Segmentation of carotid plaque using multicontrast 3D gradient echo MRI

Flow independent black blood imaging with a large FOV from the neck to the aortic arch: A feasibility study at 3 tesla

PURPOSE

To investigate the feasibility of obtaining black-blood imaging with a large FOV from the neck to the aortic arch at 3 T using a newly modified Relaxation-Enhanced Angiography without Contrast and Triggering for Black-Blood Imaging (REACT-BB).

MATERIALS AND METHODS

REACT-BB provides black-blood images by adjusting the inversion time (TI) in REACT to the null point of blood. The optimal TI for REACT-BB was investigated in 10 healthy volunteers with TI varied from 200 ms to 1400 ms. Contrast ratios were calculated between muscle and three branch arteries of the aortic arch. Additionally, a comparison between REACT-BB and MPRAGE involved evaluating the depiction of high-intensity plaques in 222 patients with stroke or transient ischemic attack. Measurements included plaque-to-muscle signal intensity ratios (PMR), plaque volumes, and carotid artery stenosis rates in 60 patients with high-intensity plaques in carotid arteries.

RESULTS

REACT-BB with TI = 850 ms produced the black-blood image with the best contrast between blood and background tissues. REACT-BB outperformed MPRAGE in depicting high-intensity plaques in the aortic arch (55.4% vs 45.5%) and exhibited superior overall image quality in visual assessment (3.31 ± 0.70 vs 2.89 ± 0.73; p < 0.05). Although the PMR of REACT-BB was significantly lower than MPRAGE (2.227 ± 0.601 vs 2.285 ± 0.662; P < 0.05), a strong positive correlation existed between REACT-BB and MPRAGE (ρ = 0.935; P < 0.05), and all high-intensity plaques that MPRAGE detected were clearly detected by REACT-BB.

CONCLUSION

REACT-BB provides black-blood images with uniformly suppressed fat and blood signals over a large FOV from the neck to the aortic arch with comparable or better high-signal plaque depiction than MPRAGE.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI

PURPOSE

Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW-MRI images is cumbersome and has low reproducibility. Therefore in this paper, by using the convolutional neural networks (CNNs), we develop a deep morphology aided diagnosis (DeepMAD) network for automated segmentation of the VW of carotid artery and for automated diagnosis of the carotid atherosclerosis with the black-blood (BB) VW-MRI (i.e., the T1-weighted MRI) in a slice-by-slice manner.

METHODS

The proposed DeepMAD network consists of a segmentation subnetwork and a diagnosis subnetwork for performing the segmentation and diagnosis tasks on the BB-VW-MRI images, where the manual labeled lumen area, the manual labeled outer wall area and the manual labeled lesion Types based on the modified American Heart Association (AHA) criteria are used as the ground-truth. Specifically, a deep U-shape CNN with a weighted fusion layer is designed as the segmentation subnetwork, where the lumen area and the outer wall area can be simultaneously segmented under the supervision of the triple Dice loss to provide the vessel wall map as morphological information. Then, the image stream from the BB-VWMRI image and the morphology stream from the obtained vessel wall map are extracted from two deep CNNs and combined to obtain the diagnosis results of atherosclerosis in the diagnosis subnetwork. In addition, the triple input set is formed by three carotid regions of interest (ROIs) from three consecutive slices of the MRI sequence and input to the DeepMAD network, where the first and last slices used as additional adjacent slices to provide 2.5D spatial information along the carotid artery centerline for the intermediate slice, which is the target slice for segmentation and diagnosis in the study.

RESULTS

Compared to other existing methods, the DeepMAD network can achieve promising segmentation performances (0.9594 Dice for the lumen and 0.9657 Dice for the outer wall) and better diagnosis Accuracy of the carotid atherosclerosis (0.9503 AUC and 0.8916 Accuracy) in the test dataset (including invisible subjects) from same source as the training dataset. In addition, the trained DeepMAD model can be successfully transferred to another test dataset for segmentation and diagnosis tasks with remarkable performance (0.9475 Dice for the lumen and 0.9542 Dice for the outer wall, 0. 9227 AUC and 0.8679 Accuracy for diagnosis).

