Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

Quantitative Magnetic Resonance Imaging Assessment of the Relationships Between Fat Fraction and R2* Inside Carotid Plaques, and Circulating Lipoproteins

BACKGROUND

Lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) are morphological features of high-risk atherosclerotic plaques. However, their relationship to circulating lipoproteins is unclear.

PURPOSE

To study associations between changes in lipoproteins vs. changes in LRNC (represented by fat fraction [FF]) and IPH (represented by R2*).

STUDY TYPE

Prospective.

SUBJECTS

Fifty-two patients with carotid plaques, 33 males (63.5%), mean age 72 (±5).

FIELD STRENGTH/SEQUENCE

Four-point fast gradient Dixon magnetic resonance imaging (MRI) was used to quantify FF and R2* (to measure IPH) inside plaques and in vessel wall. Turbo-spin echo was used for T₁ weighted sequences to guide manual segmentation.

ASSESSMENT

Carotid MRI and serum lipid levels were assessed at baseline and at 1-year follow-up. For patients, lipid-lowering therapy was customized to reduce low-density lipoprotein (LDL) levels below 1.8 mmol/L. Segmentation was performed with one set of regions of interest for the plaque and one for the vessel wall at the location of the plaque. Thereby MRI data for FF, R2*, and volumes in plaque- and vessel-wall segmentations could be obtained from baseline and follow-up, as well as changes over the study year.

STATISTICAL TESTS

Pearson correlation coefficient for correlations. Paired samples t-test for changes over time. Significance at P < 0.05, 95% confidence interval.

RESULTS

LDL decreased significantly (2.19-1.88 mmol/L, Z - 2.9), without correlation to changes in plaque composition, nor to the significant reduction in vessel-wall volume (-106.3 mm³ ). Plaque composition remained unchanged, FF +8.5% (P = 0.366) and R2* +3.5% (P = 0.304). Compared to plaque segmentations, R2* was significantly lower in the vessel-wall segmentations both at baseline (-9.3%) and at follow-up (-9.1%).

DATA CONCLUSION

The absence of correlations between changes in lipoproteins and changes in plaque composition indicates more complex relationships between these parameters than previously anticipated. The significant differences in both R2* and volume dynamics comparing plaque segmentations and vessel-wall segmentations suggest differences in their pathobiology of atherosclerosis.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 4.

18Fluorodeoxyglucose uptake in relation to fat fraction and R2* in atherosclerotic plaques, using PET/MRI: a pilot study

Inflammation inside Atherosclerotic plaques represents a major pathophysiological process driving plaques towards rupture. Pre-clinical studies suggest a relationship between lipid rich necrotic core, intraplaque hemorrhage and inflammation, not previously explored in patients. Therefore, we designed a pilot study to investigate the feasibility of assessing the relationship between these plaque features in a quantitative manner using PET/MRI. In 12 patients with high-grade carotid stenosis the extent of lipid rich necrotic core and intraplaque hemorrhage was quantified from fat and R2* maps acquired with a previously validated 4-point Dixon MRI sequence in a stand-alone MRI. PET/MRI was used to measure ¹⁸F-FDG uptake. T1-weighted images from both scanners were used for registration of the quantitative Dixon data with the PET images. The plaques were heterogenous with respect to their volumes and composition. The mean values for the group were as follows: fat fraction (FF) 0.17% (± 0.07), R2* 47.6 s⁻¹ (± 10.9) and target-to-blood pool ratio (TBR) 1.49 (± 0.48). At group level the correlation between TBR and FF_mean was − 0.406, p 0.19 and for TBR and R2*_mean 0.259, p 0.42. The lack of correlation persisted when analysed on a patient-by-patient basis but the study was not powered to draw definitive conclusions. We show the feasibility of analysing the quantitative relationship between lipid rich necrotic cores, intraplaque haemorrhage and plaque inflammation. The ¹⁸F-FDG uptake for most patients was low. This may reflect the biological complexity of the plaques and technical aspects inherent to ¹⁸F-FDG measurements.

Trial registration: ISRCTN, ISRCTN30673005. Registered 05 January 2021, retrospectively registered.

Improvement in the contrast-to-noise ratio and quantitative measurement of T1 and T2* values for carotid atherosclerotic plaque using multi-echo phase-sensitive inversion recovery

Quantitative magnetic resonance imaging is required to accurately evaluate carotid plaque vulnerability. We prospectively evaluated the potential for fast quantitative black-blood carotid vessel wall imaging using a three-dimensional (3D) multi-echo phase-sensitive inversion recovery (mPSIR) sequence. Forty-nine patients with carotid atherosclerotic plaques were examined. Two-dimensional (2D) turbo spin-echo (TSE), 3D volumetric isotropic turbo spin-echo, and 3D mPSIR imaging were performed. The contrast-to-noise ratios (CNRs) between the carotid plaque and adjacent muscle were compared for the three imaging methods. The T₁ and T₂* values of the carotid plaques were measured using 3D mPSIR images. These values were compared with those of symptomatic and asymptomatic plaques. For carotid plaques with a signal intensity ratio ≥ 1.55, between the carotid plaque and adjacent muscle in 2D TSE images, the CNR of the mPSIR images was significantly higher than that of the other sequences. T₁ values for symptomatic and asymptomatic plaques were 544.0 ± 258.0 and 569.1 ± 301.7, respectively. T₂* values for symptomatic and asymptomatic plaques were 34.0 ± 33.0 and 21.8 ± 20.3 ms, respectively. There were no significant differences in the T₁ and T₂* values between the symptomatic and asymptomatic plaques. 3D mPSIR improves the CNR of T₁-weighted images for carotid plaques and the adjacent muscle while simultaneously providing the T₁ and T₂* values of the carotid plaque. This improved CNR may be useful for assessing the vulnerability of carotid plaques.

Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic

Background

Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to validate and demonstrate a method for segmenting the carotid bifurcation into the common, internal, and external carotid arteries (CCA, ICA, ECA) in contrast-enhanced MR angiography (CE-MRA) data.

Methods

A segmentation pipeline utilizing a convolutional neural network (DeepMedic) was tailored and trained for multi-class segmentation of the carotid arteries in CE-MRA data from the Swedish CardioPulmonsary bioImage Study (SCAPIS). Segmentation quality was quantitatively assessed using the Dice similarity coefficient (DSC), Matthews Correlation Coefficient (MCC), F₂, F_0.5, and True Positive Ratio (TPR). Segmentations were also assessed qualitatively, by three observers using visual inspection. Finally, geometric descriptions of the carotid bifurcations were generated for each subject to demonstrate the utility of the proposed segmentation method.

Results

Branch-level segmentations scored DSC = 0.80 ± 0.13, MCC = 0.80 ± 0.12, F₂ = 0.82 ± 0.14, F_0.5 = 0.78 ± 0.13, and TPR = 0.84 ± 0.16, on average in a testing cohort of 46 carotid bifurcations. Qualitatively, 61% of segmentations were judged to be usable for analyses without adjustments in a cohort of 336 carotid bifurcations without ground-truth. Carotid artery geometry showed wide variation within the whole cohort, with CCA diameter 8.6 ± 1.1 mm, ICA 7.5 ± 1.4 mm, ECA 5.7 ± 1.0 mm and bifurcation angle 41 ± 21°.

Conclusion

The proposed segmentation method automatically generates branch-level segmentations of the carotid arteries that are suitable for use in further analyses and help enable large-cohort investigations.

Towards Automated Quantification of Vessel Wall Composition Using MRI

BACKGROUND

MRI can be used to generate fat fraction (FF) and R2* data, which have been previously shown to characterize the plaque compositional features lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in the carotid arteries (CAs). Previously, these data were extracted from CA plaques using time-consuming manual analyses.

PURPOSE

To design and demonstrate a method for segmenting the CA and extracting data describing the composition of the vessel wall.

STUDY TYPE

Prospective.

SUBJECTS

31 subjects from the Swedish CArdioPulmonary bioImage Study (SCAPIS).

FIELD STRENGTH/SEQUENCES

T₁ -weighted (T₁ W) quadruple inversion recovery, contrast-enhanced MR angiography (CE-MRA), and 4-point Dixon data were acquired at 3T.

ASSESSMENT

The vessel lumen of the CA was automatically segmented using support vector machines (SVM) with CE-MRA data, and the vessel wall region was subsequently delineated. Automatically generated segmentations were quantitatively measured and three observers visually compared the segmentations to manual segmentations performed on T₁ w images. Dixon data were used to generate FF and R2* maps. Both manually and automatically generated segmentations of the CA and vessel wall were used to extract compositional data.

STATISTICAL TESTS

Two-tailed t-tests were used to examine differences between results generated using manual and automated analyses, and among different configurations of the automated method. Interobserver agreement was assessed with Fleiss' kappa.

RESULTS

Automated segmentation of the CA using SVM had a Dice score of 0.89 ± 0.02 and true-positive ratio 0.93 ± 0.03 when compared against ground truth, and median qualitative score of 4/5 when assessed visually by multiple observers. Vessel wall regions of 0.5 and 1 mm yielded compositional information similar to that gained from manual analyses. Using the 0.5 mm vessel wall region, the mean difference was 0.1 ± 2.5% considering FF and 1.1 ± 5.7[1/s] for R2*.

LEVEL OF EVIDENCE

1.

TECHNICAL EFFICACY STAGE

1. J. Magn. Reson. Imaging 2020;52:710-719.

Fat fraction mapping using magnetic resonance imaging: insight into pathophysiology

Adipose cells have traditionally been viewed as a simple, passive energy storage depot for triglycerides. However, in recent years it has become clear that adipose cells are highly physiologically active and have a multitude of endocrine, metabolic, haematological and immune functions. Changes in the number or size of adipose cells may be directly implicated in disease (e.g. in the metabolic syndrome), but may also be linked to other pathological processes such as inflammation, malignant infiltration or infarction. MRI is ideally suited to the quantification of fat, since most of the acquired signal comes from water and fat protons. Fat fraction (FF, the proportion of the acquired signal derived from fat protons) has, therefore, emerged as an objective, image-based biomarker of disease. Methods for FF quantification are becoming increasingly available in both research and clinical settings, but these methods vary depending on the scanner, manufacturer, imaging sequence and reconstruction software being used. Careful selection of the imaging method-and correct interpretation-can improve the accuracy of FF measurements, minimize potential confounding factors and maximize clinical utility. Here, we review methods for fat quantification and their strengths and weaknesses, before considering how they can be tailored to specific applications, particularly in the gastrointestinal and musculoskeletal systems. FF quantification is becoming established as a clinical and research tool, and understanding the underlying principles will be helpful to both imaging scientists and clinicians.

Vessel wall characterization using quantitative MRI: what's in a number?

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.