Background-Thromboembolic disease secondary to complicated carotid atherosclerotic plaque is a major cause of cerebral ischemia. Clinical management relies on the detection of significant (Ͼ70%) carotid stenosis. A large proportion of patients suffer irreversible cerebral ischemia as a result of lesser degrees of stenosis. Diagnostic techniques that can identify nonstenotic high-risk plaque would therefore be beneficial. High-risk plaque is defined histologically if it contains hemorrhage/thrombus. Magnetic resonance direct thrombus imaging (MRDTI) is capable of detecting methemoglobin within intraplaque hemorrhage. We assessed this as a marker of complicated plaque and compared its accuracy with histological examination of surgical endarterectomy specimens. Methods and Results-Sixty-three patients underwent successful MRDTI and endarterectomy with histological examination. Of these, 44 were histologically defined as complicated (type VI plaque). MRDTI demonstrated 3 false-positive and 7 false-negative results, giving a sensitivity and specificity of 84%, negative predictive value of 70%, and positive predictive value of 93%. The interobserver (ϭ0.75) and intraobserver (ϭ0.9) agreement for reading MRDTI scans was good. Conclusions-MRDTI of the carotid vessels in patients with cerebral ischemia is an accurate means of identifying histologically confirmed complicated plaque. The high contrast generated by short T 1 species within the plaque allows for ease of interpretation, making this technique highly applicable in the research and clinical setting for the investigation of carotid atherosclerotic disease. Key Words: thrombus Ⅲ plaque Ⅲ carotid arteries Ⅲ imaging Ⅲ cerebral ischemia A therothrombotic carotid disease is a major cause of cerebral ischemia. Embolization from the surface of an atherosclerotic plaque to cerebral vessels can result in occlusion, which, if sufficiently prolonged, will result in cerebral infarction. More transient vascular occlusion may result in temporary ischemia, producing a neurological deficit that recovers with little or no residual brain damage. Clinically, transient ischemic attacks (TIAs) provide a warning of further cerebral ischemic events; Ϸ10% of patients who sustain a TIA, left untreated, will suffer a definitive stroke in the following year, 1 followed by a rate of 5% per annum. A warning TIA therefore offers the chance to intervene to prevent future permanent cerebral damage. The North American 2 and European 3 endarterectomy trials have both shown the positive benefit of surgery in patients with significant stenosis. The trials also indicate that lesser degrees of carotid disease are responsible for a significant number of strokes, but the risks of surgery matched or outweighed the benefits. Techniques have been sought to further define those among the group with moderate stenosis who are at high risk.In 1995, Stary et al, 4 for the American Heart Association, defined different atherosclerotic subtypes, the purpose being to pathologically identify plaque more likely to ...

Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis and surface irregularities using in vivo imaging techniques including sonography, CT and MR angiography, and digital subtraction angiography. However, histopathologic studies demonstrate considerable differences between plaques with identical degrees of stenosis and indicate that certain plaque features are associated with increased risk for ischemic events. The ability to look beyond the lumen using highly developed vessel wall imaging methods to identify plaque vulnerable to disruption has prompted an active debate as to whether a paradigm shift is needed to move away from relying on measurements of luminal stenosis for gauging the risk of ischemic injury. Further evaluation in randomized clinical trials will help to better define the exact role of plaque imaging in clinical decision-making. However, current carotid vessel wall imaging techniques can be informative. The goal of this article is to present the perspective of the ASNR Vessel Wall Imaging Study Group as it relates to the current status of arterial wall imaging in carotid artery disease.

For over a decade the term “Big data” has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, “Big data” no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as “data analytics” and “data science” have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises “Big Advances,” significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set “Big data” analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.