CONCLUSIONS

Even without the intervention of reviewers required for previous works, the proposed DeepMAD network automatically segments the lumen and the outer wall together and diagnoses the carotid atherosclerosis with high performances. The DeepMAD network can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate the normal carotid from the atherosclerotic arteries and outlining the vessel wall contours.

Three-dimensional black-blood multi-contrast carotid imaging using compressed sensing: a repeatability study

OBJECTIVE

The purpose of this work is to evaluate the repeatability of a compressed sensing (CS) accelerated multi-contrast carotid protocol at 3 T.

MATERIALS AND METHODS

Twelve volunteers and eight patients with carotid disease were scanned on a 3 T MRI scanner using a CS accelerated 3-D black-blood multi-contrast protocol which comprises T ₁w, T ₂w and PDw without CS, and with a CS factor of 1.5 and 2.0. The volunteers were scanned twice, the lumen/wall area and wall thickness were measured for each scan. Eight patients were scanned once, the inter/intra-observer reproducibility of the measurements was calculated.

RESULTS

In the repeated volunteer scans, the interclass correlation coefficient (ICC) for the wall area measurement using a CS factor of 1.5 in PDw, T ₁w and T ₂w were 0.95, 0.81, and 0.97, respectively. The ICC for lumen area measurement using a CS factor of 1.5 in PDw, T ₁w and T ₂w were 0.96, 0.92, and 0.96, respectively. In patients, the ICC for inter/intra-observer measurements of lumen/wall area, and wall thickness were all above 0.81 in all sequences.

CONCLUSION

The results show a CS accelerated 3-D black-blood multi-contrast protocol is a robust and reproducible method for carotid imaging. Future protocol design could use CS to reduce the scanning time.

Imaging of the high-risk carotid plaque: magnetic resonance imaging

The emergence of the concept of high-risk atherosclerotic plaque has led to considerable interest in noninvasive imaging techniques to identify high-risk features before clinical sequelae. For plaques in the carotid arteries, magnetic resonance imaging has undergone considerable histologic validation to link imaging features to indicators of plaque instability, including plaque burden, intraplaque hemorrhage, fibrous cap disruption, lipid rich necrotic core, and calcification. Recently introduced imaging technologies, especially those focused on three-dimensional imaging sequences, are now poised for integration into the clinical workup of patients with suspected carotid atherosclerosis. The purpose of this article is to review the carotid plaque magnetic resonance imaging techniques that are most ready for integration into the clinic.

Contrast agents for cardiovascular magnetic resonance imaging: an overview

Contrast agents for Cardiovascular Magnetic Resonance (CMR) play a major role in research and clinical cardiology.

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.

MRI-based biomechanical parameters for carotid artery plaque vulnerability assessment

Carotid atherosclerotic plaques are a major cause of ischaemic stroke. The biomechanical environment to which the arterial wall and plaque is subjected to plays an important role in the initiation, progression and rupture of carotid plaques. MRI is frequently used to characterize the morphology of a carotid plaque, but new developments in MRI enable more functional assessment of carotid plaques. In this review, MRI based biomechanical parameters are evaluated on their current status, clinical applicability, and future developments. Blood flow related biomechanical parameters, including endothelial wall shear stress and oscillatory shear index, have been shown to be related to plaque formation. Deriving these parameters directly from MRI flow measurements is feasible and has great potential for future carotid plaque development prediction. Blood pressure induced stresses in a plaque may exceed the tissue strength, potentially leading to plaque rupture. Multi-contrast MRI based stress calculations in combination with tissue strength assessment based on MRI inflammation imaging may provide a plaque stress-strength balance that can be used to assess the plaque rupture risk potential. Direct plaque strain analysis based on dynamic MRI is already able to identify local plaque displacement during the cardiac cycle. However, clinical evidence linking MRI strain to plaque vulnerability is still lacking. MRI based biomechanical parameters may lead to improved assessment of carotid plaque development and rupture risk. However, better MRI systems and faster sequences are required to improve the spatial and temporal resolution, as well as increase the image contrast and signal-to-noise ratio.

