Spelling suggestions: "subject:"cerebral aneurysm"" "subject:"cerebral neurysm""
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Particle image velocimetry measurements of blood flow in aneurysms using 3D printed flow phantomsTshimanga, Ilunga Jeanmark 11 1900 (has links)
Cardiovascular diseases (CVD) remain one of the leading causes of deaths worldwide. The formation and
presence of aneurysm is a very important question in the study of this CVDs. An aneurysm is a balloon-like
bulge on a blood vessel which forms over time. An aneurysm is usually considered to be a result of weakening
of the blood vessel walls, this definition has stood over many years without being conclusively proven.
Eventually, the aneurysm could clot or burst due to degradation of the aneurysm wall and accumulation of
blood. The latter would lead to internal bleeding and result in a stroke. Local hemodynamics have been found
to be very important in the study of the evolution of an aneurysm. In this study, a steady flow experimental
investigation was conducted using planar Particle Image Velocimetery (PIV) on a rigid flow phantom of an
idealised geometry consisting of a curve parent artery and a spherical aneurysm located on the outer convex
side of the curvature. The flow phantom was fabricated directly using a commercially available desktop
Stereolithography (STL) 3D printer instead of the more conventional investment casting method using a core.
Although 3D printing technologies have been around for many years, the fabrication of flow phantoms by
direct printing is still largely under-explored. This thesis details the results of investigation into the optimal
printing and post-printing procedures required to produce a flow phantom of suitable clarity and transparency.
Other important areas of concern such as the geometric accuracy, surface topography and refractive index of
the final model are also investigated. A planar PIV is conducted to study the impact of flow rates on the local
flow field in and around the aneurysm and their impact on the wall shear stress. It was found that direct 3D
printing is appropriate for the fabrication of flow phantoms suitable for PIV or other flow visualisation
techniques. It reduces the complexities and time needed compared to the conventional investment casting
methods. It was observed that the optical properties of the printed material such as the high refractive index
(RI) and the transmittivity of light could cause a problem in large models. From the PIV measurements it was
found that flow rates affect the flow field in both the parent artery and the aneurysm. First, high velocities
were observed on the outer curvature of the parent artery. Secondly the centre of rotation in the aneurysm is
not at the geometric centre but is displaced slightly in the direction of the flow. Finally, the flow rate affects
the angle in which flow enters the aneurysm from the parent vessel. This change in the flow angle affects the
flow within the aneurysm. A higher flow rate in the parent artery increases the incident angle which brings the
centre of rotation closer to the geometric centre of the aneurysm, this changes the location and magnitude of
high velocities and hence the local wall shear stress (WSS) on the wall of the aneurysm. This may have
implications in the evolution of aneurysms. / Mechanical and Industrial Engineering
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Data Augmentation and Enhancement for Cardiovascular 4D Flow MRIJiacheng Zhang (12455544) 25 April 2022 (has links)
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<p>Cerebral aneurysms are presented in 3-5% of the population and account for approximately 10% of all strokes. The clinical decision on treating unruptured aneurysms should not be taken lightly because a majority of the asymptomatic cerebral aneurysm will not rupture, while both endovascular and microsurgical treatments carry the risk of morbidity and mortality. Thus, there is a need for objective risk assessment to reliably predict the high-risk aneurysms to intervene. Recent studies have found that the blood flow hemodynamic metrics such as pressure and wall shear stress (WSS) are related to the growth and rupture of the aneurysms. 4D flow magnetic resonance imaging (MRI) measures time-resolved three-dimensional velocity fields in the aneurysms <em>in vivo</em>, allowing for the evaluation of hemodynamic parameters. This work presents the developments of flow-physics constrained data enhancement and augmentation methods for 4D flow MRI to assist the risk stratification of cerebral aneurysms. First, a phase unwrapping and denoising method is introduced to enhance the dynamic range and accuracy of 4D flow MRI velocity measurement by incorporating the divergence-free constraint of incompressible flow. Moreover, methods are developed to improve the estimation of hemodynamic parameters from 4D flow data including pressure and WSS. The pressure reconstruction method is also applied to the flow data acquired using particle imaging velocimetry (PIV) and particle tracking velocimetry (PTV) and shows superior performance as compared to the existing methods by solving the pressure Poisson equation. We also proposed a framework to estimate the uncertainty of the PIV/PTV based pressure estimation by propagating the velocity uncertainty. In addition, a multi-modality approach is introduced to enhances the resolution and accuracy of 4D flow data with sparse representation, which improves the reliability of the hemodynamic evaluation. Finally, we present a method to measure the left ventricular flow propagation velocity from cardiac imaging to help in assessing the diastolic function. </p>
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Etude de la méthode de Boltzmann sur réseau pour la segmentation d'anévrismes cérébraux / Study of the lattice Boltzmann method application to cerebral aneurysm segmentationWang, Yan 25 July 2014 (has links)
