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Endothelial cells play an important role in the autoregulation of the vascular diameter for maintaining physiological flow and shear stress. An increase in blood flow causes an increase in shear stress, which is sensed by the endothelial cells, resulting in the release of vasoactive substances. An impaired endothelial mediated vasomotive response seems reflective for several cardiovascular pathologies, such as failure of atherosclerosis and arterio-venous fistulas (AVF).

Carotid endarterectomy is the procedure of choice in patients with a recent symptomatic stenosis of 70–99%. Currently, the selection of candidates eligible for carotid endarterectomy is based on stenosis size only. However, the treatment is only beneficial for patients with unstable plaques, which comprises only 16% of the patient population [1]. Hence, identifying plaque stability at an early stage would permit timely intervention, while substantially reducing overtreatment of stable plaques. The objective of this study is to distinguish between stable and unstable carotid atherosclerotic plaques by determining the plaque geometry, the plaque composition and the mechanical properties of plaque components in three dimensions (3D). Mechanical properties from healthy vessels were assessed earlier by van den Broek et al. [2] using ultrasound (US) imaging. They obtained a dynamic dataset in 2D + t. When blood pressure and vessel wall movement are known, mechanical properties can be extracted from these data using a constitutive model. However, atherosclerotic plaques are mostly asymmetric, and present calcifications will cause unfavorable acoustic shadowing when using US. In this study, the focus is on the assessment of plaque geometry, from in vitro echo-CT data, overcoming the aforementioned problems. In an experimental set-up (Fig. 1) both healthy and endarterectomy specimens were mounted, and exposed to physiological intraluminal pressures. Echo-CT was used to image the arterial segments in 3D+t. Automated geometry assessment of the arterial segments will be demonstrated and validated using microCT (μCT).