Translational and Molecular Imaging Institute
Translational and Molecular Imaging Institute
Woodward M.,University of Oxford |
Woodward M.,University of Sydney |
Mani V.,Translational and Molecular Imaging Institute |
Fayad Z.A.,Translational and Molecular Imaging Institute
Journal of Clinical Endocrinology and Metabolism | Year: 2015
Context: Although obesity can predispose to the metabolic syndrome (MS), diabetes, and cardiovascular disease, not all obese subjects develop MS, hence the need for new indicators of risk for this syndrome. Advanced glycation end products (AGEs) correlate with factors involved in the MS, including inflammation and insulin resistance (IR). Because AGEs can be derived from food and are modifiable, it is important to determine whether they are a risk factor for MS. Objective: The objective of this study was to assess the association of endogenous and exogenous AGEs with MS criteria. Design: The following data were collected in a cross-sectional study of subjects with and without the MS: serum AGEs (sAGEs) and mononuclear cell AGEs, metabolites, pro- and antiinflammatory markers, body fat mass measures, including abdominal magnetic resonance imaging, and caloric and dietary AGE (dAGE) consumption. Setting: The study was conducted in the general community. Participants: Participants included 130 MS and 139 non-MS subjects of both sexes, older than 50 years. Results: sAGEs (εN-carboxymethyllysine, methylglyoxal) were markedly elevated in obese persons with more than one other MS criteria but not in obese without MS criteria. sAGEs directly correlated with markers of IR (HOMA) and inflammation (leptin, TNFα, RAGE) and inversely with innate defenses (SIRT1, AGE receptor 1 [AGER1], glyoxalase-I, adiponectin). sAGEs correlated with dAGEs but not with calories, nutrient consumption, or fat mass measures. Consumption of dAGE, but not of calories, was markedly higher in MS than in non-MS. Conclusion: High sAGEs, a modifiable risk factor for IR, may indicate risk for the MS, type 2 diabetes, and cardiovascular disease. High dietary AGE consumption and serum AGE levels may link healthy obesity to at-risk obesity. Copyright © 2015 by the Endocrine Society.
Besa C.,Translational and Molecular Imaging Institute |
Cui Y.,Translational and Molecular Imaging Institute |
Jajamovich G.,Translational and Molecular Imaging Institute |
Taouli B.,Translational and Molecular Imaging Institute
Journal of Magnetic Resonance Imaging | Year: 2016
Purpose: To assess the value of apparent diffusion coefficient (ADC) measured with diffusion-weighted imaging (DWI) and enhancement ratios (ER) measured with contrast-enhanced T1-weighted imaging (CE-T1WI) for the characterization of histopathologic tumor grade of neuroendocrine tumor liver metastases (NETLM). Materials and Methods: Twenty-two patients with pathology-proven NETLM and pretreatment 1.5 Tesla (T) and 3T MRI including DWI were included in this Institutional Review Board-approved retrospective study. ADC histogram parameters, including mean, minimum (min), skewness, and kurtosis as well as ER, were computed for all lesions. Tumor grading was based on the World Health Organization 2010 classification. Kruskal-Wallis and Mann-Whitney test were used to assess for differences in ADC and ER between different tumor grades. MRI parameters were correlated with pathologic findings using Spearman correlation test. Receiver operating characteristic analysis was performed to determine optimum thresholds for predicting tumor grade. Results: Forty-eight NETLM (mean size 3.5cm) were analyzed with the following grade distribution: G1 (n=25), G2 (n=16), and G3 (n=7). ADC-mean (×10-3 mm2/s) of G3 tumors (0.87±0.43) was significantly lower than that of G1 (1.47±0.63) and G2 (1.27±0.63; P=0.042). A weak significant negative correlation was observed between ADC and tumor grade (ADC-mean: r=-0.33, P=0.02; ADC-min: r=-0.37, P=0.01) and Ki-67 (ADC-mean: r=-0.31, P=0.03; ADC-min: r=-0.39, P=0.007). AUROC, sensitivity and specificity of ADC-mean/ADC-min/ER (measured at the early arterial phase) for differentiation of G3 versus G1-G2 were 0.80/0.76/0.67, 100%/50%/70%, and 68.4%/84.2%/66.6%, respectively. Conclusion: ADC is a promising marker for characterization of histopathologic grade of NETLM. These results should be confirmed in a prospective study. J. Magn. Reson. Imaging 2016. © 2016 Wiley Periodicals, Inc.
