Vessel wall characterization using quantitative MRI: what’s in a number?
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The past decade has witnessed the rapid development of new MRI technology for vessel wall imaging. Today, with advances in MRI hardware and pulse sequences, quantitative MRI of the vessel wall represents a real alternative to conventional qualitative imaging, which is hindered by significant intra- and inter-observer variability. Quantitative MRI can measure several important morphological and functional characteristics of the vessel wall. This review provides a detailed introduction to novel quantitative MRI methods for measuring vessel wall dimensions, plaque composition and permeability, endothelial shear stress and wall stiffness. Together, these methods show the versatility of non-invasive quantitative MRI for probing vascular disease at several stages. These quantitative MRI biomarkers can play an important role in the context of both treatment response monitoring and risk prediction. Given the rapid developments in scan acceleration techniques and novel image reconstruction, we foresee the possibility of integrating the acquisition of multiple quantitative vessel wall parameters within a single scan session.
keywordsMRI Atherosclerosis Quantitative imaging Vessel wall imaging Plaque imaging
Atherosclerosis is the leading cause of death in the western world, and is responsible for the majority of cerebrovascular and cardiovascular events such as ischemic stroke and myocardial infarction . Atherosclerosis consists in the formation of “plaques” in the arterial vessel wall. Endothelial dysfunction, mainly related to local reduction of wall shear stress in the presence of non-laminar flow profiles, plays a pivotal role in the initiation and progression of atherogenesis. Disruption of the endothelial barrier facilitates the subendothelial accumulation of lipids, which triggers the initial inflammatory response that leads to plaque formation. Subsequent progression of atherosclerotic disease involves numerous processes that continuously alter vessel wall composition, including smooth muscle proliferation and angiogenesis, as well as the formation of intraplaque hemorrhage (IPH), lipid necrotic core and calcifications [2, 3].
Non-invasive imaging techniques have played an important role in the assessment of different plaque phenotypes, as well as in measuring changes in biological processes that occur during the different stages of atherosclerosis development . Vessel wall magnetic resonance imaging (MRI) has proven to be a powerful technique for characterizing atherosclerosis in various regions of the vascular system, including the carotid and coronary arteries, aorta, and peripheral and intracranial arteries [5, 6, 7, 8, 9, 10]. By enabling the evaluation of plaque composition and physiology, in vivo vessel wall MRI has helped refine the assessment of plaque risk profiles for rupture and subsequent cardiovascular events beyond simple lesion size [11, 12]. Initial studies used qualitative imaging primarily for identifying distinct patterns of high/low signal intensity associated with different phenotypes of atherosclerotic plaque. In the past few years, however, there has been increasing interest in developing imaging methods that provide quantitative data related to vessel wall structure and function. Not only does this improve longitudinal monitoring of the progression of atherosclerosis; it also provides sensitive disease markers that may serve as surrogate endpoints for evaluating the effect of novel treatment strategies.
Contrast between different plaque components may originate from differences in their relaxation time constants T1 and T2. These values can be quantified in each voxel by parametric fitting of several images with different contrast weighting. The resulting T1 and T2 maps enable automated plaque segmentation in its various constituents. Additionally, the MR signal can be made sensitive to water diffusion, which can be used to map the spatial variation in the apparent diffusion coefficient (ADC) within the plaque. This is of particular interest for detecting lipid accumulation, one of the major risk factors associated with plaque rupture. Contrast-enhanced MRI has been used to assess changes in vessel wall permeability, which can be increased both through disruption of the luminal endothelial layer and by the formation of leaky angiogenic vessels within the plaque. More specifically, imaging with dynamic contrast-enhanced (DCE) MRI, a technique that samples the influx of contrast agent in the plaque over time using fast T1-weighted (T1w) imaging sequences, has enabled the quantification of several pharmacokinetic parameters, including endothelial permeability and microvascular volume. Finally, blood flow measurements with phase-contrast MRI can be used to quantify pulse wave velocity (PWV), which is a common evaluation of vessel wall stiffness. More recently, 4D flow MRI has enabled local measurement of wall shear stress (WSS), which plays a crucial role in vascular endothelial function.
In this review, we present emerging techniques for quantitative MR imaging of the vessel wall. While the main focus will be on the carotid arteries, the most extensively studied vascular bed, examples in other vascular regions (aorta, intracranial vessels) will also be touched upon. We will present the latest developments in MR sequence and protocol design, and discuss their advantages and pitfalls in the quantification of vessel wall composition and function. While vessel wall thickness reflects anatomical rather than structural/functional information, it is still considered an important quantitative parameter in characterizing atherosclerotic burden. We therefore start with a short overview of different two- and three-dimensional sequences that are used for this purpose, which are often the basis for other quantitative methods as well. Finally, we will present current promising developments in MRI that will allow further improvement in the techniques presented here.
Vessel wall thickening is one of the early visible manifestations of atherosclerosis, and therefore remains one of the most important diagnostic readouts of atherosclerotic burden. Large clinical studies have demonstrated an association between carotid intima-media thickness as measured with ultrasound, and overall risk for cardiovascular events such as stroke or myocardial infarction [13, 14]. Ultrasound still remains the first choice in clinical practice for assessing carotid stenosis after ischemic events, not least for its cost effectiveness. However, black-blood MRI techniques for measuring plaque burden have improved tremendously in recent years, and are increasingly used in studies on atherosclerosis progression or treatment effect [15, 16, 17]. Moreover, MRI has no limitations in depth penetration and is thus a powerful tool for investigating not only superficial arteries such as the carotids, but also those such as the intracranial  and coronary arteries .
2D black-blood MRI
Blood suppression is essential for achieving accurate delineation of the vessel wall, which would otherwise be compromised by smearing of the bright-blood lumen signal. Two-dimensional (2D) T2-weighted (T2w) spin-echo sequences have inherent blood suppression due to outflow effects at long echo times; however, this mechanism is not compatible with short echo times needed for T1w imaging.
The first robust technique allowing for 2D T1w black-blood imaging of the arterial vessel wall was proposed by Edelman et al. . This spin-echo-based method achieves blood suppression by using a pair of non-selective and slice-selective inversion pulses (double-inversion recovery axial image recovery, or DIR), thereby effectively inverting only the tissue and blood outside the imaging slice (Fig. 1a). The inversion time (TI) between the inversion pulses and imaging readout is chosen such that blood longitudinal magnetization is nulled (as a result of T1 relaxation), while at the same time non-inverted blood flows out of the imaging slice. The slightly higher signal-to-noise ratio (SNR) and robust blood suppression make T1w imaging the preferred choice for 2D vessel wall thickness measurements.
3D black-blood MRI
Increasing interest in isotropic 3D imaging protocols has led to the development of 3D black-blood imaging sequences that do not rely on outflow effects and are therefore more robust against slow flow artifacts. 3D turbo spin-echo (TSE) sequences actually have inherent black-blood properties themselves, caused by the buildup of intravoxel dephasing as a result of the positive gradient moment of the readout gradient during the echo train. While this was presented in the early literature as an alternative to bright-blood brain angiography , successful implementation for vessel wall imaging required novel variable-flip-angle (VFA) refocusing schemes [23, 24, 25] that also allowed a stable signal response for longer echo trains (Fig. 1b). This ensures a favorable point-spread function to prevent blurring of the thin vessel wall. Naturally, this can only be achieved for specific values of T1 and T2, and the exact choice of flip angle scheme will always be a trade-off between tissue contrast and effectiveness of flow suppression.
