Multimodal Image Analysis for Carotid Artery Plaque Characterization

Atherosclerosis of the carotid artery is a main cause of ischemic cerebrovascular events. There is evidence that the composition of the vessel wall is more strongly related to plaque vulnerability and subsequent events than luminal stenosis, which is currently used for risk stratification in clinical practice. Noninvasive imaging can characterize the composition of the vessel wall. In order to incorporate measures of plaque composition into clinical practice, accurate and robust image segmentation methods are required. This thesis describes the development and validation of image analysis techniques that aim at the automated characterization of the carotid atherosclerotic vessel wall. The first part of this thesis makes use of a dataset in which ex vivo and in vivo MRI and CT, and annotated histology sections are available and have been spatially aligned. We firstly perform segmentation of plaque components in ex vivo MRI. Voxel classifiers are trained on a ground truth of registered histology and μCT images. We show the importance of different groups of features: intensities, Gaussian filters and wall distances, and use these features in subsequent work on in vivo data. Here we address the problems that arise in training and evaluation of segmentation methods when misregistration between histology and in vivo

Maximization of Regional probabilities using Optimal Surface Graphs: Application to Carotid Artery Segmentation in MRI

__Purpose__ We present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images. __Methods__ First, voxel-wise regional probability maps were obtained using a Support Vector Machine classifier trained on local image features. Then the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints. __Results__ The method was evaluated on 49 carotid arteries from 30 subjects. The proposed method shows good accuracy with a Dice wall overlap of 74:1%+-4:3%, and significantly outperforms a published method based on an OSG using only surface information, the obtained segmentations using voxel-wise classification alone, and another published artery wall segmentation method based on a deformable surface model. Intra-class correlations (ICC) with manually measured lumen and wall volumes were similar to those obtained between observers. Finally, we show a good reproducibility of the method with ICC = 0:86 between the volumes measured in scans repeated within a short time interval. __Conclusions__ In this work a new segmentation method that uses both an OSG and regional probabilities is presented. The method shows good segmentations of the carotid artery in MRI and outperformed another segmentation method that uses OSG and edge information and the voxel-wise segmentation using the probability maps

Comparison of CT and CMR for detection and quantification of carotid artery calcification:the Rotterdam Study

Background: Carotid artery atherosclerosis is an important risk factor for stroke. As such, quantitative imaging of carotid artery calcification, as a proxy of atherosclerosis, has become a cornerstone of current stroke research. Yet, population-based data comparing the computed tomography (CT) and cardiovascular magnetic resonance (CMR) for the detection and quantification of calcification remain scarce. Methods: A total of 684 participants from the population-based Rotterdam Study underwent both a CT and CMR of the carotid artery bifurcation to quantify the amount of carotid artery calcification (mean interscan interval: 4.9 ± 1.2 years). We investigated the correlation between the amount of calcification measured on CT and CMR using Spearman’s correlation coefficient, Bland-Altman plots, and linear regression. In addition, using logistic regression modeling, we assessed the association of CT and CMR based calcification volumes with a history of stroke. Results: We found a strong correlation between CT and CMR based calcification volumes (Spearman’s correlation coefficient:0.86, p-value ≤0.01). Bland-Altman analyses showed a good agreement, though CT based calcification volumes were systematically larger. Finally, calcification volume assessed with either imaging modality was associated with a history of stroke with similar effect estimates (odds ratio (OR) per 1-SD increase in calcification volume: 1.52 (95% CI:1.00;2.30) for CT, and 1.47 (95% CI:1.01;2.14) for CMR. Conclusion: CT based and CMR based volumes of carotid artery calcification are highly correlated, but CMR based calcification is systematically smaller than those obtained with CT. Despite this difference, both provide comparable information with regard to a history of stroke. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0340-z) contains supplementary material, which is available to authorized users

Estimating central blood pressure from aortic flow: development and assessment of algorithms

Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data. NEW & NOTEWORTHY First, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available.This work was supported by a PhD Fellowship awarded by the King’s College London and Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging [EP/L015226/1], the British Heart Foundation (BHF) [PG/15/104/31913], and the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]. The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative at Guy’s and St Thomas’ NHS Foundation Trust (GSTT)

An Integrated Software Application for Non-invasive Assessment of Local Aortic Haemodynamic Parameters:20th Conference on Medical Image Understanding and Analysis (MIUA 2016)

