Atherosclerosis Imaging and the Canadian Atherosclerosis Imaging Network
ABSTRACT Atherosclerosis exacts a large toll on society in the form of cardiovascular morbidity, mortality, and resource use and is exacerbated by the epidemics of obesity and diabetes. Consequently, there is a critical need for more-effective methods of diagnosis, treatment, and prevention of the complications of atherosclerosis. Careful and well-conducted large population studies are needed in order to truly understand the natural history of the disease, its imaging biomarkers, and their links to patient outcomes. The Canadian Atherosclerosis Imaging Network (CAIN) is a unique research network funded by the Canadian Institutes of Health Research and the Canada Foundation for Innovation and designed to address these needs and to enable large population-based imaging studies. The central objective of CAIN is to move innovations in imaging toward their broad application in clinical research and clinical practice for the improved evaluation of cardiac and neurologic vascular disease. CAIN is established as an international resource for studying the natural history, progression, and regression of atherosclerosis, as well as novel therapeutic interventions aimed at atherosclerosis. The network represents Canada's leading atherosclerosis imaging experts, embodying both basic imaging science and clinical imaging research. The network is improving methods of detection and treatment of atherosclerosis and, through a better understanding of the underlying disease itself, improving strategies for disease prevention. The benefits are expected to appear in the next 2 to 3 years. CAIN will drive innovation in imaging technology within the field of cardiology and neurology and improve health outcomes in Canada and worldwide.
SourceAvailable from: Matthias G. FriedrichThe Canadian journal of cardiology 03/2013; 29(3):257-9. DOI:10.1016/j.cjca.2013.01.011 · 3.12 Impact Factor
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ABSTRACT: Cardiac PET has evolved over the past 30 years to gain wider acceptance as a valuable modality for a variety of cardiac conditions. Wider availability of scanners as well as changes in reimbursement policies in more recent years has further increased its use. Moreover, with the emergence of novel radionuclides as well as further advances in scanner technology, the use of cardiac PET can be expected to increase further in both clinical practice and the research arena. PET has demonstrated superior diagnostic accuracy for the diagnosis of coronary artery disease in comparison with single-photon emission tomography while it provides robust prognostic value. The addition of absolute flow quantification increases sensitivity for 3-vessel disease as well as providing incremental functional and prognostic information. Metabolic imaging using (18)F-fluorodeoxyglucose can be used to guide revascularization in the setting of heart failure and also to detect active inflammation in conditions such as cardiac sarcoidosis and within atherosclerotic plaque, improving our understanding of the processes that underlie these conditions. However, although the pace of new developments is rapid, there remains a gap in evidence for many of these advances and further studies are required.Seminars in nuclear medicine 11/2013; 43(6):434-448. DOI:10.1053/j.semnuclmed.2013.06.001 · 3.96 Impact Factor
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ABSTRACT: Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with [Formula: see text]CT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9[Formula: see text]1.0% for calcification, 12.7[Formula: see text]7.6% for fibrous and 12.1[Formula: see text]8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.PLoS ONE 04/2014; 9(4):e94840. DOI:10.1371/journal.pone.0094840 · 3.53 Impact Factor