Another class of black-blood methods achieves blood suppression independent of the acquisition scheme through the use of black-blood preparation modules. One method, motion-sensitized driven equilibrium (MSDE, Fig. 1c), combines T2 preparation with flow-sensitizing gradients [26, 27]. If the first moment of the preparation module is sufficiently high, intravoxel dephasing for flowing blood occurs, while the static tissue effectively only undergoes T2 relaxation during the time interval TEprep .
The final tip-up pulse restores the static tissue longitudinal magnetization for subsequent readout using turbo field-echo (TFE) or TSE acquisition schemes. As shown by Fan et al. , the latter seems the more effective strategy, as it adds the black-blood properties of the TSE readout.
An alternative preparation method uses so called-DANTE (delay alternating with nutation for tailored excitation), consisting of a non-selective train of small-flip-angle RF pulses [28, 29]. While this preparation is much longer than MSDE (~100–150 ms), static tissue signal is better preserved and suppression occurs even at low velocities. DANTE is particularly promising for intracranial imaging, where it also enhances suppression of cerebrospinal fluid closely surrounding the vessels [30, 31, 32, 33].
2D versus 3D imaging
Given the current resolution in 3D MRI protocols, overestimation of vessel wall thickness is likely to occur —especially considering smaller vessels such as the intracranial or coronary arteries [37, 38]—although this issue may be less problematic in later stages of atherosclerosis, where plaque formation has caused significant wall thickening .
While excellent reproducibility values for 2D thickness measurements have been reported for carotid and aortic vessel walls [40, 41], a great advantage of 3D imaging is that it requires no tedious, accurate planning of the imaging slices, while still allowing reconstructions in arbitrary planes. This may partly explain the good reproducibility of 3D black-blood methods reported for various applications, such as the carotid arteries [42, 43], thoracic aortic wall [8, 24, 44], abdominal aorta  and even the coronary arteries . While generally not applied, ECG and/or respiratory triggering can further minimize blurring due to vessel wall pulsation or breathing motion .
Although different black-blood mechanisms can be easily described, their exact performance in terms of SNR, tissue/lumen contrast-to-noise ratio (CNR), and effective resolution are highly dependent on the exact sequence parameters, including both the preparation module and the specific acquisition scheme (e.g. TFE vs. TSE).
While comparisons between different methods have been reported , the large number of sequence parameters makes a fair comparison very challenging. Consequently, no consensus on optimal vessel wall imaging protocols has been reported thus far.
The excellent intrinsic soft tissue contrast of MRI has enabled visualization of different structural components within the plaque, such as lipid-rich necrotic core (LRNC), calcification (CA), fibrous tissue (FIB) and intraplaque hemorrhage (IPH). To this end, many studies have used multi-contrast-weighted imaging, in which each component can be identified by the specific combination of hypo- and/or hyperintense signal intensities on T1-, T2(*) - and protein density-weighted (PDw) images [6, 45, 46]. Although accumulating evidence from imaging studies shows that the presence or absence of these components can be related to subsequent cardiovascular events [11, 47], qualitative interpretation of these images or the need to calculate relative signal intensities (e.g. compared to sternocleidomastoid muscle) gives rise to high intra/inter-observer variability . This is mainly due to the strong influence of spatial variations in coil sensitivity on relative signal intensity, as well as the specific choice of MR sequence parameters. Therefore, the quantification of T1 and T2(*) relaxation time constants of tissue on a voxel-wise basis—as quantitative measures of the underlying tissue composition—is of great interest. While quantitative T1 and T2(*) values have been reported in histological studies of carotid endarterectomy specimens [49, 50], translation of existing techniques to in vivo vessel wall imaging has long been complicated by the additional need for high-resolution imaging, blood suppression and cardiac gating. The following sections will present the newest techniques that have overcome most of these challenges.
T1 and T2(*) relaxometry
Instead of using multi-echo spin-echo-based sequences, T2* can be determined using a blood-suppressed multi-echo gradient echo approach, with typical echo times of 3–40 ms. Unlike T2, T2* is strongly affected by the presence of magnetic field inhomogeneities and thus might not solely reflect tissue structure. At the same time, this makes T2* imaging very sensitive in detecting protein-bound iron. Raman et al.  were the first to conduct an extensive study of the role of iron in atherosclerosis using quantitative T2* measurements. They found a significant decrease in T2* between asymptomatic (34.4 ± 2.7 ms) and symptomatic patients (20.0 ± 1.8 ms). Furthermore, ex vivo iron quantification in endarterectomy specimens showed equal total iron content in both groups, but greatly reduced levels of paramagnetic Fe(III) complexes. Overall, these results strongly suggest that symptomatic plaques are associated with higher amounts of ferritin-bound iron, which was sensitively assessed using quantitative T2* MRI. In addition to endogenous iron, T2*-weighted imaging has also been frequently used to detect macrophage-mediated uptake of intravenously injected superparamagnetic iron oxide (SPIO) particles as a surrogate marker of plaque inflammation [55, 56]. However, such studies still suffered from the use qualitative MRI methods . In more recent studies, quantitative T2* mapping has been applied in combination with QIR blood suppression in order to increase the accuracy in assessing SPIO accumulation by calculating ΔT2* or ΔR2* between pre- and post-contrast scans [58, 59]. While T2* values can be prone to magnetic field inhomogeneities, both studies did report similar baseline T2* values of approximately 25 ms, indicating good reproducibility with this approach. Unfortunately, 3D implementation of these multi-echo techniques has not yet proven feasible, most likely due to the inevitable increase in repetition time, leading to clinically unacceptable acquisition times.
While the main benefit in using quantitative T1 and T2(*) mapping—compared to multi-contrast T1w and T2(*)w imaging—is to improve reproducibility and facilitate longitudinal monitoring of changes in plaque composition, it still does not allow for direct assessment of the relative contribution of different tissues in each voxel. In contrast, for instance, Koppal et al. were able to assess quantitative maps of fat content based on Dixon imaging, showing significant differences between the lipid core (12.6%) and surrounding tissue (9.2%) . On the other hand, the use of multi-contrast imaging to detect different plaque components (i.e. LRNC, IPH) has been well validated against histology [45, 51]. Quantitative relaxation parameter mapping extends this concept and might enable better definition of thresholds for discriminating between these different tissue types. Indeed, a recent study reported that LRNC detection based on T2 mapping—pixels with T2 <42 or T2 >90 ms when IPH was included—showed very good correlation with histology (R = 0.85) and had good sensitivity (AUC = 0.79) for detecting recently symptomatic plaques .
MRI is also able to quantify water diffusion within tissues. Strong field gradients applied on each side of a 180° refocusing pulse cause diffusion-mediated signal attenuation due to phase dispersion of spins. Conversely, in static tissue, the effect of both gradients cancels out and the signal is maintained. The degree of diffusion weighting is given by the b-value, which depends on the gradient strength, duration and spacing . Similar to varying TE to quantify T2(*), the apparent diffusion coefficient (ADC) can be estimated by an exponential fit of signals acquired at different b-values, which determines the amount of diffusion weighting. In vessel wall imaging, diffusion weighting is of particular interest for detecting the presence of an LRNC, which from ex vivo studies has been known to have a strongly decreased ADC [68, 69]. In fact, Clarke et al. showed that, compared to T1 and T2 quantification, ADC was the parameter that could best distinguish LRNC from FIB .
