Multi-contrast Magnetic Resonance Research Articles

Fast multi-contrast MRI reconstruction

Multi-contrast magnetic resonance imaging (MRI) is a useful technique to aid clinical diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data. The joint reconstruction problem is formulated as minimizing a linear combination of three terms, corresponding to a least squares data fitting, joint total variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this problem, we decompose it into joint TV regularization and group sparsity subproblems, respectively. Finally, the reconstructed image is obtained from the weighted average of solutions from the two subproblems, in an iterative framework. Experiments demonstrate the efficiency and effectiveness of the proposed method compared to existing multi-contrast MRI methods.

FOLFIRI and sunitinib as first-line treatment in metastatic colorectal cancer patients with liver metastases – a CESAR phase II study including pharmacokinetic, biomarker, and imaging data

The aim of this study was the evaluation of pharmacokinetic parameters, biomarkers, clinical outcome, and imaging parameters in metastatic colorectal cancer (mCRC) patients treated with FOLFIRI plus sunitinib. mCRC patients with liver metastases were treated with FOLFIRI and sunitinib as 1st line therapy. At protocol-defined time points, multicontrast magnetic resonance imaging (MRI)measurements, computed tomography (CT) scans, pharmacokinetics (PK), and biomarker analyses were performed during the first and second treatment cycle. Thereafter, patients were treated until tumor progression, investigator’s decision due to toxicity, or patient withdrawal. 28 patients were screened, 26 were included, and 23 received at least one study medication. Full safety analysis was performed in 23 patients. Full PK and biomarker analyses were performed in 21 patients. Strong responses in tumor size reduction forced a change from the original imaging timing scheme. This unforeseen change in the timing scheme resulted in subgroups too small for meaningful statistical analysis of most imaging parameters. Thus, only a descriptive analysis of the MRI data was possible. In 21/22 patients, MRI showeda decrease of the liver metastases. Best response was partial remission (PR) in 8/17 patients. Plasma concentrations of sVEGFR-2 and sVEGFR-3 decreased in all patients. The majority of the patients developed some kind of toxicity not always deducible to FOLFIRI or sunitinib. Due to the observed side effect profile, FOLFIRI plus sunitinib 37.5 mg per day cannot be recommended for previously untreated mCRC.

Micro-Structural Brain Alterations in Aviremic HIV+ Patients with Minor Neurocognitive Disorders: A Multi-Contrast Study at High Field

ObjectiveMild neurocognitive disorders (MND) affect a subset of HIV+ patients under effective combination antiretroviral therapy (cART). In this study, we used an innovative multi-contrast magnetic resonance imaging (MRI) approach at high-field to assess the presence of micro-structural brain alterations in MND+ patients.MethodsWe enrolled 17 MND+ and 19 MND− patients with undetectable HIV-1 RNA and 19 healthy controls (HC). MRI acquisitions at 3T included: MP2RAGE for T1 relaxation times, Magnetization Transfer (MT), T2* and Susceptibility Weighted Imaging (SWI) to probe micro-structural integrity and iron deposition in the brain. Statistical analysis used permutation-based tests and correction for family-wise error rate. Multiple regression analysis was performed between MRI data and (i) neuropsychological results (ii) HIV infection characteristics. A linear discriminant analysis (LDA) based on MRI data was performed between MND+ and MND− patients and cross-validated with a leave-one-out test.ResultsOur data revealed loss of structural integrity and micro-oedema in MND+ compared to HC in the global white and cortical gray matter, as well as in the thalamus and basal ganglia. Multiple regression analysis showed a significant influence of sub-cortical nuclei alterations on the executive index of MND+ patients (p = 0.04 he and R2 = 95.2). The LDA distinguished MND+ and MND− patients with a classification quality of 73% after cross-validation.ConclusionOur study shows micro-structural brain tissue alterations in MND+ patients under effective therapy and suggests that multi-contrast MRI at high field is a powerful approach to discriminate between HIV+ patients on cART with and without mild neurocognitive deficits.