News Article | November 1, 2016
SHAPE, the Society for Heart Attack Prevention and Eradication (http://www.shapesociety.org), a nonprofit grassroots organization dedicated to the mission of eradicating heart attacks, today announced the agenda of its first focus group meeting on prediction of near-future heart attacks using artificial intelligence. The meeting is led by Dr. Morteza Naghavi the founder and executive director of SHAPE and features leading cardiovascular researchers from around the world.. This will be the 20th scientific meeting held by SHAPE since 2001. Detailed agenda of the meeting is shown below. The First Machine Learning Vulnerable Patient Symposium A Focus Group Meeting on Developing an Artificial Intelligence-based Forecast System A Satellite Event in Conjunction with 2016 Annual Scientific Sessions of American Heart Association This event is open to public. Participation via GoToMeeting can be requested. Dinner will be served 7:30 PM. This is the 20th Vulnerable Plaque & Vulnerable Patient Symposium held by SHAPE since 2001. Welcome: Morteza Naghavi, M.D. Founder of SHAPE and Executive Chairman of the SHAPE Task Force Opening Remarks: Valentin Fuster, M.D., Ph.D. Professor of Medicine and Physician-in-Chief, Mount Sinai Hospital and Icahn School of Medicine Jagat Narula M.D., Ph.D. Chief of Cardiology, Mount Sinai West & St. Luke’s Hospitals Associate, Dean, Arnhold Institute for Global Health at Mount Sinai Icahn School of Medicine Ioannis Kakadiaris, Ph.D. Professor of Computer Science and Biomedical Engineering, Director of Machine Learning Laboratory University of Houston Topic: What is Machine Learning and How Can It Shape the Future of Healthcare? Invited Online Presentations: Two Examples of Machine Learning Studies in CVD Risk Assessment (10 minutes each) CVD prediction using support vector machine in a large Australian cohort. Dinesh Kumar, Ph.D. and Sridhar Arjunan, Ph.D. Biosignals Lab, School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia (2) Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large clinical population Piotr Slomka, Ph.D. Chief Scientist, Artificial Intelligence in Medicine Program, Department of Imaging Cedars-Sinai Medical Center, Professor, UCLA School of Medicine, Los Angeles, CA Moderated Discussions on the Vulnerable Patient Project Machine Learning for Prediction of Near-Term CHD Events All investigators will be asked to give a very brief introduction of their study and how it can fit in Background: Imagine instead of the existing daily weather forecasts and hurricane alerts we were told the probability of a storm within the next 10 years! This is how heart attacks are predicted today. We teach our physicians to calculate the 10-year probability of a heart attack and sudden cardiac death based on their patients’ risk factors. Long term predictions do not trigger immediate preventive actions. Although some people develop warning symptoms, half of men and two-thirds of women who die suddenly of coronary heart disease (CHD) have no previous symptoms. Imagine if we could alert people months, weeks, or even days before a heart attack and trigger immediate preventive actions. The Idea: Use machine learning to create new algorithms to detect who will experience a CHD event within a year (The Vulnerable Patient). Algorithms will be based on banked biospecimen and information collected days up to 12 months prior to the event. We will utilize existing cohorts such as MESA, Heinz Nixdorf Recall Study, Framingham Heart Study, BioImage Study and the Dallas Heart Study. External validation to test for discrimination and calibration will be conducted using other longitudinal observational studies that provide adjudicated cardiovascular event information such as the MiHeart, JHS, DANRISK and ROBINSCA. Additionally, we will use machine learning to characterize individuals who, despite high conventional risk, have lived over 80 years with no CHD events (The Invulnerable ). We expect to discover new targets for drug and possibly vaccine development. We will make the algorithms available as an open source tool to collect additional data over time and increase its predictive value. Organizers: SHAPE as the originating and organizing center for the entire project, recruiting new studies and biobanks, conducting workshops with researchers from each study, fundraising, creating an open source platform