Diffusion-weighted imaging (DWI) is typically performed using 2D single-shot echo-planar imaging (EPI) sequences, which provides time efficiency for sufficient averaging of the low DW signal (because of the additional need for long TEs). However, EPI is very susceptible to B0 inhomogeneities, because phase errors accumulate for each additional phase encoding step. Kim et al.  were the first to apply inner-volume imaging for vessel wall applications, reducing the effective field of view (FOV) and thereby the echo train length by a factor of 4. In this way, good-quality ADC maps of 2-mm slices were obtained with a resolution of 1 × 1 mm2. This spatial resolution, however, is still inferior to the resolution used for vessel wall thickness measurements.
While challenging, extension of DWI to a diffusion tensor imaging (DTI) protocol using multiple gradient directions would allow calculation of the fractional anisotropy (FA) of the vessel wall microscopic fiber structure. Opriessnig et al.  were recently the first to apply a 2D DTI sequence using four b-values and 18 diffusion directions on a 10-mm carotid artery segment. In 12 healthy volunteers, the authors found a significant correlation between FA and age, indicating possible alterations of the vessel wall microstructural integrity. Moreover, the reproducibility of FA measurements appeared very high, with CV values no higher than approximately 5%.
Quantification of permeability with dynamic contrast-enhanced MRI
In the literature, average plaque K trans values calculated using this model have ranged from 0.05 to 0.3 min−1 [80, 81, 82, 83, 84, 85, 86, 87, 88], while average v p values were found to vary between 4 and 25% [78, 83, 85, 87, 89]. These broad ranges may reflect differences in patient populations and/or disease stages in animal models, or different acquisition or analysis methods. Using a 2D bright-blood (i.e. allowing sampling of the MR signal in the blood plasma during the dynamic acquisition) spoiled gradient recalled echo (SPGR) MR sequence and Patlak kinetic analysis, Kerwin et al.  were the first to demonstrate a significant, positive correlation between microvessel density in human carotid atherosclerotic plaques (CD31 immunostaining) and the parameter v p (fractional microvascular volume) derived from DCE-MRI. Using this methodology, the same group also demonstrated a significant, positive relationship between v p, K trans (permeability) and plaque macrophages, neovasculature and loose matrix (LM) . As for clinical parameters, K trans was found to correlate with lower levels of high-density lipoproteins (HDL)  and higher levels of C-reactive protein , and was higher in smokers than non-smokers [80, 83]. This analysis was extended to quantify the difference in DCE-MRI parameters between different plaque components including LRNC, IPH, LM, FIB and CA . It was demonstrated that while LM and FIB showed relatively high values of K trans and v p, NC, IPH and CA exhibited significantly lower K trans and v p. O’Brien et al. [85, 90] recently demonstrated a relationship between the duration of statin therapy and v p from DCE-MRI: the shorter the duration of statin therapy, the higher the v p values. The presence of metabolic syndrome, higher body mass index and plasma lipoprotein(a) values were also associated with higher v p values.
While the Patlak model has been widely used for quantifying endothelial permeability and microvascular volume in atherosclerosis, the best choice of model for analyzing DCE-MRI data of the vessel wall is still a topic of investigation [79, 91]. As an alternative to kinetic modeling, non-model-based approaches, such as area under the enhancement curve (AUC), uptake slope, time to peak or maximum concentration, can also be used to analyze DCE-MRI data. While the relationship between these non-model based parameters and microvascular volume and permeability is not straightforward, AUC has been particularly valuable as a surrogate measure of plaque neovascularization and permeability calculated from so-called black-blood DCE-MRI data, where the MR signal from the blood plasma is purposively suppressed to improve vessel wall delineation, and kinetic modeling cannot be easily performed. Using black-blood DCE-MRI, Calcagno et al.  demonstrated a positive, significant correlation between the parameter AUC and plaque microvessels count (CD31 immunostaining) in aortic plaques of atherosclerotic rabbits. The reproducibility of this technique was also evaluated and was found to be very good . In addition, AUC has been used as a surrogate marker of drug efficacy to evaluate the impact on vascular permeability/inflammation of several approved (atorvastatin , pioglitazone ) and novel (liposomal corticosteroids , liver X receptor [LXR] agonist ) drugs. Chen et al.  showed an increase in both plaque K trans relative to skeletal muscle  and AUC in aortic plaques of atherosclerotic rabbits between 3 and 6 months of an atherosclerotic diet. AUC was also used by Calcagno et al.  to compare perfusion/permeability by DCE-MRI to vascular inflammation by 18F-fluorodeoxyglucose (FDG) uptake by positron emission tomography with computed tomography (PET/CT) in sub-clinical plaques of patients with risk factor for coronary artery disease (CAD). In this case the authors found a weak negative relationship between the two techniques in this patient population.
Despite these significant advances, vascular DCE-MRI is still significantly challenged in achieving accurate quantification of plaque microvascular burden and permeability. As mentioned above, it is difficult to extract fully quantitative information from black-blood vascular DCE-MRI (either 2D or 3D), due to the inability to sample the concentration of contrast agent in the blood plasma (the so-called arterial input function, AIF), which is a necessary input for kinetic models. On the other hand, even bright-blood vascular DCE-MRI approaches may carry some degree of error in the quantification of vascular permeability parameters, also stemming from potential inaccuracies when estimating the AIF from the MR signal itself. As with all other vessel wall imaging techniques, vascular DCE-MRI requires imaging with high spatial resolution, which may render the temporal resolution of the acquisition inadequate for sampling the fast contrast agent kinetics in the blood plasma. In addition, MR sequence parameters used for DCE-MRI are typically optimized to accurately capture the dynamic signal range of enhancement in atherosclerotic plaques, and may not be adequate to accurately capture signal enhancement in the vessel lumen, where contrast agent concentrations are much higher, and T1 much lower, during dynamic imaging. Recent studies have focused on overcoming these challenges by proposing either 2D  or 3D [105, 106] sequences that allow for accurate sampling of both blood and plaque kinetics, interleaving the acquisition of images with different spatial and temporal resolution and different imaging parameters. For example, AIF images can be acquired with lower spatial resolution, which allows for faster imaging (high temporal resolution) and imaging parameters optimized for the high signal enhancement (low T1 values) found in the vessel lumen. Conversely, plaque dynamic images can be acquired with high spatial resolution, lower temporal resolution, and imaging parameters optimized to capture the signal enhancement of the arterial vessel wall. Other authors have instead explored the use of phased-based rather than magnitude-based AIFs for kinetic modeling of vascular DCE-MRI data . Using simulations and phantom experiments, the authors found that phase-based AIF offered a more accurate sampling of the true contrast agent kinetics in the blood plasma. While the absolute value of kinetic parameters derived from magnitude- and phase-based AIF were different, they were shown to be highly correlated.
Alternative techniques for measuring permeability
In addition to DCE-MRI, various other techniques have been proposed for quantifying endothelial permeability in atherosclerotic plaques. Delayed-enhancement imaging with either low molecular weight gadolinium chelates or albumin-binding agents has been used as a semi-quantitative measure of plaque permeability in both animal models (mice , rabbits [109, 110, 111]) and humans [112, 113]. Phinikaridou et al.  and Bar et al.  quantified endothelial permeability in the brachiocephalic artery of atherosclerotic mice as a change in the vessel wall relaxation rate (R1, s−1) 30 min after injection of an albumin-binding contrast agent (gadofosveset trisodium). This technique has also been used successfully to quantify changes in permeability in the murine brachiocephalic artery after therapeutic intervention [111, 116]. More recently, Phinikaridou et al.  used this same technique to quantify endothelial permeability in aortic plaques in atherosclerotic rabbits, and demonstrated higher R1 (indicative of higher permeability) in aortic segments more prone to disruption after injection of Russell’s viper venom. Unlike quantitative dynamic imaging with DCE-MRI, these techniques measure signal enhancement or quantify tissue relaxation time only at a fixed point after contrast agent injection. This approach is particularly well suited for higher molecular weight or albumin-binding contrast agents, whose plaque uptake kinetics are intrinsically slower. While failing to capture the dynamic uptake of contrast agents over time, these techniques offer a simpler and robust alternative to DCE-MRI for quantification of plaque microvascularization and permeability.