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies

Accurate segmentation of knee cartilage is required to obtain quantitative cartilage measurements, which is crucial for the assessment of knee pathology caused by musculoskeletal diseases or sudden injuries. This paper presents an automatic knee cartilage segmentation technique which exploits a rich set of image features from multi-contrast magnetic resonance (MR) images and the spatial dependencies between neighbouring voxels. The image features and the spatial dependencies are modelled into a support vector machine (SVM)-based association potential and a discriminative random field (DRF)-based interaction potential. Subsequently, both potentials are incorporated into an inference graphical model such that the knee cartilage segmentation is cast into an optimal labelling problem which can be efficiently solved by loopy belief propagation. The effectiveness of the proposed technique is validated on a database of multi-contrast MR images. The experimental results show that using diverse forms of image and anatomical structure information as the features are helpful in improving the segmentation, and the joint SVM-DRF model is superior to the classification models based solely on DRF or SVM in terms of accuracy when the same features are used. The developed segmentation technique achieves good performance compared with gold standard segmentations and obtained higher average DSC values than the state-of-the-art automatic cartilage segmentation studies.

Noninvasive assessment of osteoarthritis severity in human explants by multicontrast MRI

Medical imaging has the potential to noninvasively diagnose early disease onset and monitor the success of repair therapies. Unfortunately, few reliable imaging biomarkers exist to detect cartilage diseases before advanced degeneration in the tissue. In this study, we quantified the ability to detect osteoarthritis (OA) severity in human cartilage explants using a multicontrast magnetic resonance imaging (MRI) approach, inclusive of novel displacements under applied loading by MRI, relaxivity measures, and standard MRI. Displacements under applied loading by MRI measures, which characterized the spatial micromechanical environment by 2D finite and Von Mises strains, were strong predictors of histologically assessed OA severity, both before and after controlling for factors, e.g., patient, joint region, and morphology. Relaxivity measures, sensitive to local macromolecular weight and composition, including T1ρ, but not T1 or T2, were predictors of OA severity. A combined multicontrast approach that exploited spatial variations in tissue biomechanics and extracellular matrix structure yielded the strongest relationships to OA severity. Our results indicate that combining multiple MRI-based biomarkers has high potential for the noninvasive measurement of OA severity and the evaluation of potential therapeutic agents used in the treatment of early OA in animal and human trials.

The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images

Segmenting articular cartilage and meniscus from magnetic resonance (MR) images is an essential task for the assessment of knee pathology. Most of the previous classification-based works for cartilage and meniscus segmentation only rely on independent labellings by a classifier, but do not consider the spatial context interaction. The labels of most image voxels are actually dependent upon their neighbours. In this study, we present an automatic knee segmentation system working on multi-contrast MR images where a novel classification model unifying an extreme learning machine (ELM)-based association potential and a discriminative random field (DRF)-based interaction potential is proposed. The DRF model introduces spatial dependencies between neighbouring voxels to the independent ELM classification. We exploit a rich set of features From multi-contrast MR images to train the proposed classification model and perform the loopy belief propagation for the inference. The proposed model is evaluated on multi-contrast MR datasets acquired from 11 subjects with results outperforming the independent classifiers in terms of segmentation accuracy of both cartilages and menisci.

Characterization of Carotid Plaques on 3-Dimensional Ultrasound Imaging by Registration With Multicontrast Magnetic Resonance Imaging

The ability of magnetic resonance imaging (MRI) in carotid plaque component identification has been well established. However, compared to the costly nature of MRI, 3-dimensional (3D) ultrasound imaging is a more cost-effective assessment tool. Thus, an attractive alternative for carotid disease monitoring would be to establish a strategy in which 3D ultrasound imaging is used as a screening tool that precedes MRI. To develop and validate such a protocol, registration between ultrasound and MR images is required. This article introduces a surface-based algorithm for efficient ultrasound imaging-MRI registration. A surface-based 3D iterative closest point registration method was developed to align surfaces reconstructed from outer wall boundaries segmented from 3D ultrasound and MR images. The 3D ultrasound image was transformed according to the registration result and resliced to match corresponding 2-dimensional transverse MR images. Although rigid iterative closest point registration was used, the cross-sectional ultrasound images produced by the reslicing procedure can be moved relative to the MR images by an expert observer using in-house software, making nonrigid registration possible. We evaluated the registration accuracy associated with the algorithm using a vascular phantom as well as in vivo ultrasound and MR images. Our registration method was shown to have an average error of 0.3 mm in the phantom study and less than 1 mm in the in vivo study. Our findings in terms of the average intensity of each component are consistent with histologically validated results described in previous ultrasound characterization studies. We have developed a surface-based algorithm capable of registering ultrasound and MR images with high accuracy. This registration tool will potentially play an important role in a cost-effective screening protocol in which ultrasound is used to identify patients with a suspicion of vulnerable plaques, who are then further studied with MRI.

