Original contributionClinical validation of common carotid artery wall distension assessment based on multigate Doppler processing
Introduction
Altered large artery mechanics is associated with vascular aging and atherosclerotic disease and may increase susceptibility to myocardial ischemia and heart failure (Cheng et al. 2002). Physiological indices related to arterial elastic properties, such as local distensibility, stiffness index and pulse wave velocity, have an established prognostic value in predicting clinical complications of carotid, coronary and renal atherosclerotic disease. They can be modified by lifestyle and drug therapy and represent potentially useful surrogate end-points in the management of atherosclerotic vascular disease (Benetos et al 1993, Benetos et al 2002, London and Cohn 2002).
The carotid artery provides an optimal window to obtain mechanical parameters from noninvasive ultrasound (US) measurements of the wall motion (Nagai et al 2002, Meinders and Hoeks 2004). For the estimation of carotid diameter and its changes throughout the cardiac cycle, in particular, methods based on the analysis of B-mode and M-mode grey-scale images can be adopted (Stadler et al 1997, Beux et al 2001). However, most current approaches exploit radio-frequency (RF) domain cross-correlation (Bonnefous and Pesqué 1986, De Jong et al 1990, Brands et al 1997) or 1-D (Kasai et al. 1985) or 2-D autocorrelation techniques (Loupas et al 1995, Rabben et al 2002) to measure wall velocity.
This paper describes the application of a custom integrated US system to real-time detection of both the blood velocity profile and the wall movements in common carotid arteries (CCAs). This multigate Doppler (MGD) system provides accurate estimates of the distribution of spectral Doppler components (spectral profiles) within human arteries (Tortoli et al 2002, Tortoli et al 2003). Here, the extension of its processing capability to the real-time measurement of arterial distension based on a novel tracking procedure is described. In vitro and in vivo validation of distension estimates against an established reference technique, together with data on preliminary application in the clinical field, are reported. The next section describes the experimental set-up implemented for this study, including a standard US front-end coupled to a high-speed digital board. Details are given on the processing methods used to extract the velocity information from blood and wall echoes in real-time. In vitro and in vivo experimental work is finally described and the results of distension measurements from 41 healthy subjects are reported.
Section snippets
Experimental set-up
The system hardware consists of a US front-end and a digital signal processing (DSP) board with peripheral component interconnect (PCI) interface to a host personal computer (PC).
The US front-end produces acoustic pulses at pulse repetition frequency (PRF) and demodulates the received echoes onto quadrature channels (I/Q). The experimental work described in this paper is based on the US front-end included in AU-3 equipment (Esaote SpA, Florence, Italy). The AU-3 system is used in B-mode to
Experimental work and results
The MGD system was tested through in vitro and in vivo experiments. In vitro work was carried out to evaluate accuracy and precision. In vivo experiments aimed at measuring the distribution of diameters and distension in a population of 41 healthy volunteers through a wide age range. For a smaller number of volunteers, the results have been compared with those provided by a commercial reference system, the WTS-II (WallTrack System II; Esaote Pie Medical, Maastricht, The Netherlands), equipped
Discussion and conclusions
This paper has presented an US system based on a custom DSP board, allowing processing in real-time of the signals backscattered from blood as well as those reflected from arterial walls.
The experimental measurement of carotid diameter changes throughout the cardiac cycle in a population of 41 healthy volunteers including a wide age range have given results in agreement with those reported in the literature (Stadler et al 1997, Lafleche et al 1998, Samijo et al 1998, Bussy et al 2000). The low
Acknowledgements—
This work was supported by the Italian Ministry of Education, University and Research (COFIN 2002). The authors thank Esaote SpA (Florence, Italy) for providing the AU3 system and Giacomo Bambi and Francesco Guidi for valuable contribution in the development of Labview programs.
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