Flow-derived biomechanical wall parameters
Wall shear stress
Atherosclerosis originates predominantly at regions with perturbed flow that can occur at the outer edges of vessel bifurcations. In these regions, hemodynamic wall shear stress (WSS), the frictional force sensitized by endothelial cells forming the inner lining of blood vessels, is weaker than in protected regions and can even exhibit direction reversal. The atherogenic endothelial phenotype resulting from low WSS mediates recruitment and activation of monocytes, which can subsequently lead to plaque formation . WSS is also known to increase with increasing blood flow. In response, the vessel dilates to reduce blood flow such that WSS returns to normal values. Regions where WSS is chronically elevated, such as the apices of bifurcations in the cerebral vasculature, are predisposed to the formation of aneurysms . WSS therefore represents a key link between blood flow and alterations in biomechanical vessel wall parameters.
Flow measurement using MRI has traditionally been performed using 2D phase-contrast imaging in most vascular beds. With gating to the cardiac cycle, a time-resolved (cine) measurement can be performed . Currently, a comprehensive assessment of flow in an entire 3D volume (3D cine phase-contrast MRI or 4D flow MRI) is feasible , enabling the measurement of full time-varying 3D velocity fields in a wide variety of cardiovascular regions .
The importance of WSS in vascular disease has led to widespread interest among researchers in obtaining reliable estimates of WSS from MRI-measured velocity data. Oshinski et al. were the first to develop a method based on linear fitting through velocity values to obtain the velocity derivative (the shear rate) at the wall . Multiplication of the shear rate by dynamic viscosity yields the WSS. Other groups developed WSS estimation based on parabolic fitting, which showed greater accuracy than linear fitting [124, 125]. Stalder et al. used cubic B-splines to derive the shear rate at the wall . Another important hemodynamic parameter shown to correlate with atherosclerosis is the oscillatory shear index (OSI) . OSI represents the temporal oscillation of WSS during the cardiac cycle, the deviation of WSS from its predominant direction parallel to the vessel. Thus, the OSI can be calculated using methods to estimate time-resolved WSS.
These techniques applied to MRI-measured flow data have provided valuable insight into the relation between abnormal WSS and pathophysiology. Duivenvoorden et al. showed that WSS was an independent predictor of carotid wall thickness, lumen area and vessel size . Mutsaerts et al. found that WSS was associated with periventricular white matter lesions and cerebral infarcts . Markl et al. observed that low WSS and high OSI, which are potentially atherogenic wall parameters, were predominantly concentrated at the posterior wall of the internal carotid artery in normal controls, a region known to be prone to atherosclerosis . Wentzel et al. reported that the presence of atherosclerotic plaques in the descending aorta was associated with low WSS . Other studies showed that WSS on the ascending aorta was elevated compared to healthy controls in bicuspid valve disease, which implicates a relationship between elevated WSS and aortic dilation [132, 133].
Some important considerations should be kept in mind when estimating 4D flow MRI-derived WSS. First, several studies showed that an accurate definition of the vessel wall is paramount [125, 135]. Nonetheless, low inter-observer variability in WSS was found for both 2D (<10%) and 3D (<5%) WSS algorithms [137, 138]. Second, the absolute value of WSS decreases with spatial resolution [126, 135]. Thus, 4D flow MRI-derived WSS is always underestimated compared to computational fluid dynamics where fine meshes are used . Qualitatively, however, regions of high and low WSS and the direction of WSS tend to correspond well [140, 141, 142](Fig. 7).
Summary and future perspectives
In the last decade, technological advances have strengthened the position of MRI in the assessment of quantitative, physiological parameters regarding tissue structure and pathology. 3D blood suppression techniques and 3D time-resolved (4D) imaging have enabled the development of strategies for assessing the anatomical, structural and functional status of the vessel wall. A few consensus statements have been published on specific applications discussed in this review [157, 158]. Similar publications on “best practice” vessel wall imaging protocols are needed, and will help to propel this novel research field forward. In this respect, reproducibility studies on quantitative vessel wall T1/T2(*), DCE and flow imaging protocols are highly important.
Aside from what is presented in this review, the search continues for MR techniques that quantify other specific markers related to vessel wall pathology, e.g. strain , or other contrast mechanisms to increase sensitivity for specific atherosclerotic plaque features. The latter might include T1rho imaging for assessment of fibrosis  or susceptibility-weighted imaging to detect calcifications . While there is much interest in techniques that do not rely on the use of MRI contrast agents, this review has shown the relevance of DCE imaging for measuring plaque microvascular volume and permeability, which are strongly associated with vessel wall inflammation. The use of untargeted iron oxide nanoparticles has also been briefly discussed here. Novel “smart” nanoparticles that specifically target biomarkers of inflammation have yielded very promising results in animal models of atherosclerosis [162, 163, 164]. By labeling with MRI contrast agents, accumulation of these nanoparticles could be quantified using T1 and/or T2 mapping protocols as described in this review. MRI of nuclei other than 1H, such as 19F, is also a topic of active investigation for the absolute quantification of plaque inflammation using perfluorocarbons .
Quantifying physiologically relevant parameters in addition to standard anatomical imaging naturally entails longer acquisition times and/or reduced SNR. Furthermore, several sources of errors, such as the choice of fitting algorithm, motion artifacts, and data SNR, may affect parameter quantification. Aside from developing new quantitative readouts, current efforts are predominantly focused on improving the accuracy, precision and scan efficiency of existing methods by making use of ultra-high-field imaging as well as novel reconstruction algorithms.
Recent years have seen the introduction of 7T imaging in clinical research, with the direct advantage of an increase in SNR directly proportional to the strength of the magnetic field .
Advanced reconstruction techniques
Quantitative imaging generally comes with the need to acquire multiple images, either having different sensitivity to the parameter of interest (T1, T2, ADC) or sampling a dynamic process over time (DCE, flow). Long acquisition times are therefore one of the big hurdles in this field, and complicate the translation of such methods to a clinical environment. What may be able to change this in the near future is the rapid improvements in reconstruction and post-processing techniques. In recent years, mathematics has played an increasingly important role in advancing MRI technology. In particular, the introduction of “compressed sensing” has taught us that images can be reconstructed with far less data than was considered necessary using assumptions of image sparsity in combination with iterative reconstruction algorithms . As this is quite a generally applicable concept, many researchers have already experimented with compressed sensing to improve vessel wall MRI. For example, 3D MSDE acquisitions were accelerated to a factor of 5 without significant deviations in assessing vessel wall or plaque component dimensions [175, 176]. Gong et al. realized that multi-contrast vessel wall MRI could be significantly accelerated by assuming significant shareable information between the different contrast acquisitions . The use of their proposed reconstruction algorithm allowed for highly accelerated T2w and PDw scans (up to a factor of 6) once a moderately accelerated T1w scan (e.g. using SENSE) was available. Using regular CS techniques, Yuan et al. reduced a 3D MSDE-based T2 mapping protocol to a clinically acceptable imaging time of 7 min ; reconstruction of separate images was done independently. A more promising technique related to quantitative relaxation time (T1/T2) mapping are model-based algorithms that use prior knowledge of the relaxation equations to reconstruct undersampled scans at multiple inversion or echo times [179, 180]
For dynamic or time-resolved imaging, temporal relations between images are used in order to achieve high undersampling factors for the individual time frames. This technique shows great promise for achieving higher-temporal-resolution 3D DCE imaging  or allowing interleaved sampling of black- and bright-blood images for the simultaneous measurement of the arterial input function and vessel wall signal response . For 4D flow applications, the use of k-t GRAPPA has enabled acceleration factors of up to 5 without substantial errors in derived WSS parameters .