Carotid plaque high-resolution MRI at 3 T: evaluation of a new imaging score for symptomatic plaque assessment

PurposeTo assess the sensitivity and specificity of intra-plaque hemorrhage (IPH), large lipid-rich necrotic core (LR-NC) and ulceration or cap rupture (UCR) for symptomatic carotid plaque characterization and to evaluate a new imaging score [Hemorrhage, Ulceration or cap rupture, Lipid-rich necrotic Core (HULC) score based on the sum of presence/absence of IPH, UCR and LR-NC; range 0–3] for assessment of recently symptomatic carotid plaques. Material and methodsTwenty-seven recently symptomatic (<8 weeks) and 36 asymptomatic patients with a carotid plaque thicker than 2 mm were prospectively imaged on a 3-T magnetic resonance (MR) system using high-resolution, multi-contrast MR sequences. Prior to analysis, all images were reviewed to assess image quality of each sequence. Sensitivity and specificity of IPH, LR-NC, UCR and HULC scores were calculated. ResultsFifty-one patients were analyzed (26 symptomatic carotids and 67 asymptomatic carotids) after exclusion of studies with poor image quality. Sensitivity and specificity for symptomatic carotid plaque was, respectively, 46.1% and 97% for IPH, 84.6% and 73.1% for UCR and 80.7% and 76.1% for LR-NC. A HULC score of 2 or more showed a sensitivity of 73% and a specificity of 92.5%. ConclusionAt 3 T, intra-plaque hemorrhage is the most specific criterion to characterize symptomatic carotid plaque. The HULC score offers the best compromise between sensitivity and specificity.

Advanced human carotid plaque progression correlates positively with flow shear stress using follow-up scan data: An in vivo MRI multi-patient 3D FSI study

Although it has been well-accepted that atherosclerosis initiation and early progression correlate negatively with flow wall shear stresses (FSS), increasing evidence suggests mechanisms governing advanced plaque progression are not well understood. Fourteen patients were scanned 2–4 times at 18 month intervals using a histologically validated multi-contrast magnetic resonance imaging (MRI) protocol to acquire carotid plaque progression data. Thirty-two scan pairs (baseline and follow-up scans) were formed with slices matched for model construction and analysis. 3D fluid–structure interaction (FSI) models were constructed and plaque wall stress (PWS) and flow shear stress (FSS) were obtained from all matching lumen data points (400–1000 per plaque; 100 points per matched slice) to quantify correlations with plaque progression measured by vessel wall thickness increase (WTI). Using FSS and PWS data from follow-up scan, 21 out of 32 scan pairs showed a significant positive correlation between WTI and FSS (positive/negative/no significance ratio=21/8/3), and 26 out of 32 scan pairs showed a significant negative correlation between WTI and PWS (positive/negative/no significance ratio=2/26/4). The mean FSS value of lipid core nodes ( n=5294) from all 47 plaque models was 63.5 dyn/cm 2, which was 45% higher than that from all normal vessel nodes ( n=27553, p<0.00001). The results from this intensive FSI study indicate that flow shear stress from follow-up scan correlates positively with advanced plaque progression which is different from what has been observed in plaque initiation and early-stage progression. It should be noted that the correlation results do not automatically lead to any causality conclusions.

Multi-contrast high spatial resolution black blood inner volume three-dimensional fast spin echo MR imaging in peripheral vein bypass grafts

The purpose of this study is to primarily evaluate the lumen area and secondarily evaluate wall area measurements of in vivo lower extremity peripheral vein bypass grafts patients using high spatial resolution, limited field of view, cardiac gated, black blood inner volume three-dimensional fast spin echo MRI. Fifteen LE-PVBG patients prospectively underwent ultrasound followed by T1-weighted and T2-weighted magnetic resonance (MR) imaging. Lumen and vessel wall areas were measured by direct planimetry. For graft lumen areas, T1- and T2-weighted measurements were compared with ultrasound. For vessel wall areas, differences between T1- and T2-weighted measurements were evaluated. There was no significant difference between ultrasound and MR lumen measurements, reflecting minimal MR blood suppression artifact. Graft wall area measured from T1-weighted images was significantly larger than that measured from T2-weighted images (P < 0.001). The mean of the ratio of T1- versus T2-weighted vessel wall areas was 1.59 (95% CI: 1.48-1.69). The larger wall area measured on T1-weighted images was due to a significantly larger outer vessel wall boundary. Very high spatial resolution LE-PVBG vessel wall MR imaging can be performed in vivo, enabling accurate measurements of lumen and vessel wall areas and discerning differences in those measures between different tissue contrast weightings. Vessel wall area differences suggest that LE-PVBG vessel wall tissues produce distinct signal characteristics under T1 and T2 MR contrast weightings.

