Novel Imaging-Based Biomarkers for Identifying Carotid Plaque Vulnerability

Carotid artery disease has traditionally been assessed based on the degree of luminal narrowing. However, this approach, which solely relies on carotid stenosis, is currently being questioned with regard to modern risk stratification approaches. Recent guidelines have introduced the concept of the "vulnerable plaque," emphasizing specific features such as thin fibrous caps, large lipid cores, intraplaque hemorrhage, plaque rupture, macrophage infiltration, and neovascularization. In this context, imaging-based biomarkers have emerged as valuable tools for identifying higher-risk patients. Non-invasive imaging modalities and intravascular techniques, including ultrasound, computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy, have played pivotal roles in characterizing and detecting unstable carotid plaques. The aim of this review is to provide an overview of the evolving understanding of carotid artery disease and highlight the significance of imaging techniques in assessing plaque vulnerability and informing clinical decision-making.

VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification

BACKGROUND

Quantitative plaque assessment based on 3D magnetic resonance (MR) vessel wall imaging (VWI) has been shown to provide valuable numerical markers of the burden and risk of intracranial atherosclerotic disease (ICAD). However, plaque quantification is currently time-consuming and observer-dependent due to the demand for heavy manual effort. A VWI-dedicated automated processing pipeline (VWI-APP) is desirable.

PURPOSE

To develop and evaluate a VWI-APP for end-to-end quantitative analysis of intracranial atherosclerotic plaque.

METHODS

We retrospectively enrolled 91 subjects with ICAD (80 for pipeline development, 10 for an end-to-end pipeline evaluation, and 1 for demonstrating longitudinal plaque assessment) who had undergone VWI and MR angiography. In an end-to-end evaluation, diameter stenosis (DS), normalized wall index (NWI), remodeling ratio (RR), plaque wall contrast ratio (CR), and total plaque volume (TPV) were quantified at each culprit lesion using the developed VWI-APP and a computer-aided manual approach by a neuroradiologist, respectively. The time consumed in each quantification approach was recorded. Two-sided paired t-tests and intraclass correlation coefficient (ICC) were used to determine the difference and agreement in each plaque metric between VWI-APP and manual quantification approaches.

RESULTS

There was no significant difference between VWI-APP and manual quantification in each plaque metric. The ICC was 0.890, 0.813, 0.827, 0.891, and 0.991 for DS, NWI, RR, CR, and TPV, respectively, suggesting good to excellent accuracy of the pipeline method in plaque quantification. Quantitative analysis of each culprit lesion on average took 675.7 s using the manual approach but shortened to 238.3 s with the aid of VWI-APP.

CONCLUSIONS

VWI-APP is an accurate and efficient approach to intracranial atherosclerotic plaque quantification. Further clinical assessment of this automated tool is warranted to establish its utility in the risk assessment of ICAD lesions.

3D carotid artery segmentation using shape-constrained active contours

Reconstruction of the carotid artery is demanded in the detection and characterization of atherosclerosis. This study proposes a shape-constrained active contour model for segmenting the carotid artery from MR images, which embeds the output of the deep learning network into the active contour. First the centerline of the carotid artery is localized and then modified active contour initialized from the centerline is used to extract the vessel lumen, finally the probability atlas generated by the deep learning network in polar representation domain is integrated into the active contour as a prior information to detect the outer wall. The results showed that the proposed active contour model was efficient and comparable to manual segmentation.

Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion

PURPOSE

To develop an automated vessel wall segmentation method on T1-weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall.

METHODS

We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level-set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness.

RESULTS

Implemented with a 2.5D UNet with a ResNet backbone, the proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a baseline UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056, 0.119 ± 0.059 mm. Our vessel wall segmentation method achieved substantial improvement in morphological integrity and accuracy compared to benchmark methods.

CONCLUSIONS

The proposed method provides a systematic approach to model the inclusion morphology and incorporate it into an optimization infrastructure. It can be applied to any application where inclusion exists among a (sub)set of classes to be segmented. Improved feasibility in result morphology promises better support for clinical quantification and decision.