Perhaps the greatest benefit of freely undersampling k-space data is the ability to select the optimal data retrospectively. This can be used to circumvent the need for cardiac and respiratory gating, without losing scan efficiency, and order the data in the reconstruction process based on available data of cardiac and respiratory motion from either sensors or MR navigators. Using appropriate motion correction algorithms, this even allows for 100% scan efficiency [182, 183]. Recent impressive data from Ginami et al.  showed high-quality coronary vessel wall imaging by making reconstructions using different timings of the acquisition window within the cardiac cycle in order to retrospectively achieve the optimal vessel wall delineation.
Multimodal imaging: PET/MRI
In addition to the development of novel MR methods and contrast mechanisms, the integration of MRI with other imaging modalities that interrogate different aspects of plaque physiology is quickly becoming a reality. As a notable example, the introduction of simultaneous PET/MRI systems allows for the seamless combination of anatomical and physiological imaging with MRI, and metabolic/functional imaging with PET. PET is a highly sensitive modality, and has already been extensively validated for quantification of plaque macrophages with 18F-FDG [185, 186]. However, PET is an imaging modality with intrinsically low spatial resolution, and it is traditionally combined with computed tomography (CT) for improved anatomical localization. Combining PET with MRI instead of CT offers several advantages. MRI provides high-spatial-resolution imaging and better soft tissue contrast than CT, and enables the quantification of several physiological parameters in the vessel wall, as previously described in this review. The better anatomical definition of MRI can be used to apply partial volume and motion corrections to improve the localization of the PET signal [187, 188].
Last but not least, combining PET with MRI instead of CT reduces patient exposure to ionizing radiation—a highly desirable feature for longitudinal, repeated imaging in patients with chronic diseases (such as atherosclerosis). The fact that the two modalities are intrinsically co-registered allows for easier image interpretation, image analysis and experimental design [188, 189]. However, there are also specific challenges that may arise when combining these two modalities, such as the effective conversion of MR images to accurate PET attenuation maps, which are still the subject of active investigation .
Compliance with ethical standards
Conflict of interest
The authors declare that they do not have a conflict of interest.
Informed consent was obtained from all individual participants included for the purpose of generating exemplary data sp ecifically for this review article. Experiments were done in accordance with the Declaration of Helsinki and in compliance with current Good Clinical Practice guidelines.
BF.C. is funded by a VENI Grant (#14348) from the Dutch Technology Foundation STW. C.C. and Z.A.F. are supported by NIH/NHLBI R01 HL071021. C.C. is also supported by American Heart Association Scientist Development Grant 16SDG27250090. P.v.O., G.J.S. and A.J.N. are supported by Grants from the Dutch Technology Foundation STW (HTSM 13928 and CARISMA 11631).
- 8.Roes SD, Westenberg JJM, Doornbos J, van der Geest RJ, Angeli E, de Roos A, Stuber M (2009) Aortic vessel wall magnetic resonance imaging at 3.0Tesla: a reproducibility study of respiratory navigator gated free-breathing 3D black blood magnetic resonance imaging. Magn Reson Med 61:35–44PubMedPubMedCentralCrossRefGoogle Scholar
- 15.Corti R, Fuster V, Fayad ZA, Worthley SG, Helft G, Smith D, Weinberger J, Wentzel J, Mizsei G, Mercuri M, Badimon JJ (2002) Lipid lowering by simvastatin induces regression of human atherosclerotic lesions: 2 years’ follow-up by high-resolution noninvasive magnetic resonance imaging. Circulation 106:2884–2887PubMedCrossRefGoogle Scholar
- 17.Underhill HR, Yuan C, Zhao X-Q, Kraiss LW, Parker DL, Saam T, Chu B, Takaya N, Liu F, Polissar NL, Neradilek B, Raichlen JS, Cain VA, Waterton JC, Hamar W, Hatsukami TS (2008) Effect of rosuvastatin therapy on carotid plaque morphology and composition in moderately hypercholesterolemic patients: A high-resolution magnetic resonance imaging trial. Am Heart J 155:584PubMedCrossRefGoogle Scholar
- 24.Eikendal ALM, Blomberg BA, Haaring C, Saam T, van der Geest RJ, Visser F, Bots ML, den Ruijter HM, Hoefer IE, Leiner T (2016) 3D black blood VISTA vessel wall cardiovascular magnetic resonance of the thoracic aorta wall in young, healthy adults: reproducibility and implications for efficacy trial sample sizes: a cross-sectional study. J Cardiovasc Magn Reson 18:20PubMedPubMedCentralCrossRefGoogle Scholar
- 34.Duivenvoorden R, de Groot E, Afzali H, VanBavel ET, de Boer OJ, Lamris JS, Fayad ZA, Stroes ESG, Kastelein JJP, Nederveen AJ (2009) Comparison of in vivo carotid 3.0-T magnetic resonance to B-mode ultrasound imaging and histology in a porcine model. JACC Cardiovasc Imaging 2:744–750PubMedCrossRefGoogle Scholar
- 36.van den Berg AM, Coolen BF, Nederveen AJ (2013) 2D T1-weighted TSE vs. 3D MERGE in carotid artery wall imaging. In: Proceedings of the SMRT, Salt Lake CityGoogle Scholar
- 37.Qiao Y, Guallar E, Suri FK, Liu L, Zhang Y, Anwar Z, Mirbagheri S, Xie YJ, Nezami N, Intrapiromkul J, Zhang S, Alonso A, Chu H, Couper D, Wasserman BA (2016) MR imaging measures of intracranial atherosclerosis in a population-based study. Radiology 280:860–868PubMedPubMedCentralCrossRefGoogle Scholar
- 40.El Aidi H, Mani V, Weinshelbaum KB, Aguiar SH, Taniguchi H, Postley JE, Samber DD, Cohen EI, Stern J, van der Geest RJ, Reiber JH, Woodward M, Fuster V, Gidding SS, Fayad ZA (2009) Cross-sectional, prospective study of MRI reproducibility in the assessment of plaque burden of the carotid arteries and aorta. Nat Clin Pract Cardiovasc Med 6:219–228Google Scholar