Analysis of Multicontrast Carotid Plaque MR Imaging

Plaque imaging by MR imaging provides a wealth of information on the characteristics of individual plaque that may reveal vulnerability to rupture, likelihood of progression, or optimal treatment strategy. T1-weighted and T2-weighted images among other options reveal plaque morphology and composition. Dynamic contrast-enhanced-MR imaging reveals plaque activity. To extract this information, image processing tools are needed. Numerous approaches for analyzing such images have been developed, validated against histologic gold standards, and used in clinical studies. These efforts are summarized in this article.

In vivo semi-automatic segmentation of multicontrast cardiovascular magnetic resonance for prospective cohort studies on plaque tissue composition: initial experience

Automatic in vivo segmentation of multicontrast (multisequence) carotid magnetic resonance for plaque composition has been proposed as a substitute for manual review to save time and reduce inter-reader variability in large-scale or multicenter studies. Using serial images from a prospective longitudinal study, we sought to compare a semi-automatic approach versus expert human reading in analyzing carotid atherosclerosis progression. Baseline and 6-month follow-up multicontrast carotid images from 59 asymptomatic subjects with 16-79 % carotid stenosis were reviewed by both trained radiologists with 2-4 years of specialized experience in carotid plaque characterization with MRI and a previously reported automatic atherosclerotic plaque segmentation algorithm, referred to as morphology-enhanced probabilistic plaque segmentation (MEPPS). Agreement on measurements from individual time points, as well as on compositional changes, was assessed using the intraclass correlation coefficient (ICC). There was good agreement between manual and MEPPS reviews on individual time points for calcification (CA) (area: ICC; 0.85-0.91; volume: ICC; 0.92-0.95) and lipid-rich necrotic core (LRNC) (area: ICC; 0.78-0.82; volume: ICC; 0.84-0.86). For compositional changes, agreement was good for CA volume change (ICC; 0.78) and moderate for LRNC volume change (ICC; 0.49). Factors associated with LRNC progression as detected by MEPPS review included intraplaque hemorrhage (positive association) and reduction in low-density lipoprotein cholesterol (negative association), which were consistent with previous findings from manual review. Automatic classifier for plaque composition produced results similar to expert manual review in a prospective serial MRI study of carotid atherosclerosis progression. Such automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.

A computer-simulation study on the effects of MRI voxel dimensions on carotid plaque lipid-core and fibrous cap segmentation and stress modeling

Background

The benefits of a decreased slice thickness and/or in-plane voxel size in carotid MRI for atherosclerotic plaque component quantification accuracy and biomechanical peak cap stress analysis have not yet been investigated in detail because of practical limitations.

Methods

In order to provide a methodology that allows such an investigation in detail, numerical simulations of a T1-weighted, contrast-enhanced, 2D MRI sequence were employed. Both the slice thickness (2 mm, 1 mm, and 0.5 mm) and the in plane acquired voxel size (0.62x0.62 mm² and 0.31x0.31 mm²) were varied. This virtual MRI approach was applied to 8 histology-based 3D patient carotid atherosclerotic plaque models.

Results

A decreased slice thickness did not result in major improvements in lumen, vessel wall, and lipid-rich necrotic core size measurements. At 0.62x0.62 mm² in-plane, only a 0.5 mm slice thickness resulted in improved minimum fibrous cap thickness measurements (a 2–3 fold reduction in measurement error) and only marginally improved peak cap stress computations. Acquiring voxels of 0.31x0.31 mm² in-plane, however, led to either similar or significantly larger improvements in plaque component quantification and computed peak cap stress.

Conclusions

This study provides evidence that for currently-used 2D carotid MRI protocols, a decreased slice thickness might not be more beneficial for plaque measurement accuracy than a decreased in-plane voxel size. The MRI simulations performed indicate that not a reduced slice thickness (i.e. more isotropic imaging), but the acquisition of anisotropic voxels with a relatively smaller in-plane voxel size could improve carotid plaque quantification and computed peak cap stress accuracy.