Intradural artery dilation during experimentally induced migraine attacks

The middle meningeal artery is a proposed surrogate marker for activation of trigeminal nociceptors during migraine. Previous studies focused on the extracranial part of the artery; hence, vasoreactivity in the intradural arteries during migraine is unknown. Thirty-four patients with migraine without aura were given sildenafil on one day and calcitonin gene-related peptide on another in double-blind crossover fashion. Patients were scanned with 3.0 T MR angiography before drug administration and again 6 hours later during induced attacks of migraine. We measured circumference of the intradural segment of the middle meningeal artery before and during induced migraine attacks. The middle cerebral and superficial temporal arteries were also examined. Fourteen patients had attacks during the second scan after both study drugs and 11 had a migraine after either one or the other, resulting in a total of 39 attacks included in the final analysis. Mean circumference of the intradural middle meningeal artery at baseline was 3.18 mm with an increase of 0.11 mm during attacks (P = 0.005), corresponding to a relative dilation of 3.6% [95% CI: 1.4%-5.7%]. Middle cerebral artery dilated by 9.4% [95% CI: 7.1%-11.7%] and superficial temporal artery by 2.3% [95% CI: 0.2%-4.4%]. Our study shows that the intradural middle meningeal artery and the middle cerebral artery are dilated during migraine induced by calcitonin gene-related peptide as well as sildenafil. We propose that intradural vasculature is affected by migraine-driven activation of trigeminal afferents during migraine attacks.

Domain adaptive and fully automated carotid artery atherosclerotic lesion detection using an artificial intelligence approach (LATTE) on 3D MRI

PURPOSE

To develop and evaluate a domain adaptive and fully automated review workflow (lesion assessment through tracklet evaluation, LATTE) for assessment of atherosclerotic disease in 3D carotid MR vessel wall imaging (MR VWI).

METHODS

VWI of 279 subjects with carotid atherosclerosis were used to develop LATTE, mainly convolutional neural network (CNN)-based domain adaptive lesion classification after image quality assessment and artery of interest localization. Heterogeneity in test sets from various sites usually causes inferior CNN performance. With our novel unsupervised domain adaptation (DA), LATTE was designed to accurately classify arteries into normal arteries and early and advanced lesions without additional annotations on new datasets. VWI of 271 subjects from four datasets (eight sites) with slightly different imaging parameters/signal patterns were collected to assess the effectiveness of DA of LATTE using the area under the receiver operating characteristic curve (AUC) on all lesions and advanced lesions before and after DA.

RESULTS

LATTE had good performance with advanced/all lesion classification, with the AUC of >0.88/0.83, significant improvements from >0.82/0.80 if without DA.

CONCLUSIONS

LATTE can locate target arteries and distinguish carotid atherosclerotic lesions with consistently improved performance with DA on new datasets. It may be useful for carotid atherosclerosis detection and assessment on various clinical sites.

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.

Quantification of arterial, venous, and cerebrospinal fluid flow dynamics by magnetic resonance imaging under simulated micro-gravity conditions: a prospective cohort study

BACKGROUND

Astronauts undergoing long-duration spaceflight are exposed to numerous health risks, including Spaceflight-Associated Neuro-Ocular Syndrome (SANS), a spectrum of ophthalmic changes that can result in permanent loss of visual acuity. The etiology of SANS is not well understood but is thought to involve changes in cerebrovascular flow dynamics in response to microgravity. There is a paucity of knowledge in this area; in particular, cerebrospinal fluid (CSF) flow dynamics have not been well characterized under microgravity conditions. Our study was designed to determine the effect of simulated microgravity (head-down tilt [HDT]) on cerebrovascular flow dynamics. We hypothesized that microgravity conditions simulated by acute HDT would result in increases in CSF pulsatile flow.

METHODS

In a prospective cohort study, we measured flow in major cerebral arteries, veins, and CSF spaces in fifteen healthy volunteers using phase contrast magnetic resonance (PCMR) before and during 15° HDT.

RESULTS

We found a decrease in all CSF flow variables [systolic peak flow (p = 0.009), and peak-to-peak pulse amplitude (p = 0.001)]. Cerebral arterial average flow (p = 0.04), systolic peak flow (p = 0.04), and peak-to-peak pulse amplitude (p = 0.02) all also significantly decreased. We additionally found a decrease in average cerebral arterial flow (p = 0.040). Finally, a significant increase in cerebral venous cross-sectional area under HDT (p = 0.005) was also observed.

CONCLUSIONS

These results collectively demonstrate that acute application of -15° HDT caused a reduction in CSF flow variables (systolic peak flow and peak-to-peak pulse amplitude) which, when coupled with a decrease in average cerebral arterial flow, systolic peak flow, and peak-to-peak pulse amplitude, is consistent with a decrease in cardiac-related pulsatile CSF flow. These results suggest that decreases in cerebral arterial inflow were the principal drivers of decreases in CSF pulsatile flow.

Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images

Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning. First, an optimal channel fitting structure is designed for identity mapping, and a novel 3D residual U-net is used as a basic network. Second, high-resolution MR images are trained using both patch-level and global-level strategies, and the two pre-segmentation results are optimized based on structural characteristics. Third, the optimized pre-segmentation results are cascaded with the patch-cropped MR volume data and trained to segment the carotid lumen and wall. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art 3D Unet-based segmentation models.

Automated Artery Localization and Vessel Wall Segmentation using Tracklet Refinement and Polar Conversion

Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.

Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images

Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.

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.

Ultra-high field MR angiography in human migraine models: a 3.0 T/7.0 T comparison study

BACKGROUND

Sildenafil and calcitonin gene-related peptide both dilate the intradural segments of the middle meningeal artery measured with 3.0 tesla (T) MR angiography. Here we hypothesized that an increase in field strength to 7.0 T and concomitant enhanced voxel resolution would lower variance in measurements of dilation in the intradural middle meningeal artery.

METHODS

Five subjects completed two sessions at respectively 3.0 T and 7.0 T. Each session comprised MR angiography scans once before and twice after administration of sildenafil, calcitonin gene-related peptide or placebo in a three-way, crossover, double-blind, placebo-controlled design.

RESULTS

Standard deviations of arterial circumference revealed no difference between 3.0 T and 7.0 T measurements (p = 0.379). We found a decrease in standard deviation from our original angiography analysis software (QMra) to a newer (LAVA) software package (p < 0.001). Furthermore, we found that the dilation after sildenafil and calcitonin gene-related peptide were comparable between 3.0 T and 7.0 T.

CONCLUSIONS

Our findings suggest no gain from the increase in voxel resolution but cemented dilatory findings from earlier. The implemented software update improved variance in circumference measurements in the intradural middle meningeal artery, which should be exploited in future studies.

TRIAL REGISTRATION

The study is part of a parent study, which is registered at ClinicalTrials.gov ( NCT03143465 ).

Whole-brain magnetic resonance imaging of plaque burden and lenticulostriate arteries in patients with different types of stroke

Background

Large-vessel atherosclerotic disease is an important pathogenesis of deep-perforator infarction (DPI). However, altered vessel walls of intracranial large arteries and distribution of small arteries in DPI are unclear because of the limited resolution of current imaging techniques. In this study the intracranial plaque burden and lenticulostriate artery (LSA) distribution in patients with recent DPI and non-DPI using whole-brain vessel-wall imaging (WB-VWI) were investigated.

Methods

A total of 44 patients with recent DPI (23 patients) or non-DPI (21 patients) due to intracranial atherosclerotic disease were prospectively enrolled. WB-VWI was performed in all the patients using a three-dimensional T1-weighted vessel-wall magnetic resonance technique. Hemispheres with DPI and non-DPI were considered as the DPI group and non-DPI group, respectively. Hemispheres without a history of stroke were the control group. The intracranial plaque burden was compared between the DPI and non-DPI groups. The number and length of visualized LSA branches among DPI, non-DPI, and control groups were also evaluated.

Results

A total of 77 hemispheres were analyzed (23 in the DPI group, 21 in the non-DPI group, and 33 in the control group). Plaque burden was lower (p = 0.047) in the DPI group (82.0 ± 45.9 mm³) compared with the non-DPI group (130.9 ± 90.3 mm³). There was a significant reduction (p = 0.002) in length of visualized LSA branches in the DPI group (74.1 ± 21.7 mm) compared with the control group (104.6 ± 33.3 mm).

Conclusions

WB-VWI enables the combination of vessel-wall and LSA imaging in one image setting, which can provide information about plaque burden and LSA distribution.

Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting

PURPOSE

The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI.

METHODS

The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis.

RESULTS

For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections.

CONCLUSIONS

The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers.

Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures

Automatic or semi-automatic segmentation of tissue types or organs is well established for X-ray-based computed tomography, with its fixed grey-scale and tissue classes with well-established ranges of Hounsfield units. MRI is much more powerful with regard to soft tissue contrast and quantitative assessment of tissue properties (e.g., perfusion, diffusion, fat content), but the principle of signal generation and recording in MRI leads to inherent problems if simple threshold based segmentation procedures are applied. In this editorial in the special issue of MAGMA on tissue segmentation, a number of relevant methodical, scientific, and clinical aspects of reliable tissue segmentation using data recording by MRI are reported and discussed.

Determinants of carotid atherosclerotic plaque burden in a stroke-free population

BACKGROUND AND AIMS

In a large stroke-free population, we sought to identify cardiovascular risk factors and carotid plaque components associated with carotid plaque burden, lumen volume and stenosis.

