Precise Tracking and Initial Segmentation of Abdominal Aortic Aneurysm

[[abstract]]In this paper we propose a mean-shift based technique for a precise tracking and segmentation of abdominal aortic aneurysm (AAA) from computed tomography (CT) angiography images. The proposed method applies median filter on the gradient of ray-length and linear interpolation for denoising. The segmentation result can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important for selection of appropriate stent graft device for treatment of AAA. Comparing to conventional approaches, our method is very efficient and can save a lot of manual labors.[[conferencetype]]國際[[conferencedate]]20131102~20131104[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Aizu-Wakamatsu, Japa

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system

Improving patient-specific assessments of regional aortic mechanics via quantitative magnetic resonance imaging with early applications in patients at elevated risk for thoracic aortopathy

Unstable aortic aneurysms and dissections are serious cardiovascular conditions associated with high mortality. The current gold standards for assessment of stability, however, rely on simple geometric measurements, like cross-sectional area or increased diameter between follow-up scans, and fail to incorporate information about underlying aortic mechanics. Displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) has been used previously to determine heterogeneous circumferential strain patterns in the aortas of healthy volunteers. Here, I introduce technical improvements to DENSE aortic analysis and early pilot application in patients at higher risk for the development of aortopathies. Modifications to the DENSE aortic postprocessing method involving the separate spatial smoothing of the inner and outer layers of the aortic wall allowed for the preservation of radial and shear strains without impacting circumferential strain calculations. The implementation of a semiautomatic segmentation approach utilizing the intrinsic kinematic information provided by DENSE MRI reduced lengthy post-processing times while generating circumferential strain distributions with good agreement to a manually generated benchmark. Finally, a new analysis pipeline for the combined use and spatial correlation of 4D phase-contrast MRI alongside DENSE MRI to quantify both regional fluid and solid mechanics in the descending aorta is explored in a limited pilot study