community for future enhancement and collaborations. Stanford as the coordinating center for collecting data and samples, and basic science labs. Mount Sinai as the data review and publication center. Machine Learning Lab to be decided, either Google, Apple, IBM, Facebook, Amazon or wherever we find a strong industry partner or sponsor. Director, Cardiac Computed Tomography, Associate Professor of Medicine, Johns Hopkins University Division of Cardiology, The Johns Hopkins Hospital Imagine the new machine learning Vulnerable Patient detection algorithm (heart attack forecaster) is created and validated. If studies confirm the algorithm is able to detect the Vulnerable Patient with 50% or more certainty. In other words, 1 out of 2 patients classified as Vulnerable Patient goes to have an ASCVD event in the following 12 months. Now the questions are: A) What preventive actions would you take if your asymptomatic patient tested positive as a Vulnerable Patient? B) What preventive actions would you take if the patient was you?! (This question is meant to circumvent regulatory and financial limitations that may apply to your patients but may not hold you back). Moderators will invite comments from all participants in the meeting. Invited Key Opinion Leaders (Alphabetic Order) Arthur Agatston, M.D. Founder of South Beach Diet, Director of Wellness at Baptist Hospital and Professor of Medicine at University of Miami, FL Daniel Berman, M.D. Professor of Medicine at UCLA, Director of Cardiac Imaging and Nuclear Cardiology at Cedars-Sinai, Los Angeles, CA Michael Blaha, M.D., M.P.H., Director of Clinical Research, Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University, Baltimore, MD Mathew Budoff, M.D. Professor of Medicine and Director of Preventive Cardiology, UCLA Harbor, Los Angeles, CA Adolfo Correa, M.D., Ph.D. Chief Science Officer, Jackson Heart Study, Professor of Medicine and Pediatrics, University of Mississippi, Jackson, MS Rahul Deo, M.D., Ph.D. Assistant Professor of Medicine, Division of Cardiology, University of California, San Francisco, CA Raimund Erbel, M.D. Professor of Medicine, Chief of Cardiology and Director of West German Heart Centre, University Essen, Germany Sergio Fazio, M.D., Ph.D. Chair of Preventive Cardiology and Professor of Medicine, Oregon Health and Science University, Portland, OR Zahi Fayad, M.D. Professor of Radiology and Medicine (Cardiology), Director of the Translational and Molecular Imaging Institute, Mount Sinai Hospital, New York, NY Philip Greenland, M.D., Professor of Cardiology, Director, Institute for Public Health and Medicine, Center for Population Health Sciences, Chicago, IL Robert Harrington, M.D. Chair of the Department of Medicine, Professor of Medicine, Stanford University School of Medicine, Stanford, CA Harvey Hecht, M.D., Director of Cardiac CT Imaging Laboratory, Mount Sinai School of Medicine, New York, NY Karl-Heinz Jöckel, Ph.D. Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Germany Ioannis Kakadiaris, Ph.D. Professor of Computer Science and Biomedical Engineering, University of Houston, Houston, TX Stanley Kleis, Ph.D. Professor of Mechanical Engineering and Biomedical Engineering, University of Houston, Houston, TX Tatiana Kuznetsova, M.D. Professor and Director, Hypertension and Cardiovascular Epidemiology, University of Leuven, Leuven, Belgium Daniel Levy, M.D. Director of Framingham Heart Study, and Intramural Investigator, National Institute of Health, Bethesda, MD Roxana Mehran, M.D. Professor of Medicine and Director of Interventional Clinical Trials, Mount Sinai School of Medicine, New York, NY Ralph Metcalfe, Ph.D. Professor of Mechanical and Biomedical Engineering, University of Houston, Houston, TX Susanne Moebus, Ph.D., M.P.H. Biologist & Epidemiologist, Head of the Centre for Urban Epidemiology, University Essen, Germany Morteza Naghavi, M.D. Founder and Executive Chairman of the SHAPE Task Force, President of MEDITEX, Houston, TX Tasneem Z. Naqvi, M.D. Professor of Medicine and Director of Echocardiography, College of Medicine, May Clinic, Scottsdale, AZ Jagat Narula, M.D., Ph.D. Associate Dean for Global Affairs, Professor of Medicine (Cardiology), Mount Sinai Hospital and School of Medicine, New York, NY Ulla Roggenbuck, Ph.D. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany Henrik Sillesen, M.D. Professor and Head of Dept. of Vascular Surgery, Rigs Hospitalet, University of Copenhagen, Copenhagen, Denmark Robert Superko, M.D. Professor of Medicine and President at Cholesterol, Genetics, and Heart Disease Institute, Carmel, CA Pierre-Jean Touboul, M.D. Professor of Neurology, Department of Neurology and Stroke Center, AP-HP Bichat University Hospital, Neurology and Stroke Center, Paris, France Nathan Wong, M.P.H., Ph.D. Professor of Epidemiology and Director, Heart Disease Prevention Program, University of California, Irvine, CA Symposium Registration http://shapesociety.org/the-first-machine-learning-heart-attack-forecast-symposium/ About SHAPE The Society for Heart Attack Prevention and Eradication (SHAPE) is a non-profit organization that promotes education and research related to prevention, detection, and treatment of heart attacks. SHAPE is committed to raising public awareness about revolutionary discoveries that are opening exciting avenues that can lead to the eradication of heart attacks. SHAPE's mission is to eradicate heart attacks in the 21st century. SHAPE has recently embarked on “Machine Learning Heart Attack Forecast System (Vulnerable Patient Project)” Project which is a collaborative effort between world’s leading cardiovascular researchers to develop a new Heart Attack Forecast System empowered by artificial intelligence. Additional information on this innovative project will be announced soon. To learn more about SHAPE visit http://www.shapesociety.org. Contact information: 1-877-SHAPE11 and info(at)shapesociety(dot)org. Learn more about the Vulnerable Patient http://shapesociety.org/the-first-machine-learning-heart-attack-forecast-symposium About SHAPE Task Force The SHAPE Task Force, an international group of leading cardiovascular physicians and researchers, has created the SHAPE Guidelines, which educates physicians on how to identify asymptomatic atherosclerosis (hidden plaques) and implement proper therapies to prevent a future heart attack. According to the SHAPE Guidelines, men 45-75 and women 55-75 need to be tested for hidden plaques in coronary or carotid arteries. Individuals with high risk atherosclerosis (high plaque score) should be treated even if their cholesterol level is within statistical “normal range.” If they have plaques, the so-called normal is not normal for them. The higher the amount of plaque burden in the arteries the higher the risk and the more vulnerable to heart attack. SHAPE Guideline aims to identify the asymptomatic “Vulnerable Patient” and offer them intensive preventive therapy to prevent a future heart attack. Knowing one's plaque score can be a matter of life and death. The SHAPE Task Force includes the following: Click below to learn about SHAPE Centers of Excellence http://shapesociety.org/centers-of-excellence/ Drs Naghavi, PK Shah, Daniel Berman, and Mathew Budoff members of the SHAPE Task Force explain how hospitals and community clinics can become a SHAPE Center of Excellence and establish themselves a leader in preventive health.
PubMed | University of Michigan, University of Texas at Austin, University of Maryland Baltimore County, Oregon Health And Science University and 4 more.
Type: Journal Article | Journal: Tomography : a journal for imaging research | Year: 2017
Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, -35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.
Skajaa T.,Translational and Molecular Imaging Institute |
Skajaa T.,Aarhus University Hospital |
Zhao Y.,University Utrecht |
Van Den Heuvel D.J.,University Utrecht |
And 11 more authors.
Nano Letters | Year: 2010
The study of lipoproteins, natural nanoparticles comprised of lipids and apolipoproteins that transport fats throughout the body, is of key importance to better understand, treat, and prevent cardiovascular disease. In the current study, we have developed a lipoprotein-based nanoparticle that consists of a quantum dot (QD) core and Cy5.5 labeled lipidic coating. The methodology allows judicious tuning of the QD/Cy5.5 ratio, which enabled us to optimize Förster resonance energy transfer (FRET) between the QD core and the Cy5.5-labeled coating. This phenomenon allowed us to study lipoprotein- lipoprotein interactions, lipid exchange dynamics, and the influence of apolipoproteins on these processes. Moreover, we were able to study HDL-cell interactions and exploit FRET to visualize HDL association with live macrophage cells. © 2010 American Chemical Society.
