bifurcation due to the consistent pulsatile cycle length and flow output produced by the pump. However, the computer-controlled pump can also be programmed with output flow waveforms with known variability in either time and/or amplitude (Holdsworth et al. 1999). This will facilitate further tests of the sensitivity and specificity of the Doppler indices, such as turbulence intensity, with the introduction of realistic variations in the flow. For example, Walburn et al. (1983) have shown that the use of ensemble averaging on in vivo data produces higher estimates of turbulence intensity than the use of a digital filter, due to beat-to-beat variations. The introduction of variability in the output flow waveform may indicate that alternative analysis techniques or parameters are needed for in vivo investigations. Animations The data presented here represent a small subset of the total available. Visualisation of time-dependent 2-D and 3-D spectral maps obviously lends itself more naturally to high-frame-rate animation than to still images. Digital animations of any of the calculated parameters

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can be prepared from the acquired data, varying either the slice within the volume of interest or the time within the cardiac cycle. Limitations Particular care must be taken to filter and degas the blood-mimicking fluid, as addressed by Lubbers (1999). Air bubbles or contaminants in the BMF can cause significantly reduced precision with the system. Air bubbles, which are highly attenuating, are often found to form at high flow rates. These can be observed as either a transient increase in power as the bubble passes through the sample volume or as foam collecting along the top of the vessel lumen, which interferes with transmission of the Doppler signal. The latter is more common with constant flow in large-diameter tubes, such as were used for the velocity accuracy and precision measurements. These problems are generally rectified by operating the system in a pulsatile flow mode before each experiment for approximately 1 h. Other large contaminants can also cause transient power increases, but this is easily resolved by filtering or replacing the BMF. With the use of wall-less phantoms, the stenosis and surrounding tissue are composed of a single material and, therefore, a single elasticity or compliance, which is an oversimplification of the complex in vivo situation with plaque of varying composition and reduced arterial compliance (Hansen et al. 1995). A more compliant stenosis may cause a reduced peak velocity by distending to create a larger cross-sectional area. The agar-based, Doppler US-compatible material (Rickey et al. 1995) used here is just one of many possibilities; several alternative approaches are described in the literature (Burlew et al. 1980; Madsen 1986; Ryan and Foster 1997; Teirlinck et al. 1998). One limitation of this agar-based material containing 50-␮m scattering particles is the frequency-dependence of the acoustic attenuation, which varies with frequency to the power of 1.84, although this is less of a problem when narrowband Doppler pulses are used (Rickey et al. 1995). A potentially more serious limitation of wall-less, agar-based phantoms is the possibility of mechanical rupture, leakage or degradation (Ramnarine et al. 2001) at higher flow rates. We are currently investigating alternative fabrication techniques to overcome these limitations in the future. Significance Obviously, from the long acquisition time, this system is not intended to be used directly for in vivo acquisitions. The intent is to investigate regions and parameters of particular relevance for improved in vivo diagnosis of stroke risk. The development of multigate acquisition systems that can provide Doppler spectra

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from numerous range cells along a single Doppler sampling line (Hoeks et al. 1982; Tortoli et al. 1997) makes it feasible to consider more extensive in vivo data collection than is currently done. For example, to acquire 10 cardiac cycles at 20 acquisition sites, producing 20 timevarying 1-D profiles of each of the various parameters mentioned here, would require less than a 10-min examination. The in vitro investigation that can be completed with the system described here may allow us to predict the most important parameters to investigate and regions to interrogate within the lumen in future in vivo studies. Routine clinical acquisition and analysis of additional Doppler spectral parameters, relating to shear and turbulence, could help distinguish which patients are at greatest risk of ischaemic stroke. For example, this may help to decide the best form of therapy in symptomatic patients with moderate (50% to 69%) carotid stenosis, a group that as a whole only sees a moderate reduction in stroke risk with carotid endarterectomy (Barnett et al. 1998). SUMMARY AND FUTURE WORK In summary, we have developed a unique in vitro system that mimics the human vasculature with respect to both anatomy and flow perfusion. With this system, in vitro flow can be studied in bifurcations with both normal and stenosed lumen geometry. The system provides for control over stenosis severity (ranging from normal to 80% stenosis by NASCET criteria), stenosis geometry (concentric vs. eccentric), and flow rate. For each set of experimental variables, 4-D Doppler spectral parameter maps can be produced. Future work This system will enable rigourous investigation of the effect of numerous factors, such as stenosis severity, stenosis eccentricity, and flow rate, on the observed Doppler US spectrum and haemodynamic patterns. Future work will include a study of the flowrate dependence of various Doppler diagnostic indices and spectral parameters. Also, shear stresses have been implicated in the development of atherosclerosis, thrombogenesis and plaque rupture. Previous work using digital particle imaging and computational fluid dynamics has shown significantly different flow and shear-stress patterns for concentric vs. eccentric stenoses (Steinman et al. 2000). It may be possible to quantify regions of recirculation, disturbed flow or turbulence, and shear stress (with regard to temporal and spatial extent) in the various stenosed models. Furthermore, computational fluid dynamics modelling has difficulty estimating velocities in regions of turbulence. Hence, it is highly desirable to

In vitro carotid flow system ● T. L. POEPPING et al.

determine both the severity of stenosis and the intervals within the cardiac cycle that correspond to the onset of turbulence. Although specific spectral analysis techniques have been incorporated here, this does not preclude the use of other techniques but, rather, provides an extensive database for testing other techniques because the raw demodulated Doppler signal is recorded off-line. The peak velocity tracer described here, which uses the percentile method in addition to adaptive noise subtraction, is analogous to the hybrid method developed by Mo et al. (1988). The hybrid method determines the integrated power spectrum (IPS), including both noise and Doppler signal contributions, and an estimate of the average noise level from the pure noise region of the power spectrum. The average noise level, No, in the power spectrum corresponds to the slope of the curve in the pure noise region of the IPS. The peak (signal) frequency is found from the intersection of a line of slope No (or arbitrarily some multiple of No) and the IPS curve. For a power spectrum with an average noise level already subtracted, the slope of the pure noise region of the IPS should be nearly zero. Hence, when we compensate for the average noise level in the power spectrum by subtracting 3No across all frequency bins in the spectrum and then determine the frequency corresponding to 90% integrated power, we are essentially finding the intersection of a horizontal (slope of zero) line with the IPS curve. However, the percentile method appears to be easier to implement than the hybrid method. In the future, our combined percentile method with noise subtraction will be more rigourously tested and compared to the hybrid method and others. Future studies with this in vitro system may also incorporate less generic geometries, such as patient-specific carotid geometries derived from 3-D computed rotational angiography (Fahrig et al. 1997), to investigate the correlation of clinical pathology with haemodynamics. Also, in the future, vector Doppler measurements may be possible by rotating the probe about the y-axis to obtain multiple vector components at three mutually orthogonal angles for each acquisition site within the lumen. Acknowledgements—The authors thank Bill Holt and Shiping Zhu for their programming assistance and ATL for providing the Ultramark 8 US unit. The authors acknowledge financial support from the Heart & Stroke Foundation of Canada (Grant #NA-3632 and Research Scholarship of D. W. Holdsworth) in conjunction with the Canadian Institutes of Health Research (CIHR/HSFC Partnership Graduate Scholarship of T. L. Poepping), and the John P. Robarts Research Institute (Graduate Student Award of T. L. Poepping).

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