Background-One of the features of high-risk atherosclerotic plaques is a preponderance of macrophages. Experimental studies with hyperlipidemic rabbits have shown that ultrasmall superparamagnetic particles of iron oxide (USPIOs) accumulate in plaques with a high macrophage content and that this induces magnetic resonance (MR) signal changes. The purpose of our study was to investigate whether USPIO-enhanced MRI can also be used for in vivo detection of macrophages in human plaques. Methods and Results-MRI was performed on 11 symptomatic patients scheduled for carotid endarterectomy before and 24 (nϭ11) and 72 (nϭ5) hours after administration of USPIOs (Sinerem) at a dose of 2.6 mg Fe/kg. Histological and electron microscopical analyses of the plaques showed USPIOs primarily in macrophages within the plaques in 10 of 11 patients. Histological analysis showed USPIOs in 27 of 36 (75%) of the ruptured and rupture-prone lesions and 1 of 14 (7%) of the stable lesions. Of the patients with USPIO uptake, signal changes in the post-USPIO MRI were observed by 2 observers in the vessel wall in 67 of 123 (54%) and 19 of 55 (35%) quadrants of the T2*-weighted MR images acquired after 24 and 72 hours, respectively. For those quadrants with changes, there was a significant signal decrease of 24% (95% CI, 33% to 15%) in regions of interest in the images acquired after 24 hours, whereas no significant signal change was found after 72 hours. Conclusions-Accumulation

In this work we aimed to study the possibility of using supervised classifiers to quantify the main components of carotid atherosclerotic plaque in vivo on the basis of multisequence MRI data. MRI data consisting of five MR weightings were obtained from 25 symptomatic subjects. Histological micrographs of endarterectomy specimens from the 25 carotids were used as a standard of reference for training and evaluation. The set of subjects was divided in a training set (12 subjects) and an evaluation set (13 subjects). Four different classifiers and two human MRI readers determined the percentages of calcified tissue, fibrous tissue, lipid core, and intraplaque hemorrhage on the subject level for all subjects in the evaluation set. Methods to assess the composition of atherosclerotic plaque have gained interest in the last decade because such findings may reveal the risk of heart disease and stroke (1-9). By quantifying plaque components in vivo, one can monitor the regression or progression of atherosclerosis. This would be important for the development of therapies to prevent thromboembolic events. Moreover, in vivo quantification of plaque components could be useful in making the decision to apply an existing therapy.In particular, plaques that contain lipid core and hemorrhage are considered to be at risk for thromboembolic events (1,4,5,8,9), but at present it is unclear which proportions of these high-risk components are clinically relevant (10 -15). Further studies are needed to determine the relationship between the proportion of high-risk components and thromboembolic events. Therefore, it is important to determine the accuracy and reproducibility of the methods used to quantify these components.It has been shown that multisequence MRI has the potential to detect the main plaque components both ex vivo (16,17) and in vivo (18 -23). Objective quantification of the plaque in terms of its main constituents on the basis of MR images is still an important problem. Therefore, recent studies have focused on developing (semi-)quantitative algorithms to characterize plaque (16,17,22). Several authors have shown the efficacy of ex vivo MRI in conjunction with algorithmic classifiers (16,17).Two essentially different classification methods are supervised and unsupervised classification (24). Supervised methods require training data (i.e., a set of input patterns with known classification). Given a new input pattern, a trained supervised method can predict its class.On the other hand, unsupervised classifiers simply separate the input data into a number of classes (clusters). In general, supervised classifiers are considered to be more powerful than unsupervised classifiers.In this study multisequence MRI was used for in vivo assessment of atherosclerotic plaque in the carotids of symptomatic subjects. Supervised classification algorithms were employed to quantify the main plaque components. Four plaque components were distinguished: calcified tissue, fibrous tissue, intraplaque hemorrhage, and lipid core. Results are a...

Abundant data now link composition of the vascular wall, rather than the degree of luminal narrowing, with the risk for acute ischemic syndromes in the coronary, central nervous system, and peripheral arterial beds. Over the past few years, magnetic resonance angiography has evolved as a well-established method to determine the location and severity of advanced, lumen-encroaching atherosclerotic lesions. In addition, more recent studies have shown that high spatial resolution, multisequence MRI is also a promising tool for noninvasive, serial imaging of the aortic and carotid vessel wall, which potentially can be applied in the clinical setting. Because of the limited spatial resolution of current MRI techniques, characterization of coronary vessel wall atherosclerosis, however, is not yet possible and remains the holy grail of plaque imaging. Recent technical developments in MRI technology such as dedicated surface coils, the introduction of 3.0-T high-field systems and parallel imaging, as well as developments in the field of molecular imaging such as contrast agents targeted to specific plaque constituents, are likely to lead to the necessary improvements in signal to noise ratio, imaging speed, and specificity. These improvements will ultimately lead to more widespread application of this technology in clinical practice. In the present review, the current status and future role of MRI for plaque detection and characterization are summarized.

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