AbstractNon-invasive assessment of haemodynamic data, such as pressure and flow profiles, is helpful in detecting cardiac disease at an early stage. However, current methods lack spatial accuracy and do not take local variations into account. This paper presents a software tool that extracts the arterial geometry and blood inflow profiles from MR images, which are subsequently used to run a 1D haemodynamic simulation model, and displays its output. The workflow is highly automated but allows user-interaction to correct inaccuracies. The tool was evaluated for inter-observer agreement on one healthy volunteer, and results are shown for one patient with an aortic coarctation. The resulting haemodynamic parameters show high agreement between different users and reveal local changes within a coarctation patient

Aortic length measurements for pulse wave velocity calculation:manual 2D vs automated 3D centreline extraction

BACKGROUND: Pulse wave velocity (PWV) is a biomarker for the intrinsic stiffness of the aortic wall, and has been shown to be predictive for cardiovascular events. It can be assessed using cardiovascular magnetic resonance (CMR) from the delay between phase-contrast flow waveforms at two or more locations in the aorta, and the distance on CMR images between those locations. This study aimed to investigate the impact of different distance measurement methods on PWV. We present and evaluate an algorithm for automated centreline tracking in 3D images, and compare PWV calculations using distances derived from 3D images to those obtained from a conventional 2D oblique-sagittal image of the aorta. METHODS: We included 35 patients from a twin cohort, and 20 post-coarctation repair patients. Phase-contrast flow was acquired in the ascending, descending and diaphragmatic aorta. A 3D centreline tracking algorithm is presented and evaluated on a subset of 30 subjects, on three CMR sequences: balanced steady-state free precession (SSFP), black-blood double inversion recovery turbo spin echo, and contrast-enhanced CMR angiography. Aortic lengths are subsequently compared between measurements from a 2D oblique-sagittal plane, and a 3D geometry. RESULTS: The error in length of automated 3D centreline tracking compared with manual annotations ranged from 2.4 [1.8-4.3] mm (mean [IQR], black-blood) to 6.4 [4.7-8.9] mm (SSFP). The impact on PWV was below 0.5m/s (<5%). Differences between 2D and 3D centreline length were significant for the majority of our experiments (p < 0.05). Individual differences in PWV were larger than 0.5m/s in 15% of all cases (thoracic aorta) and 37% when studying the aortic arch only. Finally, the difference between end-diastolic and end-systolic 2D centreline lengths was statistically significant (p < 0.01), but resulted in small differences in PWV (0.08 [0.04 - 0.10]m/s). CONCLUSIONS: Automatic aortic centreline tracking in three commonly used CMR sequences is possible with good accuracy. The 3D length obtained from such sequences can differ considerably from lengths obtained from a 2D oblique-sagittal plane, depending on aortic curvature, adequate planning of the oblique-sagittal plane, and patient motion between acquisitions. For accurate PWV measurements we recommend using 3D centrelines

Three-Dimensional Carotid Ultrasound Plaque Texture Predicts Vascular Events

Background and Purpose-Carotid ultrasound atherosclerosis measurements, including those of the arterial wall and plaque, provide a way to monitor patients at risk of vascular events. Our objective was to examine carotid ultrasound plaque texture measurements and the change in carotid plaque texture during 1 year in patients at risk of events and to compare these with measurements of plaque volume and other risk factors as predictors of vascular events. Methods-We evaluated 298 patients with carotid atherosclerosis using 3-dimensional (3D) ultrasound at baseline and after 1 year and measured carotid plaque volume and 376 measures of plaque texture. Patients were followed up to 5 years (median [range], 3.12 [0.77-4.66]) for myocardial infarction, transient ischemic attack, and stroke. Sparse Cox regression was used to select the most predictive plaque texture measurements in independent training sets using a 10-fold cross-validation, repeated 5x, to ensure unbiased results. Results-Receiver operator curves and Kaplan-Meier analysis showed that changes in texture and total plaque volume combined provided the best predictor of vascular events. In multivariate Cox regression, changes in plaque texture (median hazard ratio, 1.4; P<0.001) and total plaque volume (median hazard ratio, 1.5 per 100 mm(3); P<0.001) were both significant predictors, whereas the Framingham risk score was not. Conclusions-Changes in both plaque texture and volume are strongly predictive of vascular events. In high-risk patients, 3D ultrasound plaque measurements should be considered for vascular event risk prediction