We have developed an accurate, non-invasive, and easy-to-use method to quantify endothelial permeability and dysfunction in the arteries using Magnetic Resonance Imaging (MRI), named qMETRIC. This technique enables assessing vascular damage and cardiovascular risk associated with atherosclerosis in preclinical models and humans.
Cardiovascular diseases are the leading causes of death worldwide. A permeable/leaky and dysfunctional endothelium is considered the earliest marker of vascular damage and thought to drive atherosclerosis. A method to identify these changes in vivo would be desirable in the clinic. Magnetic resonance imaging (MRI)-based tools and other technologies have enabled a profound understanding of the role of the endothelium in cardiovascular diseases and risk in vivo. There is, however, a need for reproducible and simple approaches for extracting quantifiable data reflective of endothelial damage from a single imaging study. A non-invasive, easy-to-implement, and quantitative MRI workflow was developed to acquire and analyze images that allow the quantification of two imaging biomarkers of arterial endothelial damage (leakiness/permeability and dysfunction). Here, the protocol describes the application of this method in the brachiocephalic artery of atherosclerotic ApoE-/- mice using a clinical MRI scanner. First, late gadolinium enhancement (LGE) and Modified Look-Locker Inversion Recovery (MOLLI) T1 mapping protocols to quantify endothelial leakage using an albumin-binding probe are described. Second, anatomic, and quantitative blood flow sequences to measure endothelial dysfunction, in response to acetylcholine are described. Importantly, the method outlined here allows the acquisition of high-spatial-resolution 3D images with large volumetric coverage enabling accurate segmentation of vessel wall structures to improve inter- and intra-observer variability and to increase reliability and reproducibility. Additionally, it provides quantitative data without the need for high-temporal resolution for complex kinetic modeling, making it model-independent and even allowing for imaging of highly mobile vessels (coronary arteries). Therefore, the approach simplifies and expedites data analysis. Finally, this method can be implemented on different scanners, can be extended to image different arterial beds, and is clinically applicable for use in humans. This method could be used to diagnose and treat patients with atherosclerosis by adopting a precision-medicine approach.
Cardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity worldwide, accounting for nearly one-third of deaths1, and the cause of lifelong disabilities that exert a high financial cost on the healthcare systems1. Among CVDs, ischaemic heart disease and stroke are primarily caused by atherosclerotic plaques. Atherosclerosis is a multifactorial disease; however, a common hallmark is early damage of the vascular endothelial cells that lead to the formation, progression, and eventual complications of atherosclerosis. An intact vascular endothelium has fundamental vasculo-protective properties2. The endothelium regulates vascular permeability by controlling translocation of cells and molecules between the systemic circulation and the vessel wall; controls vascular tone by balancing the production of vasodilators (e.g., nitric oxide, prostacyclin) and vasoconstrictors (e.g., endothelin-1, angiotensin II); and also has anti-coagulant properties. However, both the function and permeability of the endothelial cells can deteriorate in the presence of cardiovascular risk factors (e.g., smoking, high cholesterol, diabetes, systemic inflammation, oxidative stress) and by blood flow hemodynamic patterns. A dysfunctional endothelium has reduced vasodilation in response to stressors, consequently increasing arterial stiffness. In addition, a permeable/leaky endothelium has widened tight gap junctions between adjacent cells3,4,5,6,7. Such change occurs both on the luminal endothelium and newly-formed plaque microvessels that appear fragile, leaky, and dysmorphic8. Permeable endothelial cells act as entry points for plasma-borne molecules and cells-exacerbating the risk of cardiovascular disease.
Building on this knowledge, in the past 15 years, endothelial permeability and function has emerged as a promising imaging and therapeutic target to better diagnose subjects at risk for cardiovascular disease and to assess the effects of known or novel drugs. However, direct and quantitative imaging of endothelium function is limited9,10,11,12. Currently, much of the interpretation of endothelial function in vivo is based on studies of endothelial-dependent dilation (FMD) in peripheral vessels whose function modestly correlates with atherosclerosis burden in vascular beds that cause clinical events13,14,15. Only a limited number of imaging studies have shown a direct link between endothelial dysfunction and atherosclerosis burden in vivo9,10,11,12. Conversely, more accessible MRI-based approaches have enabled imaging endothelial permeability more widely. Using the percent vessel wall signal enhancement after administration of MRI gadolinium agents has provided a semi-quantitative measurement of endothelial permeability16,17. Later, the development of dynamic contrast-enhanced (DCE) protocols has permitted an improved and more quantitative measurement of vascular endothelial permeability. Quantitative parameters such as the contrast extravasation rate (Ktrans) and microvascular volume (Vρ) derived from kinetic modeling or the area under the curve (AUC), upslope, time to peak, and peak concentration extracted from non-modeled methods correlated not only with endothelial permeability but also plaque vascularity18,19,20. However, the application of vascular DCE remains challenging despite significant technical advances because: (i) it requires both high spatial (0.5-0.7 mm2) and temporal resolution21 for accurate delineation of the vessel wall. Sampling the concentration of contrast agent in the blood to calculate the arterial input function also requires kinetic modeling, which leads to a trade-off of either limiting anatomical coverage22,23 to gain temporal resolution or vice versa24,25; (ii) data analysis may require complex pharmacokinetic modeling (e.g., Patlak vs. Tofts); (iii) provides limited image quality, poor scan-rescan reproducibility, and average inter-observer and intra-observer variability26,27. Therefore, there is still a need for reproducible and simple approaches for extracting direct and quantifiable data of endothelial permeability and (dys)function from single imaging studies that could have better clinical utility.