METHODS

The carotid arteries of 1562 stroke-free participants from The Rotterdam Study were imaged on a 1.5-Tesla MRI scanner. Inner and outer wall of the carotid arteries were automatically segmented and lumen volume (mm³), wall volume (outer wall-inner wall) and plaque burden (wall volume/outer wall volume) (%) were quantified. Plaque components were visually determined and luminal stenosis was assessed. We analyzed associations of cardiovascular risk factors and carotid plaque components with plaque burden and lumen volumes using regression analysis.

RESULTS

We investigated 2821 carotid plaques and found that women had larger plaque burden (50.7 ± 7.8% vs. 49.2 ± 7.7%, p < 0.0001) and smaller lumen volumes (933 ± 286 mm³vs. 1078 ± 334 mm³, p < 0.0001) than men. In women, age, HDL-cholesterol and systolic blood pressure, and in men, total cholesterol, non-HDL cholesterol and statin use were independently associated with higher plaque burden and lumen volume. Furthermore, smoking and diabetes were associated with lumen volume in men (respectively p = 0.04 and p = 0.002). Intraplaque hemorrhage (IPH) and lipid were related to a larger plaque burden (OR 1.30 [1.05-1.60] and OR 1.28[1.06-1.55]). Finally, within the highest quartile of plaque burden, IPH was strongly associated with luminal stenosis independent of age, sex, plaque burden and composition (Beta = 15.2; [11.8-18.6]).

CONCLUSIONS

Several cardiovascular risk factors and plaque components, in particular IPH, are associated with higher plaque burden. Carotid IPH is strongly associated with an increased luminal stenosis.

Low-glycaemic index diet to improve glycaemic control and cardiovascular disease in type 2 diabetes: design and methods for a randomised, controlled, clinical trial

INTRODUCTION

Type 2 diabetes (T2DM) produces macrovascular and microvascular damage, significantly increasing the risk of cardiovascular disease (CVD), renal failure and blindness. As rates of T2DM rise, the need for effective dietary and other lifestyle changes to improve diabetes management become more urgent. Low-glycaemic index (GI) diets may improve glycaemic control in diabetes in the short term; however, there is a lack of evidence on the long-term adherence to low-GI diets, as well as on the association with surrogate markers of CVD beyond traditional risk factors. Recently, advances have been made in measures of subclinical arterial disease through the use of MRI, which, along with standard measures from carotid ultrasound (CUS) scanning, have been associated with CVD events. We therefore designed a randomised, controlled, clinical trial to assess whether low-GI dietary advice can significantly improve surrogate markers of CVD and long-term glycaemic control in T2DM.

METHODS AND ANALYSIS

169 otherwise healthy individuals with T2DM were recruited to receive intensive counselling on a low-GI or high-cereal fibre diet for 3 years. To assess macrovascular disease, MRI and CUS are used, and to assess microvascular disease, retinal photography and 24-hour urinary collections are taken at baseline and years 1 and 3. Risk factors for CVD are assessed every 3 months.

ETHICS AND DISSEMINATION

The study protocol and consent form have been approved by the research ethics board of St. Michael's Hospital. If the study shows a benefit, these data will support the use of low-GI and/or high-fibre foods in the management of T2DM and its complications.

TRIAL REGISTRATION NUMBER

NCT01063374; Pre-results.

Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets

Extraction of the aorto-femoral vessel trajectory is important to utilize computed tomography angiography (CTA) in an integrated workflow of the image-guided work-up prior to trans-catheter aortic valve replacement (TAVR). The aim of this study was to develop a new, fully-automated technique for the extraction of the entire arterial access route from the femoral artery to the aortic root. An automatic vessel tracking algorithm was first used to find the centerline that connected the femoral accessing points and the aortic root. Subsequently, a deformable 3D-model fitting method was used to delineate the lumen boundary of the vascular trajectory in the whole-body CTA dataset. A validation was carried out by comparing the automatically obtained results with semi-automatically obtained results from two experienced observers. The whole framework was validated on whole body CTA datasets of 36 patients. The average Dice similarity indexes between the segmentations of the automatic method and observer 1 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta were 0.977 ± 0.030, 0.980 ± 0.019, 0.982 ± 0.016; the average Dice similarity indexes between the segmentations of the automatic method and observer 2 were 0.950 ± 0.040, 0.954 ± 0.031 and 0.965 ± 0.019, respectively. The inter-observer variability resulted in a Dice similarity index of 0.954 ± 0.038, 0.952 ± 0.031 and 0.969 ± 0.018 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta. The average minimal luminal diameters (MLDs) of the ilio-femoral artery were 6.03 ± 1.48, 5.70 ± 1.43 and 5.52 ± 1.32 mm for the automatic method, observer 1 and observer 2 respectively. The MLDs of the aorta were 13.43 ± 2.54, 12.40 ± 2.93 and 12.08 ± 2.40 mm for the automatic method, observer 1 and observer 2 respectively. The automatic measurement overestimated the MLD slightly in the ilio-femoral artery at the average by 0.323 mm (SD = 0.49 mm, p < 0.001) compared to observer 1 and by 0.51 mm (SD = 0.71 mm, p < 0.001) compared to observer 2. The proposed segmentation approach can automatically provide reliable measurements of the entire arterial accessing route that can be used to support TAVR procedures. To the best of our knowledges, this approach is the first fully automatic segmentation method of the whole aorto-femoral vessel trajectory in CTA images.