Hayashi K.,Translational and Molecular Imaging Institute |
Mani V.,Translational and Molecular Imaging Institute |
Nemade A.,Translational and Molecular Imaging Institute |
Aguiar S.,Translational and Molecular Imaging Institute |
And 4 more authors.
Journal of Cardiovascular Magnetic Resonance | Year: 2010
Background. Atherosclerosis is a progressive disease that causes vascular remodeling that can be positive or negative. The evolution of arterial wall thickening and changes in lumen size under current "standard of care" in different arterial beds is unclear. The purpose of this study was to examine arterial remodeling and progression/regression of atherosclerosis in aorta and carotid arteries of individuals at risk for atherosclerosis normalized over a 1-year period. Methods. In this study, 28 patients underwent at least 2 black-blood in vivo cardiovascular magnetic resonance (CMR) scans of aorta and carotids over a one-year period (Mean 17.8 7.5 months). Clinical risk profiles for atherosclerosis and medications were documented and patients were followed by their referring physicians under current "standard of care" guidelines. Carotid and aortic wall lumen areas were matched across the time-points from cross-sectional images. Results. The wall area increased by 8.67%, 10.64%, and 13.24% per year (carotid artery, thoracic aorta and abdominal aorta respectively, p < 0.001). The lumen area of the abdominal aorta increased by 4.97% per year (p = 0.002), but the carotid artery and thoracic aorta lumen areas did not change significantly. The use of statin therapy did not change the rate of increase of wall area of carotid artery, thoracic and abdominal aorta, but decreased the rate of change of lumen area of carotid artery (-3.08 11.34 vs. 0.19 12.91 p < 0.05). Conclusions. Results of this study of multiple vascular beds indicated that different vascular locations exhibited varying progression of atherosclerosis and remodeling as monitored by CMR. © 2010Hayashi et al; licensee BioMed Central Ltd.
PubMed | Mount Sinai School of Medicine, Daiichi Sankyo, Translational and Molecular Imaging Institute and Harvard University
Type: | Journal: Journal of visualized experiments : JoVE | Year: 2015
We evaluated a magnetic resonance venography (MRV) approach with gadofosveset to quantify total thrombus volume changes as the principal criterion for treatment efficacyin a multicenter randomized study comparing edoxaban monotherapy with a heparin/warfarin regimen for acute, symptomatic lower extremities deep vein thrombosis (DVT) treatment. We also used a direct thrombus imaging approach (DTHI, without the use of a contrast agent) to quantify fresh thrombus. We then sought to evaluate the reproducibility of the analysis methodology and applicability of using 3D magnetic resonance venography and direct thrombus imaging for the quantification of DVT in a multicenter trial setting. From 10 randomly selected subjects participating in the edoxaban Thrombus Reduction Imaging Study (eTRIS), total thrombus volume in the entire lower extremity deep venous system was quantified bilaterally. Subjects were imaged using 3D-T1W gradient echo sequences before (direct thrombus imaging, DTHI) and 5 min after injection of 0.03 mmol/kg of gadofosveset trisodium (magnetic resonance venography, MRV). The margins of the DVT on corresponding axial, curved multi-planar reformatted images were manually delineated by two observers to obtain volumetric measurements of the venous thrombi. MRV was used to compute total DVT volume, whereas DTHI was used to compute volume of fresh thrombus. Intra-class correlation (ICC) and Bland Altman analysis were performed to compare inter and intra-observer variability of the analysis. The ICC for inter and intra-observer variability was excellent (0.99 and 0.98, p <0.001, respectively) with no bias on Bland-Altman analysis for MRV images. For DTHI images, the results were slightly lower (ICC = 0.88 and 0.95 respectively, p <0.001), with bias for inter-observer results on Bland-Altman plots. This study showed feasibility of thrombus volume estimation in DVT using MRV with gadofosveset trisodium, with good intra- and inter-observer reproducibility in a multicenter setting.