Normalized Wall Index Specific and MRI-Based Stress Analysis of Atherosclerotic Carotid Plaques - A Study Comparing Acutely Symptomatic and Asymptomatic Patients -

Biomechanical stresses play an important role in determining plaque stability. Quantification of these simulated stresses can be potentially used to assess plaque vulnerability and differentiate different patient groups. 54 asymptomatic and 45 acutely symptomatic patients underwent in vivo multicontrast magnetic resonance imaging (MRI) of the carotid arteries. Plaque geometry used for nite element analysis was derived from in vivo MRI at the sites of maximum and minimum plaque burden. In total, 198 slices were used for the computational simulations. A pre-shrink technique was used to refine the simulation. Maximum principle stress at the vulnerable plaque sites (ie, critical stress) was extracted for the selected slices and a comparison was performed between the 2 groups. Critical stress in the slice with maximum plaque burden is significantly higher in acutely symptomatic patients as compared to asymptomatic patients (median, inter quartile range: 198.0 kPa (119.8-359.0 kPa) vs 138.4 kPa (83.8-242.6 kPa), P=0.04). No significant difference was found in the slice with minimum plaque burden between the 2 groups (196.7 kPa (133.3-282.7 kPa) vs 182.4 kPa (117.2-310.6 kPa), P=0.82). Acutely symptomatic carotid plaques have significantly high biomechanical stresses than asymptomatic plaques. This might be potentially useful for establishing a biomechanical risk stratification criteria based on plaque burden in future studies.

Quantitative Segmentation of Principal Carotid Atherosclerotic Lesion Components by Feature Space Analysis Based on Multicontrast MRI at 1.5 T

The purpose of this paper is to evaluate the capability of feature space analysis (FSA) for quantifying the relative volumes of principal components (thrombus, calcification, fibrous, normal intima, and lipid) of atherosclerotic plaque tissue in multicontrast magnetic resonance images (mc-MRI) acquired in a setup resembling clinical conditions ex vivo. Utilizing endogenous contrast, proton density, T1-weighted, and T2-weighted images were acquired for 13 carotid endarterectomy (CEA) tissues under near-clinical conditions (human 1.5 T GE Excite scanner with sequence parameters comparable to an in vivo acquisition). An FSA algorithm was utilized to segment and quantify the principal components of atherosclerotic plaques. Pilot in vivo mc-MRI images were analyzed in the same way as the ex vivo images for exploring the possible adaptation of this technique to in vivo imaging. Relative abundance of principal plaque components in CEA tissues as determined by mc-MRI/FSA were compared to those measured by histology. Mean differences +/- standard deviations were 5.8 +/- 4.1% for thrombus, 1.5 +/-1.4 % for calcification, 4.0 +/-2.8% for fibrous, 8.2 +/- 10% for normal intima, and 2.4 +/- 2.2% for lipid. Reasonable quantitative agreement between the classification results obtained with FSA and histological data was obtained for near-clinical imaging conditions. Combination of mc-MRI and FSA may have an application for determining atherosclerotic lesion composition and monitoring treatment in vivo.

Semi-Automated Segmentation of Excised Atherosclerotic Plaque Tissue Components by K-Means Clustering Techniques

With the increasing amount of medical imaging data available through advances in imaging hardware, automated pattern classification will gain more importance in the near future. This study highlights technical considerations of cluster-based k-means pattern classification techniques applied to the segmentation of major tissue components in multi-contrast magnetic resonance images (mcMRI) of excised atherosclerotic plaque tissue. The stability of the k-means algorithm was assessed by varying the initial positions of the cluster centroids and the values of the convergence criterion. Variations of the criterion function with the number of clusters were explored. The segmentation results of the k-means clustering algorithm were compared to two color- classification algorithms, the first based on dynamic programming and principal analysis, and the second based on color-bi-partitioning. While the standard k-means algorithm was found to provide a robust method for the unsupervised classification of major components in mcMR images acquired of atherosclerotic plaque tissue under clinical conditions, the Two color-classification algorithms provided less accurate classification when compared with histological data.

