Arterial wall mechanical inhomogeneity detection and atherosclerotic plaque characterization using high frame rate pulse wave imaging in carotid artery disease patients in vivo.

Pulse wave imaging (PWI) is a non-invasive, ultrasound-based technique, which provides information on arterial wall stiffness by estimating the pulse wave velocity (PWV) along an imaged arterial wall segment. The aims of the present study were to: (1) utilize the PWI information to automatically and optimally divide the artery into the segments with most homogeneous properties and (2) assess the feasibility of this method to provide arterial wall mechanical characterization in normal and atherosclerotic carotid arteries in vivo. A silicone phantom consisting of a soft and stiff segment along its longitudinal axis was scanned at the stiffness transition, and the PWV in each segment was estimated through static testing. The proposed algorithm detected the stiffness interface with an average error of 0.98  ±  0.49 mm and 1.04  ±  0.27 mm in the soft-to-stiff and stiff-to-soft pulse wave transmission direction, respectively. Mean PWVs estimated in the case of the soft-to-stiff pulse wave transmission direction were 2.47 [Formula: see text] 0.04 m s-1 and 3.43 [Formula: see text] 0.08 m s-1 for the soft and stiff phantom segments, respectively, while in the case of stiff-to-soft transmission direction PWVs were 2.60 [Formula: see text] 0.18 m s-1 and 3.72 [Formula: see text] 0.08 m s-1 for the soft and stiff phantom segments, respectively, which were in good agreement with the PWVs obtained through static testing (soft segment: 2.41 m s-1, stiff segment: 3.52 m s-1). Furthermore, the carotid arteries of N  =  9 young subjects (22-32 y.o.) and N  =  9 elderly subjects (60-73 y.o.) with no prior history of carotid artery disease were scanned, in vivo, as well as the atherosclerotic carotid arteries of N  =  12 (59-85 y.o.) carotid artery disease patients. One-way ANOVA with Holm-Sidak correction showed that the number of most homogeneous segments in which the artery was divided was significantly higher in the case of carotid artery disease patients compared to young (3.25 [Formula: see text] 0.86 segments versus 1.00 [Formula: see text] 0.00 segments, p -value  <  0.0001) and elderly non-atherosclerotic subjects (3.25 [Formula: see text] 0.86 segments versus 1.44 [Formula: see text] 0.51 segments p -value  <  0.0001), indicating increased wall inhomogeneity in atherosclerotic arteries. The compliance provided by the proposed algorithm was significantly higher in non-calcified/high-lipid plaques as compared with calcified plaques (3.35 [Formula: see text] 2.45 *[Formula: see text] versus 0.22 [Formula: see text] 0.18 * [Formula: see text], p -value  <  0.01) and the compliance estimated in elderly subjects (3.35 [Formula: see text] 2.45 * [Formula: see text] versus 0.79 [Formula: see text] 0.30 * [Formula: see text], p -value  <  0.01). Moreover, lower compliance was estimated in cases where vulnerable plaque characteristics were present (i.e. necrotic lipid core, thrombus), compared to stable plaque components (calcification), as evaluated through plaque histological examination. The proposed algorithm was thus capable of evaluating arterial wall inhomogeneity and characterize wall mechanical properties, showing promise in vascular disease diagnosis and monitoring.