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Atherosclerotic disease of the carotid artery is common in the elderly population, and is a major cause of cerebral ischemia [1], [2]. The underlying mechanism is the rupture of atherosclerotic plaque with subsequent embolisation of thrombus and/or plaque material in the cerebral circulation. Clinical manifestations and fatal outcomes are most often associated with plaques of American Heart Association (AHA) type IV, V and VI [3]. On MRI these are characterized by presence of a lipid or necrotic core (LRNC) and possibly calcifications (type IV–V) or a possible surface defect, hemorrhage or thrombus (type VI) [4]. These characteristics as found in carotid histology have been related to recent symptoms [5], [6], and measurements of tissue components from MRI have been related with future events [7], [8]. Prevention of (recurrent) cerebral ischemia is the goal of pharmacological or surgical treatment. Currently the decision for surgical treatment such as carotid endarterectomy or carotid artery stenting is based on the degree of stenosis, but incorporating non-invasive measures of plaque composition is expected to improve the selection of patients that will benefit from surgical intervention [9]–[11].
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+Non-invasive identification of different plaque components is possible both with magnetic resonance imaging (MRI) [12]–[14] and CT-angiography (CTA) [15], [16]. Manual component segmentation and quantification in MRI is time-consuming and subject to inter- and intraobserver variability [13], [17]. Automated segmentation methods that are accurate and robust are therefore essential to perform large scale studies that can determine the clinical relevance of plaque composition, and to be able to incorporate these measures into daily clinical practice if this is deemed advantageous. In this paper we perform automatic segmentation of plaque components using a combination of MRI and CTA images and evaluate the advantage of combining those imaging modalities.
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This text discusses the common occurrence of atherosclerotic carotid artery disease in the elderly, causing cerebral ischemia due to plaque rupture and embolization. Plaque types IV-VI, characterized by MRI features like lipid cores, calcifications, and surface defects, are linked to symptoms. Surgery decisions based on stenosis severity can benefit from non-invasive plaque composition measures via MRI and CT-angiography. Automated segmentation of components in these images is crucial for accurate and practical assessment, with the study focusing on combining MRI and CTA for improved evaluation.
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