Automatic plaque characterization employing quantitative and multicontrast MRI

Multicontrast magnetic resonance imaging (MRI) has shown promise in identifying and characterizing atherosclerotic plaques. One of the limitations of this technique is the lack of a practical automated plaque characterization scheme. In the current study, a prior-information-enhanced clustering (PIEC) technique that utilizes both multicontrast MR images and quantitative T(2) maps is proposed to characterize atherosclerotic plaque components automatically. The PIEC algorithm was assessed on computationally simulated images and multicontrast MRI data of coronary arteries. Multicontrast (T(1)-, T(2)-, partial T(2)-, and proton density-weighted) MR images were acquired from freshly excised human coronary arteries using a 4.7T small-animal scanner. The T(2) distribution for each plaque constituent was measured by exponentially fitting the signal from multiple MR images with different TEs and the same TR. The calculated T(2) distributions were used as the a priori information and combined with the Fuzzy C-Means (FCM)-based clustering algorithm to characterize plaque constituents. The proposed PIEC technique appears to be a promising algorithm for accurate automated plaque characterization.

MR Imaging Features of Solid-pseudopapillary Tumor of the Pancreas

Solid-pseudopapillary tumor (SPT) of the pancreas is characterized as cystic, necrotic, and hemorrhagic degeneration. In this study, magnetic resonance (MR) findings of 4 cases were reviewed. Patchy or spotty areas of high intensity that suggested hemorrhagic degeneration were constantly detected on fat-suppressed T(1)-weighted images. Dynamic contrast-enhanced MR imaging revealed mild and gradual increase of contrast enhancement in solid portions. Multi-contrast MR imaging that included fat-suppressed T(1)-weighted imaging and dynamic contrast-enhanced imaging allowed accurate diagnosis of SPT and its differentiation from other tumors.

Automated classification of atherosclerotic plaque from magnetic resonance images using predictive models

The information contained within multicontrast magnetic resonance images (MRI) promises to improve tissue classification accuracy, once appropriately analyzed. Predictive models capture relationships empirically, from known outcomes thereby combining pattern classification with experience. In this study, we examine the applicability of predictive modeling for atherosclerotic plaque component classification of multicontrast ex vivo MR images using stained, histopathological sections as ground truth. Ten multicontrast images from seven human coronary artery specimens were obtained on a 9.4 T imaging system using multicontrast-weighted fast spin-echo (T1-, proton density-, and T2-weighted) imaging with 39-μm isotropic voxel size. Following initial data transformations, predictive modeling focused on automating the identification of specimen's plaque, lipid, and media. The outputs of these three models were used to calculate statistics such as total plaque burden and the ratio of hard plaque (fibrous tissue) to lipid. Both logistic regression and an artificial neural network model (Relevant Input Processor Network—RIPNet) were used for predictive modeling. When compared against segmentation resulting from cluster analysis, the RIPNet models performed between 25 and 30% better in absolute terms. This translates to a 50% higher true positive rate over given levels of false positives. This work indicates that it is feasible to build an automated system of plaque detection using MRI and data mining.

Frontiers in Cardiovascular Magnetic Resonance

Cardiovascular magnetic resonance (MR) is emerging as a multipurpose imaging modality for the assessment of cardiovascular disease in general and ischemic heart disease in particular. Currently, the pace of innovation is rapid, and the modality is changing from one that is used primarily as a research tool to one that is increasingly used in routine clinical practice. The process of innovation includes not only improvements in scanner hardware, such as coil and gradient technology, and the development of new contrast agents but also the development of novel pulse sequences. The concept of the pulse sequence, in which programming changes at the scanner can lead to fundamental changes in activating tissue, is unique to MR and gives this modality the potential to assess a vast number of biological parameters. Cardiovascular MR promises to play an important clinical and investigational role in both vascular and cardiac systems. Current and potential future applications of cardiovascular MR will be discussed with a particular focus on ischemic heart disease. Multidetector-row computed tomography, another promising and complementary noninvasive imaging technology, will be discussed briefly in relation to cardiovascular MR for the assessment of atherothrombotic disease. ### Nomenclature and Evolving Imaging Assessment Atherothrombosis is a systemic or multiterritory arterial disease that primarily affects the large- and medium systemic arteries, including the aorta and the carotid, coronary, and peripheral arteries. Although the epicardial coronary arteries appear to be the most susceptible to atherothrombosis,1,2 intramyocardial arteries are relatively resistant. The concept of multiterritory atherothrombosis has been addressed in 2 large studies of symptomatic patients that showed that at entry into the studies, 3% to 8% had symptomatic atherothrombotic disease in all 3 main arterial districts and 23% to 32% had disease in 2 districts.3,4 From a structural point of view, the 4 main components of the atherothrombotic plaques are as follows: (1) fibrocellular, …