Carotid Artery Wall Segmentation in Multispectral MRI by Coupled Optimal Surface Graph Cuts

We present a new three-dimensional coupled optimal surface graph-cut algorithm to segment the wall of the carotid artery bifurcation from Magnetic Resonance (MR) images. The method combines the search for both inner and outer borders into a single graph cut and uses cost functions that integrate information from multiple sequences. Our approach requires manual localization of only three seed points indicating the start and end points of the segmentation in the internal, external, and common carotid artery. We performed a quantitative validation using images of 57 carotid arteries. Dice overlap of 0.86 ± 0.06 for the complete vessel and 0.89 ± 0.05 for the lumen compared to manual annotation were obtained. Reproducibility tests were performed in 60 scans acquired with an interval of 15 ± 9 days, showing good agreement between baseline and follow-up segmentations with intraclass correlations of 0.96 and 0.74 for the lumen and complete vessel volumes respectively.

Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease

Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20.

Validation of renal artery dimensions measured by magnetic resonance angiography in patients referred for renal sympathetic denervation

RATIONALE AND OBJECTIVES

Magnetic resonance angiography (MRA) is a well-established modality for the assessment of renal artery stenosis. Using dedicated quantitative analyses, MRA can become a useful tool for assessing renal artery dimensions in patients referred for renal sympathetic denervation (RDN) and for providing accurate measurements of vascular response after RDN. The purpose of this study was to test the reproducibility of a novel MRA quantitative imaging tool and to validate these measurements against intravascular ultrasound (IVUS).

MATERIALS AND METHODS

In nine patients referred for renal denervation, renal artery dimensions were measured. Bland-Altman analysis was used to assess the intraobserver and interobserver reproducibility.

RESULTS

Mean lumen diameter was 5.8 ± 0.7 mm, with a very good intraobserver and interobserver variability of 0.7% (reproducibility: bias, 0 mm; standard deviation [SD], 0.1 mm) and 1.2% (bias, 0 mm; SD, 0.1 mm), respectively. Mean total lumen volume was 1035.3 ± 403.6 mm(3) with good intraobserver and interobserver variability of 2.9% (bias, -9.7 mm(3); SD, 34.0 mm(3)) and 2.8% (bias, -11.4 mm(3); SD, 42.4 mm(3)). The correlation (Pearson R) between mean lumen diameter measured with MRA and IVUS was 0.750 (P = .002).

CONCLUSIONS

Using a novel MRA quantitative imaging tool, renal artery dimensions can be measured with good reproducibility and accuracy. MRA-derived diameters and volumes correlated well with IVUS measurements.

Multiple Sparse Representations Classification

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level.

Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty

Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with μCT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9±1.0% for calcification, 12.7±7.6% for fibrous and 12.1±8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.

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.

Efficient global optimization based 3D carotid AB-LIB MRI segmentation by simultaneously evolving coupled surfaces

Magnetic resonance (MR) imaging of carotid atherosclerosis biomarkers are increasingly being investigated for the risk assessment of vulnerable plaques. A fast and robust 3D segmentation of the carotid adventitia (AB) and lumen-intima (LIB) boundaries can greatly alleviate the measurement burden of generating quantitative imaging biomarkers in clinical research. In this paper, we propose a novel global optimization-based approach to segment the carotid AB and LIB from 3D T1-weighted black blood MR images, by simultaneously evolving two coupled surfaces with enforcement of anatomical consistency of the AB and LIB. We show that the evolution of two surfaces at each discrete time-frame can be optimized exactly and globally by means of convex relaxation. Our continuous max-flow based algorithm is implemented in GPUs to achieve high computational performance. The experiment results from 16 carotid MR images show that the algorithm obtained high agreement with manual segmentations and achieved high repeatability in segmentation.

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.