Validation of an arterial tortuosity measure with application to hypertension collection of clinical hypertensive patients

<p>Abstract</p> <p>Background</p> <p>Hypertension may increase tortuosity or twistedness of arteries. We applied a centerline extraction algorithm and tortuosity metric to magnetic resonance angiography (MRA) brain images to quantitatively measure the tortuosity of arterial vessel centerlines. The most commonly used arterial tortuosity measure is the distance factor metric (DFM). This study tested a DFM based measurement’s ability to detect increases in arterial tortuosity of hypertensives using existing images. Existing images presented challenges such as different resolutions which may affect the tortuosity measurement, different depths of the area imaged, and different artifacts of imaging that require filtering.</p> <p>Methods</p> <p>The stability and accuracy of alternative centerline algorithms was validated in numerically generated models and test brain MRA data. Existing images were gathered from previous studies and clinical medical systems by manually reading electronic medical records to identify hypertensives and negatives. Images of different resolutions were interpolated to similar resolutions. Arterial tortuosity in MRA images was measured from a DFM curve and tested on numerically generated models as well as MRA images from two hypertensive and three negative control populations. Comparisons were made between different resolutions, different filters, hypertensives versus negatives, and different negative controls.</p> <p>Results</p> <p>In tests using numerical models of a simple helix, the measured tortuosity increased as expected with more tightly coiled helices. Interpolation reduced resolution-dependent differences in measured tortuosity. The Korean hypertensive population had significantly higher arterial tortuosity than its corresponding negative control population across multiple arteries. In addition one negative control population of different ethnicity had significantly less arterial tortuosity than the other two.</p> <p>Conclusions</p> <p>Tortuosity can be compared between images of different resolutions by interpolating from lower to higher resolutions. Use of a universal negative control was not possible in this study. The method described here detected elevated arterial tortuosity in a hypertensive population compared to the negative control population and can be used to study this relation in other populations.</p

Doctor of Philosophy

dissertationHigh arterial tortuosity, or twistedness, is a sign of many vascular diseases. Some ocular diseases are clinically diagnosed in part by assessment of increased tortuosity of ocular blood vessels. Increased arterial tortuosity is seen in other vascular diseases but is not commonly used for clinical diagnosis. This study develops the use of existing magnetic resonance angiography (MRA) image data to study arterial tortuosity in a range of arteries of hypertensive and intracranial aneurysm patients. The accuracy of several centerline extraction algorithms based on Dijkstra's algorithm was measured in numeric phantoms. The stability of the algorithms was measured in brain arteries. A centerline extraction algorithm was selected based on its accuracy. A centerline tortuosity metric was developed using a curve of tortuosity scores. This tortuosity metric was tested on phantoms and compared to observer-based tortuosity rankings on a test data set. The tortuosity metric was then used to measure and compare with negative controls the tortuosity of brain arteries from intracranial aneurysm and hypertension patients. A Dijkstra based centerline extraction algorithm employing a distance-from-edge weighted center of mass (DFE-COM) cost function of the segmented arteries was selected based on generating 15/16 anatomically correct centerlines in a looping artery iv compared to 15/16 for the center of mass (COM) cost function and 7/16 for the inverse modified distance from edge cost function. The DFE-COM cost function had a lower root mean square error in a lopsided phantom (0.413) than the COM cost function (0.879). The tortuosity metric successfully ordered electronic phantoms of arteries by tortuosity. The tortuosity metric detected an increase in arterial tortuosity in hypertensive patients in 13/13 (10/13 significant at α = 0.05). The metric detected increased tortuosity in a subset of the aneurysm patients with Loeys-Dietz syndrome (LDS) in 7/7 (three significant at α = 0.001). The tortuosity measurement combination of the centerline algorithm and the distance factor metric tortuosity curve was able to detect increases in arterial tortuosity in hypertensives and LDS patients. Therefore the methods validated here can be used to study arterial tortuosity in other hypertensive population samples and in genetic subsets related to LDS

Machine learning approaches for early prediction of hypertension.

Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery pressure to diagnose hypertension after its onset. However, this method cannot detect cerebrovascular alterations that lead to adverse events which may occur prior to the onset of hypertension. The early detection and quantification of these cerebral vascular structural changes could help in predicting patients who are at a high risk of developing hypertension as well as other cerebral adverse events. This may enable early medical intervention prior to the onset of hypertension, potentially mitigating vascular-initiated end-organ damage. The goal of this dissertation is to develop a novel efficient noninvasive computer-aided diagnosis (CAD) system for the early prediction of hypertension. The developed CAD system analyzes magnetic resonance angiography (MRA) data of human brains gathered over years to detect and track cerebral vascular alterations correlated with hypertension development. This CAD system can make decisions based on available data to help physicians on predicting potential hypertensive patients before the onset of the disease

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

© 2019 The Authors Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events

Evaluating retinal blood vessels' abnormal tortuosity in digital image fundus

Abnormal tortuosity of retinal blood vessels is one of the early indicators of a number of vascular diseases. Therefore early detection and evaluation of this phenomenon can provide a window for early diagnosis and treatment. Currently clinicians rely on a qualitative gross scale to estimate the degree of vessel tortuosity. There have been many attempts to develop an accurate automated measure of tortuosity, yet it seems that none of these measures has gained universal acceptance. This can be attributed to the fact that descriptions and de�nitions of retinal vessel tortuosity are ambiguous and non-standard. In addition uni�ed public datasets for di�erent disease are not regularly available. I have propose a tortuosity evaluation framework in order to quantify the tortuosity of arteries and veins in two dimensional colour fundus images. The quanti�cation methods within the framework include retinal vessel morphology analysis based on the measurements of 66 features of blood vessels. These features are grouped as follows: 1) Structural properties 2) Distance approach features 3) Curvature approach features 4) Combined approach features 5) Signal approach features. The features numbered 1 to 4 above are derived from literature. Item number �ve are new features which I have proposed and developed in this thesis. These features have been evaluated using a manually graded retinal tortuosity dataset as controlled set. I have also built three tortuosity datasets, each of which contains two manual gradings. These datasets are: 1) A general tortuosity dataset 2) A diabetic retinopathy dataset 3) A hypertensive retinopathy dataset. In addition, I have investigated the di�erences in tortuosity patterns in hypertensive and diabetic retinopathy. New pathology based datasets were used in this investigation. These are the major contributions of this thesi

Retinal vessel analysis:flicker reproducibility, methodological standardisations and practical limitations

The Retinal Vessel Analyser (RVA) is a commercially available ophthalmoscopic instrument capable of acquiring vessel diameter fluctuations in real time and in high temporal resolution. Visual stimulation by means of flickering light is a unique exploration tool of neurovascular coupling in the human retina. Vessel reactivity as mediated by local vascular endothelial vasodilators and vasoconstrictors can be assessed non-invasively, in vivo. In brief, the work in this thesis • deals with interobserver and intraobserver reproducibility of the flicker responses in healthy volunteers • explains the superiority of individually analysed reactivity parameters over vendorgenerated output • links in static retinal measures with dynamic ones • highlights practical limitations in the use of the RVA that may undermine its clinical usefulness • provides recommendations for standardising measurements in terms of vessel location and vessel segment length and • presents three case reports of essential hypertensives in a -year follow-up. Strict standardisation of measurement procedures is a necessity when utilising the RVA system. Agreement between research groups on implemented protocols needs to be met, before it could be considered a clinically useful tool in detecting or predicting microvascular dysfunction

Arterial Tortuosity: An Imaging Biomarker of Childhood Stroke Pathogenesis?

Background and purposeArteriopathy is the leading cause of childhood arterial ischemic stroke. Mechanisms are poorly understood but may include inherent abnormalities of arterial structure. Extracranial dissection is associated with connective tissue disorders in adult stroke. Focal cerebral arteriopathy is a common syndrome where pathophysiology is unknown but may include intracranial dissection or transient cerebral arteriopathy. We aimed to quantify cerebral arterial tortuosity in childhood arterial ischemic stroke, hypothesizing increased tortuosity in dissection.MethodsChildren (1 month to 18 years) with arterial ischemic stroke were recruited within the Vascular Effects of Infection in Pediatric Stroke (VIPS) study with controls from the Calgary Pediatric Stroke Program. Objective, multi-investigator review defined diagnostic categories. A validated imaging software method calculated the mean arterial tortuosity of the major cerebral arteries using 3-dimensional time-of-flight magnetic resonance angiographic source images. Tortuosity of unaffected vessels was compared between children with dissection, transient cerebral arteriopathy, meningitis, moyamoya, cardioembolic strokes, and controls (ANOVA and post hoc Tukey). Trauma-related versus spontaneous dissection was compared (Student t test).ResultsOne hundred fifteen children were studied (median, 6.8 years; 43% women). Age and sex were similar across groups. Tortuosity means and variances were consistent with validation studies. Tortuosity in controls (1.346±0.074; n=15) was comparable with moyamoya (1.324±0.038; n=15; P=0.998), meningitis (1.348±0.052; n=11; P=0.989), and cardioembolic (1.379±0.056; n=27; P=0.190) cases. Tortuosity was higher in both extracranial dissection (1.404±0.084; n=22; P=0.021) and transient cerebral arteriopathy (1.390±0.040; n=27; P=0.001) children. Tortuosity was not different between traumatic versus spontaneous dissections (P=0.70).ConclusionsIn children with dissection and transient cerebral arteriopathy, cerebral arteries demonstrate increased tortuosity. Quantified arterial tortuosity may represent a clinically relevant imaging biomarker of vascular biology in pediatric stroke