Here, we have developed a non-invasive, easy-to-implement, and quantitative MRI to acquire and analyze images that allows direct quantification of two markers of arterial endothelial damage (leakiness/permeability and dysfunction) using preclinical models of atherosclerosis in a single scan. The method is named Quantitative MRI of EndoThelial peRmeabIlity and dysfunCtion (qMETRIC). It involves the acquisition of late gadolinium enhancement (LGE) and Modified Look-Locker Inversion Recovery (MOLLI) T1 mapping protocols to quantify endothelial leakage, after administration of an intravascular albumin-binding probe; and acquisition of anatomic and quantitative blood flow sequences to measure endothelial dysfunction, in response to an acetylcholine bolus. We have demonstrated that qMETRIC accurately detects: the severity of atherosclerosis and the risk of complications; treatment responses; and can be adapted for use in patients5,6,7. Importantly, the method outlined here allows the acquisition of high-spatial-resolution images to enable accurate segmentation of the vessel wall to minimize inter/intra-observer bias and to increase reliability and reproducibility with large anatomical coverage. Finally, this method can be adapted for use on different scanners and can be extended to image different arterial beds (even coronary arteries28). The straightforward workflow makes this approach more accessible to the cardiovascular imaging community.
Determining vascular endothelial health is an attractive imaging biomarker that can potentially be used to diagnose atherosclerotic-related risk and to monitor treatment effects. The qMETRIC protocol outlined here can be used to reproducibly quantitate endothelial permeability/leakiness and (dys)function in a comprehensive, fast, and clinically applicable MRI protocol. Such an approach can provide a simpler alternative or complementary tool to existing DCE-MRI protocols for quantifying endothelial permeability. It can also provide a non-invasive tool for direct assessment of endothelial (dys)function in vascular beds, such as the coronary and carotid arteries, instead of using either invasive techniques or surrogate measurements in peripheral arteries that are less severely affected by the disease. Measuring endothelial permeability using this method allows coverage of the aorta, the aortic arch, and the brachiocephalic and carotid arteries at high spatial resolution (0.1 mm for the LGE images and 0.22 mm for T1 mapping) that is crucial for accurate segmentation of the vessel wall in rodents. Analysis of the images can be carried out using an open-source platform and requires only a simple segmentation of the vessel wall without the need for complex pharmacokinetic modeling. Importantly, this protocol can be adapted to be used in a number of different commercially available scanners and can be extended to be used in different animal models and also humans. Although this protocol describes the methodology using a clinical scanner setup, the MRI protocols can also be implemented when using high-field small animal scanners. These scanners frequently offer inversion recovery, T1 mapping, and angiography protocols that can be used or can be programmed in collaboration with the scanner manufacturers.