Carotid magnetic resonance imaging for monitoring atherosclerotic plaque progression: a multicenter reproducibility study

This study sought to determine the multicenter reproducibility of magnetic resonance imaging (MRI) and the compatibility of different scanner platforms in assessing carotid plaque morphology and composition. A standardized multi-contrast MRI protocol was implemented at 16 imaging sites (GE: 8; Philips: 8). Sixty-eight subjects (61 ± 8 years; 52 males) were dispersedly recruited and scanned twice within 2 weeks on the same magnet. Images were reviewed centrally using a streamlined semiautomatic approach. Quantitative volumetric measurements on plaque morphology (lumen, wall, and outer wall) and plaque tissue composition [lipid-rich necrotic core (LRNC), calcification, and fibrous tissue] were obtained. Inter-scan reproducibility was summarized using the within-subject standard deviation, coefficient of variation (CV) and intraclass correlation coefficient (ICC). Good to excellent reproducibility was observed for both morphological (ICC range 0.98-0.99) and compositional (ICC range 0.88-0.96) measurements. Measurement precision was related to the size of structures (CV range 2.5-4.9 % for morphology, 36-44 % for LRNC and calcification). Comparable measurement variability was found between the two platforms on both plaque morphology and tissue composition. In conclusion, good to excellent inter-scan reproducibility of carotid MRI can be achieved in multicenter settings with comparable measurement precision between platforms, which may facilitate future multicenter endeavors that use serial MRI to monitor atherosclerotic plaque progression.

Imaging of high-risk carotid artery plaques: current status and future directions

In this paper, the authors review the definition of high-risk plaque as developed by experienced researchers in atherosclerosis, including pathologists, clinicians, molecular biologists, and imaging scientists. Current concepts of vulnerable plaque are based on histological studies of coronary and carotid artery plaque as well as natural history studies and include the presence of a lipid-rich necrotic core with an overlying thin fibrous cap, plaque inflammation, fissured plaque, and intraplaque hemorrhage. The extension of these histologically identified high-risk carotid plaque features to human in vivo MRI is reviewed as well. The authors also assess the ability of in vivo MRI to depict these vulnerable carotid plaque features. Next, the ability of these MRI-demonstrated high-risk carotid plaque features to predict the risk of ipsilateral carotid thromboembolic events is reviewed and compared with the risk assessment provided by simple carotid artery stenosis measurements. Lastly, future directions of high-risk carotid plaque MRI are discussed, including the potential for increased clinical availability and more automated analysis of carotid plaque MRI. The ultimate goal of high-risk plaque imaging is to design and run future multicenter trials using carotid plaque MRI to guide individual patient selection and decisions about optimal atherosclerotic treatment strategies.

Visualizing the atherosclerotic plaque: a chemical perspective

Atherosclerosis is the major underlying pathologic cause of coronary artery disease. An early detection of the disease can prevent clinical sequellae such as angina, myocardial infarction, and stroke. The different imaging techniques employed to visualize the atherosclerotic plaque provide information of diagnostic and prognostic value. Furthermore, the use of contrast agents helps to improve signal-to-noise ratio providing better images. For nuclear imaging techniques and optical imaging these agents are absolutely necessary. We report on the different contrast agents that have been used, are used or may be used in future in animals, humans, or excised tissues for the distinct imaging modalities for atherosclerotic plaque imaging.

The Influence of Inaccuracies in Carotid MRI Segmentation on Atherosclerotic Plaque Stress Computations