Contributions to Ensemble Classifiers with Image Analysis Applications

134 p.Ésta tesis tiene dos aspectos fundamentales, por un lado, la propuesta denuevas arquitecturas de clasificadores y, por otro, su aplicación a el análisis deimagen.Desde el punto de vista de proponer nuevas arquitecturas de clasificaciónla tesis tiene dos contribucciones principales. En primer lugar la propuestade un innovador ensemble de clasificadores basado en arquitecturas aleatorias,como pueden ser las Extreme Learning Machines (ELM), Random Forest (RF) yRotation Forest, llamado Hybrid Extreme Rotation Forest (HERF) y su mejoraAnticipative HERF (AHERF) que conlleva una selección del modelo basada enel rendimiento de predicción para cada conjunto de datos específico. Ademásde lo anterior, proveemos una prueba formal tanto del AHERF, como de laconvergencia de los ensembles de regresores ELMs que mejoran la usabilidad yreproducibilidad de los resultados.En la vertiente de aplicación hemos estado trabajando con dos tipos de imágenes:imágenes hiperespectrales de remote sensing, e imágenes médicas tanto depatologías específicas de venas de sangre como de imágenes para el diagnósticode Alzheimer. En todos los casos los ensembles de clasificadores han sido la herramientacomún además de estrategias especificas de aprendizaje activo basadasen dichos ensembles de clasificadores. En el caso concreto de la segmentaciónde vasos sanguíneos nos hemos enfrentado con problemas, uno relacionado conlos trombos del Aneurismas de Aorta Abdominal en imágenes 3D de tomografíacomputerizada y el otro la segmentación de venas sangineas en la retina. Losresultados en ambos casos en términos de rendimiento en clasificación y ahorrode tiempo en la segmentación humana nos permiten recomendar esos enfoquespara la práctica clínica.Chapter 1Background y contribuccionesDado el espacio limitado para realizar el resumen de la tesis hemos decididoincluir un resumen general con los puntos más importantes, una pequeña introducciónque pudiera servir como background para entender los conceptos básicosde cada uno de los temas que hemos tocado y un listado con las contribuccionesmás importantes.1.1 Ensembles de clasificadoresLa idea de los ensembles de clasificadores fue propuesta por Hansen y Salamon[4] en el contexto del aprendizaje de las redes neuronales artificiales. Sutrabajo mostró que un ensemble de redes neuronales con un esquema de consensogrupal podía mejorar el resultado obtenido con una única red neuronal.Los ensembles de clasificadores buscan obtener unos resultados de clasificaciónmejores combinando clasificadores débiles y diversos [8, 9]. La propuesta inicialde ensemble contenía una colección homogena de clasificadores individuales. ElRandom Forest es un claro ejemplo de ello, puesto que combina la salida de unacolección de árboles de decisión realizando una votación por mayoría [2, 3], yse construye utilizando una técnica de remuestreo sobre el conjunto de datos ycon selección aleatoria de variables.2CHAPTER 1. BACKGROUND Y CONTRIBUCCIONES 31.2 Aprendizaje activoLa construcción de un clasificador supervisado consiste en el aprendizaje de unaasignación de funciones de datos en un conjunto de clases dado un conjunto deentrenamiento etiquetado. En muchas situaciones de la vida real la obtenciónde las etiquetas del conjunto de entrenamiento es costosa, lenta y propensa aerrores. Esto hace que la construcción del conjunto de entrenamiento sea unatarea engorrosa y requiera un análisis manual exaustivo de la imagen. Esto se realizanormalmente mediante una inspección visual de las imágenes y realizandoun etiquetado píxel a píxel. En consecuencia el conjunto de entrenamiento esaltamente redundante y hace que la fase de entrenamiento del modelo sea muylenta. Además los píxeles ruidosos pueden interferir en las estadísticas de cadaclase lo que puede dar lugar a errores de clasificación y/o overfitting. Por tantoes deseable que un conjunto de entrenamiento sea construido de una manera inteligente,lo que significa que debe representar correctamente los límites de clasemediante el muestreo de píxeles discriminantes. La generalización es la habilidadde etiquetar correctamente datos que no se han visto previamente y quepor tanto son nuevos para el modelo. El aprendizaje activo intenta aprovecharla interacción con un usuario para proporcionar las etiquetas de las muestrasdel conjunto de entrenamiento con el objetivo de obtener la clasificación másprecisa utilizando el conjunto de entrenamiento más pequeño posible.1.3 AlzheimerLa enfermedad de Alzheimer es una de las causas más importantes de discapacidaden personas mayores. Dado el envejecimiento poblacional que es una realidaden muchos países, con el aumento de la esperanza de vida y con el aumentodel número de personas mayores, el número de pacientes con demencia aumentarátambién. Debido a la importancia socioeconómica de la enfermedad enlos países occidentales existe un fuerte esfuerzo internacional focalizado en laenfermedad del Alzheimer. En las etapas tempranas de la enfermedad la atrofiacerebral suele ser sutil y está espacialmente distribuida por diferentes regionescerebrales que incluyen la corteza entorrinal, el hipocampo, las estructuras temporaleslateral e inferior, así como el cíngulo anterior y posterior. Son muchoslos esfuerzos de diseño de algoritmos computacionales tratando de encontrarbiomarcadores de imagen que puedan ser utilizados para el diagnóstico no invasivodel Alzheimer y otras enfermedades neurodegenerativas.CHAPTER 1. BACKGROUND Y CONTRIBUCCIONES 41.4 Segmentación de vasos sanguíneosLa segmentación de los vasos sanguíneos [1, 7, 6] es una de las herramientas computacionalesesenciales para la evaluación clínica de las enfermedades vasculares.Consiste en particionar un angiograma en dos regiones que no se superponen:la región vasculares y el fondo. Basándonos en los resultados de dicha particiónse pueden extraer, modelar, manipular, medir y visualizar las superficies vasculares.Éstas estructuras son muy útiles y juegan un rol muy imporntate en lostratamientos endovasculares de las enfermedades vasculares. Las enfermedadesvasculares son una de las principales fuentes de morbilidad y mortalidad en todoel mundo.Aneurisma de Aorta Abdominal El Aneurisma de Aorta Abdominal (AAA)es una dilatación local de la Aorta que ocurre entre las arterias renal e ilíaca. Eldebilitamiento de la pared de la aorta conduce a su deformación y la generaciónde un trombo. Generalmente, un AAA se diagnostica cuando el diámetro anterioposteriormínimo de la aorta alcanza los 3 centímetros [5]. La mayoría delos aneurismas aórticos son asintomáticos y sin complicaciones. Los aneurismasque causan los síntomas tienen un mayor riesgo de ruptura. El dolor abdominalo el dolor de espalda son las dos principales características clínicas que sugiereno bien la reciente expansión o fugas. Las complicaciones son a menudo cuestiónde vida o muerte y pueden ocurrir en un corto espacio de tiempo. Por lo tanto,el reto consiste en diagnosticar lo antes posible la aparición de los síntomas.Imágenes de Retina La evaluación de imágenes del fondo del ojo es una herramientade diagnóstico de la patología vascular y no vascular. Dicha inspecciónpuede revelar hipertensión, diabetes, arteriosclerosis, enfermedades cardiovascularese ictus. Los principales retos para la segmentación de vasos retinianos son:(1) la presencia de lesiones que se pueden interpretar de forma errónea comovasos sanguíneos; (2) bajo contraste alrededor de los vasos más delgados, (3)múltiples escalas de tamaño de los vasos.1.5 ContribucionesÉsta tesis tiene dos tipos de contribuciones. Contribuciones computacionales ycontribuciones orientadas a una aplicación o prácticas.CHAPTER 1. BACKGROUND Y CONTRIBUCCIONES 5Desde un punto de vista computacional las contribuciones han sido las siguientes:¿ Un nuevo esquema de aprendizaje activo usando Random Forest y el cálculode la incertidumbre que permite una segmentación de imágenes rápida,precisa e interactiva.¿ Hybrid Extreme Rotation Forest.¿ Adaptative Hybrid Extreme Rotation Forest.¿ Métodos de aprendizaje semisupervisados espectrales-espaciales.¿ Unmixing no lineal y reconstrucción utilizando ensembles de regresoresELM.Desde un punto de vista práctico:¿ Imágenes médicas¿ Aprendizaje activo combinado con HERF para la segmentación deimágenes de tomografía computerizada.¿ Mejorar el aprendizaje activo para segmentación de imágenes de tomografíacomputerizada con información de dominio.¿ Aprendizaje activo con el clasificador bootstrapped dendritic aplicadoa segmentación de imágenes médicas.¿ Meta-ensembles de clasificadores para detección de Alzheimer conimágenes de resonancia magnética.¿ Random Forest combinado con aprendizaje activo para segmentaciónde imágenes de retina.¿ Segmentación automática de grasa subcutanea y visceral utilizandoresonancia magnética.¿ Imágenes hiperespectrales¿ Unmixing no lineal y reconstrucción utilizando ensembles de regresoresELM.¿ Métodos de aprendizaje semisupervisados espectrales-espaciales concorrección espacial usando AHERF.¿ Método semisupervisado de clasificación utilizando ensembles de ELMsy con regularización espacial