Assessment of aortic stiffness in computed tomography : methodology of radiological examination from 2000 to 2020

Introduction: Vascular elasticity may be a predictive factor of various diseases. Although stiffening is thought to be a natural consequence of ageing, it can be accelerated by a number of pathological conditions such as hypertension, diabetes, or renal diseases. Aim of the study was to discuss the methodology used to assess aortic stiffness, with particular emphasis on radiological examination. Material and methods: The PubMed and Google Scholar databases were screened from inception to the year 2000 by 2 independent analysts initially working separately and then comparing their results. Results: Assessment of stiffness can be divided into methods not requiring computed tomography scan, such as tonometry of carotid femoral pulse wave velocity, bioelectrical impedance analysis, and cardio ankle vascular index, and methods requiring it, such as multidetector row computed tomography - ECG gated, in which indexes such as aortic distensibility, aortic stiffness, and aortic compliance can be obtained with simultaneous calcification evaluation based on the Agatston score. Discussion: Aortic stiffness was corelated with left ventricular afterload, prehypertension, coronary artery plaques, predic tion of coronary artery diseases, bone demineralization, chronic obstructive pulmonary diseases, and diabetes mellitus. Conclusions: Being a factor of various severe diseases, aortic stiffness may play an important role in the early detection of patients requiring additional medical care

Mri Assessment Of Maternal Uteroplacental Circulation In Pregnancy

Hypertensive pregnancy disorders (HPD) such as preeclampsia are highly associated with maternal vascular malperfusion of the placenta, an organ that exchanges nutrients and oxygen between the maternal circulation and the growing fetus. Adverse pregnancy outcomes are difficult to predict because there is insufficient understanding of how poor maternal arterial remodeling leads to disease. There is also a lack of reliable tools to evaluate these changes in early gestation. The hypothesis of this dissertation was that magnetic resonance imaging (MRI) could noninvasively evaluate uteroplacental function in vivo through a combination of arterial spin labeling (ASL), 4D flow, and time-of-flight (TOF) techniques which were already effective in the evalution of other cardiovascular diseases. These flow and perfusion imaging studies were conducted on human pregnant volunteers in their second and third trimesters at 1.5T. Many of them were also examined by conventional Doppler ultrasound (US) and followed through delivery. Flow-sensitive Alternating Inversion Recovery (FAIR) ASL MRI with background suppression was found to be feasible in detecting placental perfusion signal despite the presence of motion artifacts. An important consideration when studying placental ASL was the slow movement of maternal arterial blood in a large cavity called the intervillous space. This was a unique feature of placental anatomy which distinguished it from other organs containing capillaries. It became apparent that traditional models to estimate perfusion from MRI were no longer applicable. In this work, a statistical approach was first developed to filter out motion artifacts, followed by a coordinate transformation to better represent the lobular distribution of blood flow in the intervillous space of the placenta. The uterine arteries (UtAs) are the main maternal blood supply of the placenta and have also long been suspected to be involved in HPD, though US-based measurements have not yet been found to be highly predictive for widespread clinical use. In this work, 4D flow MRI enabled visualization of the tortuous UtAs while measuring volumetric flow rate. Its performance in predicting incidence of preeclampsia and small-for-gestational age births was comparable to Doppler US. When considering the innovative potential of 4D flow MRI to capture complex flow dynamics, this validation demonstrated the value of continuing technical development for improving HPD risk assessment. Furthermore, centerline extraction of the maternal pelvic arteries in TOF MRI, from the descending aorta to the UtAs and external iliac arteries, provided quantitative metrics to characterize the geometry including path length and curvature. Pulse wave velocity (PWV) was estimated using path length by TOF MRI and velocimetry by 2D phase contrast and 4D flow MRI with results showing sensitivity to differences between UtAs and external iliac arteries. These approaches provided physiological metrics to explore and characterize the remodeling process of the uteroplacental arteries. This dissertation demonstrates the feasibility of measuring structure and hemodynamics of the maternal vascular blood supply using non-contrast MRI that can lead to the more reliable biomarkers of adverse pregnancy outcomes needed to diagnose and treat HPD