Pengas G.,University of Cambridge |
Pengas G.,University of Portsmouth |
Williams G.B.,University of Cambridge |
Acosta-Cabronero J.,University of Cambridge |
And 8 more authors.
Frontiers in Aging Neuroscience | Year: 2012
The network activated during normal route learning shares considerable homology with the network of degeneration in the earliest symptomatic stages of Alzheimer's disease (AD). This inspired the virtual route learning test (VRLT) in which patients learn routes in a virtual reality environment. This study investigated the neural basis of VRLT performance in AD to test whether impairment was underpinned by a network or by the widely held explanation of hippocampal degeneration. VRLT score in a mild AD cohort was regressed against gray matter (GM) density and diffusion tensor metrics of white matter (WM) (n = 30), and, cerebral glucose metabolism (n = 26), using a mass univariate approach. GM density and cerebral metabolism were then submitted to a multivariate analysis [support vector regression (SVR)] to examine whether there was a network associated with task performance. Univariate analyses of GM density, metabolism and WM axial diffusion converged on the vicinity of the retrosplenial/posterior cingulate cortex, isthmus and, possibly, hippocampal tail. The multivariate analysis revealed a significant, right hemisphere-predominant, network level correlation with cerebral metabolism; this comprised areas common to both activation in normal route learning and early degeneration in AD (retrosplenial and lateral parietal cortices). It also identified right medio-dorsal thalamus (part of the limbic-diencephalic hypometabolic network of early AD) and right caudate nucleus (activated during normal route learning). These results offer strong evidence that topographical memory impairment in AD relates to damage across a network, in turn offering complimentary lesion evidence to previous studies in healthy volunteers for the neural basis of topographical memory. The results also emphasize that structures beyond the mesial temporal lobe (MTL) contribute to memory impairment in AD-it is too simplistic to view memory impairment in AD as a synonym for hippocampal degeneration.
Owen D.R.J.,Imperial College London |
Owen D.R.J.,Glaxosmithkline |
Lindsay A.C.,University of Oxford |
Choudhury R.P.,University of Oxford |
Fayad Z.A.,Translational and Molecular Imaging Institute
Annual Review of Medicine | Year: 2011
It is now well recognized that the atherosclerotic plaques responsible for thrombus formation are not necessarily those that impinge most on the lumen of the vessel. Nevertheless, clinical investigations for atherosclerosis still focus on quantifying the degree of stenosis caused by plaques. Many of the features associated with a high-risk plaque, including a thin fibrous cap, large necrotic core, macrophage infiltration, neovascularization, and intraplaque hemorrhage, can now be probed by novel imaging techniques. Each technique has its own strengths and drawbacks. In this article, we review the various imaging modalities used for the evaluation and quantification of atherosclerosis. © 2011 by Annual Reviews. All rights reserved.
Garcia-Garcia H.M.,Erasmus Medical Center |
Jang I.-K.,Harvard University |
Serruys P.W.,Erasmus Medical Center |
Kovacic J.C.,Zena And Michael ener Cardiovascular Institute And Cardiovascular Research Center |
And 3 more authors.
Circulation Research | Year: 2014
Culprit lesions of patients, who have had an acute coronary syndrome commonly, are ruptured coronary plaques with superimposed thrombus. The precursor of such lesions is an inflamed thin-capped fibroatheroma. These plaques can be imaged by means of invasive techniques, such as intravascular ultrasound (and derived techniques), optical coherence tomography, and near-infrared spectroscopy. Often these patients exhibit similar (multiple) plaques beyond the culprit lesion. These remote plaques can be assessed noninvasively by computed tomographic angiography and MRI and also using invasive imaging. The detection of these remote plaques is not only feasible but also in natural history studies have been associated with clinical coronary events. Different systemic pharmacological treatments have been studied (mostly statins) with modest success and, therefore, newer approaches are being tested. Local treatment for such lesions is in its infancy and larger, prospective, and randomized trials are needed. This review will describe the pathological and imaging findings in culprit lesions of patients with acute coronary syndrome and the assessment of remote plaques. In addition, the pharmacological and local treatment options will be reviewed. © 2014 American Heart Association, Inc.