L'anévrisme cérébral est une région fragile de la paroi d'un vaisseau sanguin dans le cerveau, qui peut se rompre et provoquer des saignements importants et des accidents vasculaires cérébraux. La segmentation de l'anévrisme cérébral est une étape primordiale pour l'aide au diagnostic, le traitement et la planification chirurgicale. Malheureusement, la segmentation manuelle prend encore une part importante dans l'angiographie clinique et elle est devenue couteuse en temps de traitement étant donné la gigantesque quantité de données générées par les systèmes d'imagerie médicale. Les méthodes de segmentation automatique d'image constituent un moyen essentiel pour faciliter et accélérer l'examen clinique et pour réduire l'interaction manuelle et la variabilité inter-opérateurs. L'objectif principal de ce travail de thèse est de développer des méthodes automatiques pour la segmentation et la mesure des anévrismes. Le présent travail de thèse est constitué de trois parties principales. La première partie concerne la segmentation des anévrismes géants qui contiennent à la fois la lumière et le thrombus. La méthode consiste d'abord à extraire la lumière et le thrombus en utilisant une procédure en deux étapes, puis à affiner la forme du thrombus à l'aide de la méthode des courbes de niveaux. Dans cette partie, la méthode proposée est également comparée à la segmentation manuelle, démontrant sa bonne précision. La deuxième partie concerne une approche LBM pour la segmentation des vaisseaux dans des images 2D+t et de l'anévrisme cérébral dans les images en 3D. La dernière partie étudie un modèle de segmentation 4D en considérant les images 3D+t comme un hypervolume 4D et en utilisant un réseau LBM D4Q81, dans lequel le temps est considéré de la même manière que les trois autres dimensions pour la définition des directions de mouvement des particules dans la LBM, considérant les données 3D+t comme un hypervolume 4D et en utilisant un réseau LBM D4Q81. Des expériences sont réalisées sur des images synthétiques d'hypercube 4D et d'hypersphere 4D. La valeur de Dice sur l'image de l'hypercube avec et sans bruit montre que la méthode proposée est prometteuse pour la segmentation 4D et le débruitage. / Cerebral aneurysm is a fragile area on the wall of a blood vessel in the brain, which can rupture and cause major bleeding and cerebrovascular accident. The segmentation of cerebral aneurysm is a primordial step for diagnosis assistance, treatment and surgery planning. Unfortunately, manual segmentation is still an important part in clinical angiography but has become a burden given the huge amount of data generated by medical imaging systems. Automatic image segmentation techniques provides an essential way to easy and speed up clinical examinations, reduce the amount of manual interaction and lower inter operator variability. The main purpose of this PhD work is to develop automatic methods for cerebral aneurysm segmentation and measurement. The present work consists of three main parts. The first part deals with giant aneurysm segmentation containing lumen and thrombus. The methodology consists of first extracting the lumen and thrombus using a two-step procedure based on the LBM, and then refining the shape of the thrombus using level set technique. In this part the proposed method is also compared with manual segmentation, demonstrating its good segmentation accuracy. The second part concerns a LBM approach to vessel segmentation in 2D+t images and to cerebral aneurysm segmentation in 3D medical images through introducing a LBM D3Q27 model, which allows achieving a good segmentation and high robustness to noise. The last part investigates a true 4D segmentation model by considering the 3D+t data as a 4D hypervolume and using a D4Q81 lattice in LBM where time is considered in the same manner as for other three dimensions for the definition of particle moving directions in the LBM model.
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Hemodynamic investigation and thrombosis modeling of intracranial aneurysms / Etude hémodynamique dans les anévrismes intracrâniens et modélisation de la thromboseZhang, Yue 25 September 2015 (has links)
La sélection de variables est une tâche primordiale en fouille de données et apprentissage automatique. Il s’agit d’une problématique très bien connue par les deux communautés dans les contextes, supervisé et non-supervisé. Le contexte semi-supervisé est relativement récent et les travaux sont embryonnaires. Récemment, l’apprentissage automatique a bien été développé à partir des données partiellement labélisées. La sélection de variables est donc devenue plus importante dans le contexte semi-supervisé et plus adaptée aux applications réelles, où l’étiquetage des données est devenu plus couteux et difficile à obtenir. Dans cette thèse, nous présentons une étude centrée sur l’état de l’art du domaine de la sélection de variable en s’appuyant sur les méthodes qui opèrent en mode semi-supervisé par rapport à celles des deux contextes, supervisé et non-supervisé. Il s’agit de montrer le bon compromis entre la structure géométrique de la partie non labélisée des données et l’information supervisée de leur partie labélisée. Nous nous sommes particulièrement intéressés au «small labeled-sample problem» où l’écart est très important entre les deux parties qui constituent les données. / Feature selection is an important task in data mining and machine learning processes. This task is well known in both supervised and unsupervised contexts. The semi-supervised feature selection is still under development and far from being mature. In general, machine learning has been well developed in order to deal with partially-labeled data. Thus, feature selection has obtained special importance in the semi-supervised context. It became more adapted with the real world applications where labeling process is costly to obtain. In this thesis, we present a literature review on semi-supervised feature selection, with regard to supervised and unsupervised contexts. The goal is to show the importance of compromising between the structure from unlabeled part of data, and the background information from their labeled part. In particular, we are interested in the so-called «small labeled-sample problem» where the difference between both data parts is very important.
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Vliv okolní tkáně na napjatost výdutě mozkových tepen / Influence of the surrounding tissue on the stresses in brain arterial aneurysmsLipenský, Zdeněk January 2012 (has links)
This thesis is focused on stress in brain aneurysms. It consists of three parts. First part is aimed for gaining information about the topic from scientific resources. Next part consists of analyses of geometry of cerebral aneurysms on the computed wall stress. Analyses are performed on four basic geometrical models and results are being discussed. The risky areas in each investigated shape have been identified as well as the comparisons of stress between those shapes have been performed and the most dangerous shape among investigated shapes has been determined. Third part investigates the influence of surrounding tissue on the brain aneurysm. Conclusion of this thesis is that brain gray tissue has positive yet negligible effect on the computed wall stress.
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