- 41.Sun J, Zhao X-Q, Balu N, Hippe DS, Hatsukami TS, Isquith DA, Yamada K, Neradilek MB, Cantn G, Xue Y, Fleg JL, Desvigne-Nickens P, Klimas MT, Padley RJ, Vassileva MT, Wyman BT, Yuan C (2015) Carotid magnetic resonance imaging for monitoring atherosclerotic plaque progression: a multicenter reproducibility study. Int J Cardiovasc Imaging 31:95–103PubMedCrossRefGoogle Scholar
- 43.Balu N, Sun J, Hippe DS, Zhu D, Kim SE, Roberts J, De Marco JK, Parker DL, Salonder D, McConnell MV, Yuan C, Hatsukami TS (2014) Multiplatform reproducbility of 3D carotid vessel wall MRI. In: Proceedings of the Annual Meeting ISMRM, MilanGoogle Scholar
- 46.Truijman MTB, Kooi ME, van Dijk AC, de Rotte AAJ, van der Kolk AG, Liem MI, Schreuder FHBM, Boersma E, Mess WH, van Oostenbrugge RJ, Koudstaal PJ, Kappelle LJ, Nederkoorn PJ, Nederveen AJ, Hendrikse J, van der Steen AFW, Daemen MJAP, van der Lugt A (2014) Plaque at RISK (PARISK): prospective multicenter study to improve diagnosis of high-risk carotid plaques. Int J Stroke 9:747–754PubMedCrossRefGoogle Scholar
- 47.Saam T, Hetterich H, Hoffmann V, Yuan C, Dichgans M, Poppert H, Koeppel T, Hoffmann U, Reiser MF, Bamberg F (2013) Meta-analysis and systematic review of the predictive value of carotid plaque hemorrhage on cerebrovascular events by magnetic resonance imaging. J Am Coll Cardiol 62:1081–1091PubMedCrossRefGoogle Scholar
- 54.Raman SV, Winner MW, Tran T, Velayutham M, Simonetti OP, Baker PB, Olesik J, McCarthy B, Ferketich AK, Zweier JL (2008) In vivo atherosclerotic plaque characterization using magnetic susceptibility distinguishes symptom-producing plaques. JACC Cardiovasc Imaging 1:49–57PubMedPubMedCentralCrossRefGoogle Scholar
- 55.Kooi ME, Cappendijk VC, Cleutjens KBJM, Kessels AGH, Kitslaar PJEHM, Borgers M, Frederik PM, Daemen MJAP, Van Engelshoven JMA (2003) Accumulation of ultrasmall superparamagnetic particles of iron oxide in human atherosclerotic plaques can be detected by in vivo magnetic resonance imaging. Circulation 107:2453–2458PubMedCrossRefGoogle Scholar
- 56.Tang TY, Howarth SP, Miller SR, Graves MJ, Patterson AJ, JM UK-I, Li ZY, Walsh SR, Brown AP, Kirkpatrick PJ, Warburton EA, Hayes PD, Varty K, Boyle JR, Gaunt ME, Zalewski A, Gillard JH (2009) The ATHEROMA (Atorvastatin therapy: effects on reduction of macrophage activity) Study. Evaluation using ultrasmall superparamagnetic iron oxide-enhanced magnetic resonance imaging in carotid disease. J Am Coll Cardiol 53:2039–2050PubMedCrossRefGoogle Scholar
- 65.Koppal S, Warntjes M, Swann J, Dyverfeldt P, Kihlberg J, Moreno R, Magee D, Roberts N, Zachrisson H, Forssell C, Lnne T, Treanor D, de Muinck ED (2017) Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis. Magn Reson Med 78:285–296PubMedCrossRefGoogle Scholar
- 66.Chai JT, Biasiolli L, Li L, Alkhalil M, Galassi F, Darby C, Halliday AW, Hands L, Magee T, Perkins J, Sideso E, Handa A, Jezzard P, Robson MD, Choudhury RP (2017) Quantification of lipid-rich core in carotid atherosclerosis using magnetic resonance T2 mapping. JACC Cardiovasc Imaging 10:747–756PubMedPubMedCentralCrossRefGoogle Scholar
- 71.Kim SE, Jeong EK, Shi XF, Morrell G, Treiman GS, Parker DL (2009) Diffusion-weighted imaging of human carotid artery using 2D single-shot interleaved multislice inner volume diffusion-weighted echo planar imaging (2D ss-IMIV-DWEPI) at 3T: diffusion measurement in atherosclerotic plaque. J Magn Reson Imaging 30:1068–1077PubMedPubMedCentralCrossRefGoogle Scholar
- 77.Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, Larsson HB, Lee TY, Mayr NA, Parker GJ, Port RE, Taylor J, Weisskoff RM (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232PubMedCrossRefGoogle Scholar
- 79.Gaens ME, Backes WH, Rozel S, Lipperts M, Sanders SN, Jaspers K, Cleutjens JPM, Sluimer JC, Heeneman S, Daemen MJAP, Welten RJTJ, Daemen J-WH, Wildberger JE, Kwee RM, Kooi ME (2013) Dynamic contrast-enhanced MR imaging of carotid atherosclerotic plaque: model selection, reproducibility, and validation. Radiology 266:271–279PubMedCrossRefGoogle Scholar
- 84.Truijman MTB, Kwee RM, van Hoof RHM, Hermeling E, van Oostenbrugge RJ, Mess WH, Backes WH, Daemen MJ, Bucerius J, Wildberger JE, Kooi ME (2013) Combined 18F-FDG PET-CT and DCE-MRI to assess inflammation and microvascularization in atherosclerotic plaques. Stroke 44:3568–3570PubMedCrossRefGoogle Scholar
- 85.O’Brien KD, Hippe DS, Chen H, Neradilek MB, Probstfield JL, Peck S, Isquith DA, Canton G, Yuan C, Polissar NL, Zhao X, Kerwin WS (2016) Longer duration of statin therapy is associated with decreased carotid plaque vascularity by magnetic resonance imaging. Atherosclerosis 245:74–81PubMedCrossRefGoogle Scholar
- 86.Calcagno C, Lobatto ME, Dyvorne H, Robson PM, Millon A, Senders ML, Lairez O, Ramachandran S, Coolen BF, Black A, Mulder WJM, Fayad ZA (2015) Three-dimensional dynamic contrast-enhanced MRI for the accurate, extensive quantification of microvascular permeability in atherosclerotic plaques. NMR Biomed 28:1304–1314PubMedPubMedCentralCrossRefGoogle Scholar
- 87.Chen H, Sun J, Kerwin WS, Balu N, Neradilek MB, Hippe DS, Isquith D, Xue Y, Yamada K, Peck S, Yuan C, O’Brien KD, Zhao X-Q (2014) Scan-rescan reproducibility of quantitative assessment of inflammatory carotid atherosclerotic plaque using dynamic contrast-enhanced 3T CMR in a multi-center study. J Cardiovasc Magn Reson 16:51PubMedPubMedCentralCrossRefGoogle Scholar
- 88.van Hoof RHM, Vöö SA, Sluimer JC, Wijnen NJA, Hermeling E, Schreuder FHBM, Truijman MT, Cleutjens JPM, Daemen MJAP, Daemen JH, van Oostenbrugge RJ, Mess WH, Wildberger JE, Heeneman S, Kooi ME (2017) Vessel wall and adventitial DCE-MRI parameters demonstrate similar correlations with carotid plaque microvasculature on histology. J Magn Reson Imaging. doi: 10.1002/jmri.25648 PubMedGoogle Scholar
- 90.O’Brien KD, Hippe DS, Chen H, Neradilek MB, Probstfield JL, Peck S, Isquith DA, Canton G, Yuan C, Polissar NL, Zhao X, Kerwin WS (2016) Summary of clinical and laboratory data of study subjects with and without DCE-MRI plaque measurements in the AIM-HIGH clinical trial. Data Brief 6:476–481PubMedPubMedCentralCrossRefGoogle Scholar