To obtain accurate and reproducible results, particular attention should be paid to some critical steps of the protocol. Firstly, when imaging small animals in a clinical scanner, suitable and custom-made receiver coils are necessary to maximize the signal-to-noise ratio for high image quality. The animal positioning on the coil is also crucial, avoiding separation and air-filled spaces between the animal and the coil to improve the signal-to-noise ratio. For this reason, the anatomical area of interest should be placed in the center of the coil, and then moved to the isocenter of the magnet to expose them to the magnetic field with maximum homogeneity. Secondly, a stable, strong, and accurate ECG signal is paramount for reliable imaging triggering/gating. This is important for consistent excitation of the magnetization and the timing of the image acquisition window at specific time points and for acquiring accurate time-resolved images that include the end-diastolic phase for the functional test. Small animal pad-based or needle-based electrodes are more suitable options when used at higher-field strength scanners, which are better shielded compared to clinical scanners. When these options are used at clinical field scanners, the ECG cables need to be warped together to avoid the formation of resonant circuits at the MRI Lamour frequency that may deteriorate the ECG signal during the pulse sequence. Alternatively, we propose the use of the ECG module and pads used for human scans with adjustment of the pad size to that of the mouse paw and extra stabilization of the pads with tape to improve conductivity. Thirdly, when acquiring LGE images while the contrast agent is still circulating in the bloodstream, it is crucial to choose the correct nulling time to efficiently suppress the blood pool to delineate the vessel wall. A Look-locker sequence must be run before every LGE sequence, and the inversion delay time needs to be adjusted accordingly. Fourthly, for accurate and precise T1 mapping using a modified look-locker inversion recovery (MOLLI) sequence, the proposed image acquisition scheme should be implemented to cover a range of inversion delays ranging at least from 20 ms to 2000 ms to capture the short and long T1 species. Lastly, segmentation of MRI data must be rigorous and strict criteria applied to avoid intra and/or inter-observer biases in the area/volume and T1 value calculations.
Unlike DCE-MRI, the procedure described here does not provide kinetic data of the wash-in and wash-out of the contrast agent in the vessel wall. Rather, it provides a snapshot of endothelial permeability at a specific time point after injection of the albumin-binding contrast agent, gadofosveset. However, the extracted quantitative data from these time-points highly correlated with other albumin-dyes, such as Evan’s blue dye, which is considered a gold-standard to measure endothelial permeability and increased endothelial gap-junction width. Mechanistically, both the albumin-bound and unbound-fraction of gadofosveset are small enough to pass through breaks in the endothelial junctions and lead to MRI signal enhancement. Additionally, it is possible that the unbound-fraction may also bind to intraplaque albumin after it enters the vessel wall and results in signal enhancement. It was observed that the relaxivity of the vessel wall is r1≈17 mmol/L/s, when gadofosveset is injected at a clinical dose. This value is closer to that reported for the albumin-bound fraction (r1≈25 mmol/L/s) compared to the free-fraction (r1≈6.6 mmol/L/s)5,29.
Future applications of this imaging method include basic science studies in different animal models and other arterial segments and the use of this method to assess for biological responses to existing or novel pharmaceutical agents. Studies can be performed either cross-sectionally or longitudinally to gather mechanistic and outcome data, respectively. The straightforward workflow makes this approach accessible and clinically applicable for use in humans also. Adaptation of this method for imaging human carotid and peripheral arteries is more imminent, but the application of this method for imaging the coronary arteries requires further advancements in image acquisition, reconstruction, and motion-correction that are currently being developed30,31.
The authors have nothing to disclose.
We are grateful for funding to the: (1) British Heart Foundation (A.P Early Career Development Fellowship, Project grant-PG/2019/34897, and R.M.B. Project and Programme grants PG/10/044/28343, RG/12/1/29262 and RG/20/1/34802); (2) the King's BHF Centre for Research Excellence RE/18/2/34213; (3) the Wellcome EPSRC Centre for Medical Engineering (NS/A000049/1); (4) the Department of Health via the National Institute for Health Research (NIHR) Cardiovascular Health Technology Cooperative (HTC) and comprehensive Biomedical Research Centre awarded to Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust; (5) Chilean Agency for Research and Development (ANID) – Millennium Science Initiative Program – NCN17_129 and FONDECYT 1180525.
Acetylcholine | Sigma Aldrich | A6625- 100G, 16.6 mg/kg | |
Anesthesia equipment | General Anesthetic Services | General Anesthetic Services | |
Circulating heating pump | ThermoFisher Scientific, USA | BOM: 152510101 | |
ECG conductive gel (Nuprep) | Waever and Company, USA | 10-30-T | |
ECG monitoring module | Invivo, USA | REF 0700-1002 | |
Gadofosveset trisordium (Vasovist/ Ablavar) | Lantheus Medical Imaging Inc, North Billerica, MA, USA | 0.03 mmol/kg | |
High fat diet | Special Diets Services, Witham, UK | 21% fat from lard, 0.15% (wt/wt) cholesterol | |
Induction box | Vet Tech Solutions LTD | ||
Insulin syringes | BD Biosciences | 0.5 mL, 29 G | |
OsirixX software | OsiriX Foundation, Geneva, Switzerland | Open-source platform | |
Philips Achieva MRI Scanner (3 Tesla) | Philips Healthcare, Best, The Netherlands | Equipped with a clinical gradient system (30 mT m-1, 200 mT m-1 ms-1) | |
Single–loop surface microscopy receiver coil | Phillips Hamburg | Diameter = 23 mm | Custom built |
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