Optimization of ex vivo CT- and MR- Imaging of Atherosclerotic Vessel Wall Changes

To optimize a methodology for ex vivo imaging of atherosclerotic vessel wall changes using multidetector-row computed tomography (MDCT) and multi-contrast magnetic resonance imaging (MRI). In phantom studies and studies on intact ex vivo porcine and human hearts, various filling mixtures of MDCT and MRI contrast agents have been evaluated, to enable filling and distension of the coronary arteries for optimal visualization of atherosclerotic vessel wall changes with both techniques. Various proportions of methyl cellulose, iodine-containing CT contrast agent and paramagnetic MR contrast agent containing iron-oxide particles have been tested. Imaging parameters have been optimized for high resolution plaque imaging using a four detector-row CT scanner and a 1.5 T MR system. Phantom studies and studies on ex vivo porcine and human hearts demonstrated optimal proportion of methyl cellulose and CT contrast agent to be 98% vs. 2%, and 75% vs. 25% of methyl cellulose vs. MR contrast agent, respectively. These proportions provided optimal opacification of the vessel lumen in the MDCT images with 250 Hounsfield Units, and good signal suppression within the vessel lumen in the MR images, resembling in vivo imaging techniques. After retrospective matching with histopathology, atherosclerotic lesions of the human ex vivo specimens could be identified on MRI and MDCT images. Using an optimized mixture of methyl cellulose, MDCT and MRI contrast agents, visualization of atherosclerotic vessel wall changes is feasible, and applicable to various ex vivo models.

Hemorrhage in the atherosclerotic carotid plaque: a high-resolution MRI study.

High-resolution, multicontrast magnetic resonance imaging (MRI) has developed into an effective tool for the identification of carotid atherosclerotic plaque components, such as necrotic core, fibrous matrix, and hemorrhage/thrombus. Factors that may lead to plaque instability are lipid content, thin fibrous cap, and intraplaque hemorrhage. Determining the age of intraplaque hemorrhage can give insight to the history and current condition of the biologically active plaque. The aim of this study was to develop criteria for the identification of the stages of intraplaque hemorrhage using high-resolution MRI. Twenty-seven patients, scheduled for carotid endarterectomy (CEA), were imaged on a 1.5-T GE SIGNA scanner (sequences: 3-dimensional time of flight, double-inversion recovery, T1-weighted (T1W), PDW and T2W). Two readers, blinded to histology, reviewed MR images and grouped hemorrhage into fresh, recent, and old categories using a modified cerebral hemorrhage criteria. The CEA specimens were serially sectioned and graded as to presence and stage of hemorrhage. Hemorrhage was histologically identified and staged in 145/189 (77%) of carotid artery plaque locations. MRI detected intraplaque hemorrhage with high sensitivity (90%) but moderate specificity (74%). Moderate agreement in classifying stages occurred between MRI and histology (Cohen kappa=0.7, 95% CI: 0.5 to 0.8 for reviewer 1 and 0.4, 95% CI: 0.2 to 0.6 for reviewer 2), with moderate agreement between the 2 MRI readers (kappa=0.4, 95% CI: 0.3 to 0.6). Multicontrast MRI can detect and classify carotid intraplaque hemorrhage with high sensitivity and moderate specificity.

New technological developments in the clinical imaging of atherosclerotic plaque.

Direct visualization of the composition of the atherosclerotic plaque during its natural history and after therapeutic intervention may be helpful in detecting lesions with high risk of acute events and in understanding progression and regression of the disease. A wide variety of invasive and non-invasive imaging techniques is available to detect clue aspects of atherosclerosis from the early stage to the clinical evidence appearance. We will firstly review the ongoing technological and clinical research on both invasive and non-invasive techniques. Afterward, we will discuss in detail the use of high-resolution, multi-contrast magnetic resonance imaging for non-invasive imaging of the plaque and its characterization in terms of its various components (i.e., thickness, lipid, fibrous, calcium, or thrombus). Finally, we will describe the potential of quantitative analysis in describing of plaque constituents with improved reproducibility.