- 91.Wan T, Madabhushi A, Phinikaridou A, Hamilton JA, Hua N, Pham T, Danagoulian J, Kleiman R, Buckler AJ (2014) Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model. Med Phys 41:42303CrossRefGoogle Scholar
- 92.Calcagno C, Cornily J-C, Hyafil F, Rudd JHF, Briley-Saebo KC, Mani V, Goldschlager G, Machac J, Fuster V, Fayad ZA (2008) Detection of neovessels in atherosclerotic plaques of rabbits using dynamic contrast enhanced MRI and 18F-FDG PET. Arterioscler Thromb Vasc Biol 28:1311–1317PubMedPubMedCentralCrossRefGoogle Scholar
- 94.Vucic E, Calcagno C, Dickson SD, Rudd JHF, Hayashi K, Bucerius J, Moshier E, Mounessa JS, Roytman M, Moon MJ, Lin J, Ramachandran S, Tanimoto T, Brown K, Kotsuma M, Tsimikas S, Fisher EA, Nicolay K, Fuster V, Fayad ZA (2012) Regression of inflammation in atherosclerosis by the LXR agonist R211945. JACC Cardiovasc Imaging 5:819–828PubMedPubMedCentralCrossRefGoogle Scholar
- 95.Vucic E, Dickson SD, Calcagno C, Rudd JHF, Moshier E, Hayashi K, Mounessa JS, Roytman M, Moon MJ, Lin J, Tsimikas S, Fisher EA, Nicolay K, Fuster V, Fayad ZA (2011) Pioglitazone modulates vascular inflammation in atherosclerotic rabbits. JACC Cardiovasc Imaging 4:1100–1109PubMedPubMedCentralCrossRefGoogle Scholar
- 96.Lobatto ME, Fayad ZA, Silvera S, Vucic E, Calcagno C, Mani V, Dickson SD, Nicolay K, Banciu M, Schiffelers RM, Metselaar JM, van Bloois L, Wu H-S, Fallon JT, Rudd JH, Fuster V, Fisher EA, Storm G, Mulder WJM (2010) Multimodal clinical imaging to longitudinally assess a nanomedical anti-inflammatory treatment in experimental atherosclerosis. Mol Pharm 7:2020–2029PubMedPubMedCentralCrossRefGoogle Scholar
- 99.Calcagno C, Ramachandran S, Izquierdo-Garcia D, Mani V, Millon A, Rosenbaum D, Tawakol A, Woodward M, Bucerius J, Moshier E, Godbold J, Kallend D, Farkouh ME, Fuster V, Rudd JHF, Fayad ZA (2013) The complementary roles of dynamic contrast-enhanced MRI and 18F-fluorodeoxyglucose PET/CT for imaging of carotid atherosclerosis. Eur J Nucl Med Mol Imaging 40:1884–1893PubMedCrossRefGoogle Scholar
- 100.Taqueti VR, Di Carli MF, Jerosch-Herold M, Sukhova GK, Murthy VL, Folco EJ, Kwong RY, Ozaki CK, Belkin M, Nahrendorf M, Weissleder R, Libby P (2014) Increased microvascularization and vessel permeability associate with active inflammation in human atheromata. Circ Cardiovasc Imaging 7:920–929PubMedPubMedCentralCrossRefGoogle Scholar
- 101.Kim Y, Lobatto ME, Kawahara T, Lee Chung B, Mieszawska AJ, Sanchez-Gaytan BL, Fay F, Senders ML, Calcagno C, Becraft J, Tun Saung M, Gordon RE, Stroes ESG, Ma M, Farokhzad OC, Fayad ZA, Mulder WJM, Langer R (2014) Probing nanoparticle translocation across the permeable endothelium in experimental atherosclerosis. Proc Natl Acad Sci 111:1078–1083PubMedPubMedCentralCrossRefGoogle Scholar
- 102.Lobatto ME, Calcagno C, Millon A, Senders ML, Fay F, Robson PM, Ramachandran S, Binderup T, Paridaans MPM, Sensarn S, Rogalla S, Gordon RE, Cardoso L, Storm G, Metselaar JM, Contag CH, Stroes ESG, Fayad ZA, Mulder WJM (2015) Atherosclerotic plaque targeting mechanism of long-circulating nanoparticles established by multimodal imaging. ACS Nano 9:1837–1847PubMedPubMedCentralCrossRefGoogle Scholar
- 103.Kim Y, Lobatto ME, Kawahara T, Lee Chung B, Mieszawska AJ, Sanchez-Gaytan BL, Fay F, Senders ML, Calcagno C, Becraft J, Tun Saung M, Gordon RE, Stroes ESG, Ma M, Farokhzad OC, Fayad ZA, Mulder WJM, Langer R (2014) Probing nanoparticle translocation across the permeable endothelium in experimental atherosclerosis. Proc Natl Acad Sci 111:1078–1083PubMedPubMedCentralCrossRefGoogle Scholar
- 104.Calcagno C, Robson PM, Ramachandran S, Mani V, Kotys-Traughber M, Cham M, Fischer SE, Fayad ZA (2013) SHILO, a novel dual imaging approach for simultaneous HI-/LOw temporal (Low-/Hi-spatial) resolution imaging for vascular dynamic contrast enhanced cardiovascular magnetic resonance: numerical simulations and feasibility in the carotid arteries. J Cardiovasc Magn Reson 15:42PubMedPubMedCentralCrossRefGoogle Scholar
- 107.van Hoof RHM, Hermeling E, Truijman MTB, van Oostenbrugge RJ, Daemen JWH, van der Geest RJ, van Orshoven NP, Schreuder AH, Backes WH, Daemen MJAP, Wildberger JE, Kooi ME (2015) Phase-based vascular input function: improved quantitative DCE-MRI of atherosclerotic plaques. Med Phys 42:4619–4628PubMedCrossRefGoogle Scholar
- 108.Tang J, Lobatto ME, Hassing L, van der Staay S, van Rijs SM, Calcagno C, Braza MS, Baxter S, Fay F, Sanchez-Gaytan BL, Duivenvoorden R, Sager HB, Astudillo YM, Leong W, Ramachandran S, Storm G, Perez-Medina C, Reiner T, Cormode DP, Strijkers GJ, Stroes ESG, Swirski FK, Nahrendorf M, Fisher EA, Fayad ZA, Mulder WJM (2015) Inhibiting macrophage proliferation suppresses atherosclerotic plaque inflammation. Sci Adv 1:e1400223PubMedPubMedCentralCrossRefGoogle Scholar
- 113.Lobbes MBI, Heeneman S, Passos VL, Welten R, Kwee RM, van der Geest RJ, Wiethoff AJ, Caravan P, Misselwitz B, Daemen MJAP, van Engelshoven JMA, Leiner T, Kooi ME (2010) Gadofosveset-enhanced magnetic resonance imaging of human carotid atherosclerotic plaques: a proof-of-concept study. Invest Radiol 45:275–281PubMedCrossRefGoogle Scholar
- 117.Phinikaridou A, Andia ME, Lavin B, Smith A, Saha P, Botnar RM (2016) Increased vascular permeability measured with an albumin-binding magnetic resonance contrast agent is a surrogate marker of rupture-prone atherosclerotic plaque. Circ Cardiovasc Imaging 9:e004910PubMedPubMedCentralCrossRefGoogle Scholar
- 124.Oyre S, Ringgaard S, Kozerke S, Paaske WP, Scheidegger MB, Boesiger P, Pedersen EM (1998) Quantitation of circumferential subpixel vessel wall position and wall shear stress by multiple sectored three-dimensional paraboloid modeling of velocity encoded cine MR. Magn Reson Med 40:645–655PubMedCrossRefGoogle Scholar
- 128.Duivenvoorden R, VanBavel E, de Groot E, Stroes ESG, Disselhorst JA, Hutten BA, Lameris JS, Kastelein JJP, Nederveen AJ (2010) Endothelial shear stress: a critical determinant of arterial remodeling and arterial stiffness in humans—a carotid 3.0-T MRI study. Circ Cardiovasc Imaging 3:578–585PubMedCrossRefGoogle Scholar