Biomechanical finite element analysis (FEA) based on in vivo carotid magnetic resonance imaging (MRI) can be used to assess carotid plaque vulnerability noninvasively by computing peak cap stress. However, the accuracy of MRI plaque segmentation and the influence this has on FEA has remained unreported due to the lack of a reliable submillimeter ground truth. In this study, we quantify this influence using novel numerical simulations of carotid MRI. Histological sections from carotid plaques from 12 patients were used to create 33 ground truth plaque models. These models were subjected to numerical computer simulations of a currently used clinically applied 3.0 T T1-weighted black-blood carotid MRI protocol (in-plane acquisition voxel size of 0.62 × 0.62 mm2) to generate simulated in vivo MR images from a known underlying ground truth. The simulated images were manually segmented by three MRI readers. FEA models based on the MRI segmentations were compared with the FEA models based on the ground truth. MRI-based FEA model peak cap stress was consistently underestimated, but still correlated (R) moderately with the ground truth stress: R = 0.71, R = 0.47, and R = 0.76 for the three MRI readers respectively (p < 0.01). Peak plaque stretch was underestimated as well. The peak cap stress in thick-cap, low stress plaques was substantially more accurately and precisely predicted (error of −12 ± 44 kPa) than the peak cap stress in plaques with caps thinner than the acquisition voxel size (error of −177 ± 168 kPa). For reliable MRI-based FEA to compute the peak cap stress of carotid plaques with thin caps, the current clinically used in-plane acquisition voxel size (∼0.6 mm) is inadequate. FEA plaque stress computations would be considerably more reliable if they would be used to identify thick-cap carotid plaques with low stresses instead.

3D high-resolution contrast enhanced MRI of carotid atheroma--a technical update

OBJECTIVE

Development of a fast 3D high-resolution magnetic resonance imaging (MRI) protocol for improved carotid artery plaque imaging.

METHODS

Two patients with carotid atherosclerosis disease underwent 3D high-resolution MRI which included time-of-flight and T1-weighted variable flip angle, fast-spin-echo (FSE) imaging, pre- and post-intravenous gadolinium-based contrast agent administration.

RESULTS

Good quality images with intrinsic blood suppression were obtained pre- and post-contrast administration using a 3D FSE sequence. The plaque burden, lipid core volume, hemorrhage volume and fibrous cap thickness were well determined.

CONCLUSIONS

3D high-resolution MR imaging of carotid plaque using TOF and 3D FSE can achieve high isotropic resolution, large coverage, and excellent image quality within a short acquisition time.

3-D carotid multi-region MRI segmentation by globally optimal evolution of coupled surfaces

In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into three regions: wall, lumen, and background. The algorithm performs such partitioning by simultaneously evolving two coupled 3-D surfaces of carotid artery adventitia boundary (AB) and lumen-intima boundary (LIB) while preserving their anatomical inter-surface consistency such that the LIB is always located within the AB. In particular, we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the AB and LIB to their globally optimal positions during each discrete time frame by convex relaxation. In this regard, we introduce the continuous max-flow model and prove its duality/equivalence to the convex relaxed optimization problem with respect to each evolution step. We then propose a fully parallelized continuous max-flow-based algorithm, which can be readily implemented on a GPU to achieve high computational efficiency. Extensive experiments, with four users using 12 3T MR and 26 1.5T MR images, demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume. In addition, we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions.

Carotid artery disease and stroke: assessing risk with vessel wall MRI

Although MRI is widely used to diagnose stenotic carotid arteries, it also detects characteristics of the atherosclerotic plaque itself, including its size, composition, and activity. These features are emerging as additional risk factors for stroke that can be feasibly acquired clinically. This paper summarizes the state of evidence for a clinical role for MRI of carotid atherosclerosis.

Gray blood magnetic resonance for carotid wall imaging and visualization of deep-seated and superficial vascular calcifications

White blood and black blood magnetic resonance imaging methods are often used for lumenography and visualization of the arterial wall, respectively. However, the use of white blood imaging invariably obscures arterial wall boundaries, and thus, impedes precise measurement of arterial wall area. Conversely, black blood imaging imposes strict limits on sequence timing to suppress the arterial lumen, and by itself, precludes separation of superficial calcification from the hypointense arterial lumen. In this work, a three-dimensional arterial wall imaging methodology providing gray blood image contrast is described that remedies the above limitations. When applied to the carotid arteries, the described gray blood imaging method is found to clearly depict the inner and outer arterial wall boundaries as well as superficial and deep-seated vascular calcifications. A tailored phase-encoding schedule is also presented that enables concurrent gray and black blood, or "dual contrast," imaging of the arterial wall with no increase in the acquisition time. Taken together, presented data demonstrate that gray and dual blood contrast magnetic resonance imaging provide an efficient means for viewing and characterizing the composition of atherosclerotic plaques.