- 130.Markl M, Wegent F, Zech T, Bauer S, Strecker C, Schumacher M, Weiller C, Hennig J, Harloff A (2010) In vivo wall shear stress distribution in the carotid artery: effect of bifurcation geometry, internal carotid artery stenosis, and recanalization therapy. Circ Cardiovasc Imaging 3:647–655PubMedCrossRefGoogle Scholar
- 133.Bissell MM, Hess AT, Biasiolli L, Glaze SJ, Loudon M, Pitcher A, Davis A, Prendergast B, Markl M, Barker AJ, Neubauer S, Myerson SG (2013) Aortic dilation in bicuspid aortic valve disease: flow pattern is a major contributor and differs with valve fusion type. Circ Cardiovasc Imaging 6:499–507PubMedCrossRefGoogle Scholar
- 142.Cibis M, Potters WV, Gijsen FJ, Marquering H, vanBavel E, van der Steen AF, Nederveen AJ, Wentzel JJ (2014) Wall shear stress calculations based on 3D cine phase contrast MRI and computational fluid dynamics: a comparison study in healthy carotid arteries. NMR Biomed 27:826–834PubMedCrossRefGoogle Scholar
- 146.Ben-Shlomo Y, Spears M, Boustred C, May M, Anderson SG, Benjamin EJ, Boutouyrie P, Cameron J, Chen CH, Cruickshank JK, Hwang SJ, Lakatta EG, Laurent S, Maldonado J, Mitchell GF, Najjar SS, Newman AB, Ohishi M, Pannier B, Pereira T, Vasan RS, Shokawa T, Sutton-Tyrell K, Verbeke F, Wang KL, Webb DJ, Willum Hansen T, Zoungas S, McEniery CM, Cockcroft JR, Wilkinson IB (2014) Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects. J Am Coll Cardiol 63:636–646PubMedCrossRefGoogle Scholar
- 150.Bargiotas I, Mousseaux E, Yu W-C, Venkatesh BA, Bollache E, de Cesare A, Lima JAC, Redheuil A, Kachenoura N (2015) Estimation of aortic pulse wave transit time in cardiovascular magnetic resonance using complex wavelet cross-spectrum analysis. J Cardiovasc Magn Reson 17:65PubMedPubMedCentralCrossRefGoogle Scholar
- 152.Westenberg JJM, de Roos A, Grotenhuis HB, Steendijk P, Hendriksen D, van den Boogaard PJ, van der Geest RJ, Bax JJ, Jukema JW, Reiber JHC (2010) Improved aortic pulse wave velocity assessment from multislice two-directional in-plane velocity-encoded magnetic resonance imaging. J Magn Reson Imaging 32:1086–1094PubMedCrossRefGoogle Scholar
- 156.Markl M, Wallis W, Strecker C, Gladstone BP, Vach W, Harloff A (2012) Analysis of pulse wave velocity in the thoracic aorta by flow-sensitive four-dimensional MRI: reproducibility and correlation with characteristics in patients with aortic atherosclerosis. J Magn Reson Imaging 35:1162–1168PubMedCrossRefGoogle Scholar
- 157.Mandell DM, Mossa-Basha M, Qiao Y, Hess CP, Hui F, Matouk C, Johnson MH, Daemen MJAP, Vossough A, Edjlali M, Saloner D, Ansari SA, Wasserman BA, Mikulis DJ (2017) Intracranial vessel wall MRI: principles and expert consensus recommendations of the American society of neuroradiology. Am J Neuroradiol 38:218–229PubMedCrossRefGoogle Scholar
- 158.Dyverfeldt P, Bissell M, Barker AJ, Bolger AF, Carlhäll C-J, Ebbers T, Francios CJ, Frydrychowicz A, Geiger J, Giese D, Hope MD, Kilner PJ, Kozerke S, Myerson S, Neubauer S, Wieben O, Markl M (2015) 4D flow cardiovascular magnetic resonance consensus statement. J Cardiovasc Magn Reson 17:72PubMedPubMedCentralCrossRefGoogle Scholar
- 164.Duivenvoorden R, Tang J, Cormode DP, Mieszawska AJ, Izquierdo-Garcia D, Ozcan C, Otten MJ, Zaidi N, Lobatto ME, van Rijs SM, Priem B, Kuan EL, Martel C, Hewing B, Sager H, Nahrendorf M, Randolph GJ, Stroes ESG, Fuster V, Fisher EA, Fayad ZA, Mulder WJM (2014) A statin-loaded reconstituted high-density lipoprotein nanoparticle inhibits atherosclerotic plaque inflammation. Nat Commun 5:3065PubMedPubMedCentralGoogle Scholar
- 166.Hess AT, Bissell MM, Ntusi NAB, Lewis AJM, Tunnicliffe EM, Greiser A, Stalder AF, Francis JM, Myerson SG, Neubauer S, Robson MD (2015) Aortic 4D flow: quantification of signal-to-noise ratio as a function of field strength and contrast enhancement for 1.5, 3, and 7T. Magn Reson Med 73:1864–1871PubMedCrossRefGoogle Scholar
- 169.de Rotte AAJ, Koning W, Truijman MTB, den Hartog AG, Bovens SM, Vink A, Sepehrkhouy S, Zwanenburg JJM, Klomp DWJ, Pasterkamp G, Moll FL, Luijten PR, Hendrikse J, de Borst GJ (2014) Seven-Tesla magnetic resonance imaging of atherosclerotic plaque in the significantly stenosed carotid artery. Invest Radiol 49:749–757PubMedCrossRefGoogle Scholar
- 170.Kröner ESJ, van Schinkel LD, Versluis MJ, Brouwer NJ, van den Boogaard PJ, van der Wall EE, de Roos A, Webb AG, Siebelink H-MJ, Lamb HJ (2012) Ultrahigh-field 7-T magnetic resonance carotid vessel wall imaging: initial experience in comparison with 3-T field strength. Invest Radiol 47:697–704PubMedCrossRefGoogle Scholar
- 171.Calcagno C, Coolen BF, Zhang B, Boeykens G, Robson PM, Mani V, Nederveen AJ, Mulder WJM, Fayad ZA (2016) Optimization of 3D high resolution T2 weighted SPACE for carotid vessel wall imaging on a 7T whole-body clinical scanner. Proc Annu Meet ISMRM 968Google Scholar
- 172.Koning W, Bluemink JJ, Langenhuizen EAJ, Raaijmakers AJ, Andreychenko A, van den Berg CAT, Luijten PR, Zwanenburg JJM, Klomp DWJ (2013) High-resolution MRI of the carotid arteries using a leaky waveguide transmitter and a high-density receive array at 7T. Magn Reson Med 69:1186–1193PubMedCrossRefGoogle Scholar
- 173.Thalhammer C, Renz W, Winter L, Hezel F, Rieger J, Pfeiffer H, Graessl A, Seifert F, Hoffmann W, von Knobelsdorff-Brenkenhoff F, Tkachenko V, Schulz-Menger J, Kellman P, Niendorf T (2012) Two-dimensional sixteen channel transmit/receive coil array for cardiac MRI at 7.0 T: design, evaluation, and application. J Magn Reson Imaging 36:847–857PubMedPubMedCentralCrossRefGoogle Scholar
- 181.Schnell S, Markl M, Entezari P, Mahadewia RJ, Semaan E, Stankovic Z, Collins J, Carr J, Jung B (2014) k-t GRAPPA accelerated four-dimensional flow MRI in the aorta: effect on scan time, image quality, and quantification of flow and wall shear stress. Magn Reson Med 72:522–533PubMedCrossRefGoogle Scholar
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