Computational fluid dynamics indicators to improve cardiovascular pathologies

In recent years, the study of computational hemodynamics within anatomically complex vascular regions has generated great interest among clinicians. The progress in computational fluid dynamics, image processing and high-performance computing haveallowed us to identify the candidate vascular regions for the appearance of cardiovascular diseases and to predict how this disease may evolve. Medicine currently uses a paradigm called diagnosis. In this thesis we attempt to introduce into medicine the predictive paradigm that has been used in engineering for many years. The objective of this thesis is therefore to develop predictive models based on diagnostic indicators for cardiovascular pathologies. We try to predict the evolution of aortic abdominal aneurysm, aortic coarctation and coronary artery disease in a personalized way for each patient. To understand how the cardiovascular pathology will evolve and when it will become a health risk, it is necessary to develop new technologies by merging medical imaging and computational science. We propose diagnostic indicators that can improve the diagnosis and predict the evolution of the disease more efficiently than the methods used until now. In particular, a new methodology for computing diagnostic indicators based on computational hemodynamics and medical imaging is proposed. We have worked with data of anonymous patients to create real predictive technology that will allow us to continue advancing in personalized medicine and generate more sustainable health systems. However, our final aim is to achieve an impact at a clinical level. Several groups have tried to create predictive models for cardiovascular pathologies, but they have not yet begun to use them in clinical practice. Our objective is to go further and obtain predictive variables to be used practically in the clinical field. It is to be hoped that in the future extremely precise databases of all of our anatomy and physiology will be available to doctors. These data can be used for predictive models to improve diagnosis or to improve therapies or personalized treatments.En els últims anys, l'estudi de l'hemodinàmica computacional en regions vasculars anatòmicament complexes ha generat un gran interès entre els clínics. El progrés obtingut en la dinàmica de fluids computacional, en el processament d'imatges i en la computació d'alt rendiment ha permès identificar regions vasculars on poden aparèixer malalties cardiovasculars, així com predir-ne l'evolució. Actualment, la medicina utilitza un paradigma anomenat diagnòstic. En aquesta tesi s'intenta introduir en la medicina el paradigma predictiu utilitzat des de fa molts anys en l'enginyeria. Per tant, aquesta tesi té com a objectiu desenvolupar models predictius basats en indicadors de diagnòstic de patologies cardiovasculars. Tractem de predir l'evolució de l'aneurisma d'aorta abdominal, la coartació aòrtica i la malaltia coronària de forma personalitzada per a cada pacient. Per entendre com la patologia cardiovascular evolucionarà i quan suposarà un risc per a la salut, cal desenvolupar noves tecnologies mitjançant la combinació de les imatges mèdiques i la ciència computacional. Proposem uns indicadors que poden millorar el diagnòstic i predir l'evolució de la malaltia de manera més eficient que els mètodes utilitzats fins ara. En particular, es proposa una nova metodologia per al càlcul dels indicadors de diagnòstic basada en l'hemodinàmica computacional i les imatges mèdiques. Hem treballat amb dades de pacients anònims per crear una tecnologia predictiva real que ens permetrà seguir avançant en la medicina personalitzada i generar sistemes de salut més sostenibles. Però el nostre objectiu final és aconseguir un impacte en l¿àmbit clínic. Diversos grups han tractat de crear models predictius per a les patologies cardiovasculars, però encara no han començat a utilitzar-les en la pràctica clínica. El nostre objectiu és anar més enllà i obtenir variables predictives que es puguin utilitzar de forma pràctica en el camp clínic. Es pot preveure que en el futur tots els metges disposaran de bases de dades molt precises de tota la nostra anatomia i fisiologia. Aquestes dades es poden utilitzar en els models predictius per millorar el diagnòstic o per millorar teràpies o tractaments personalitzats.Postprint (published version

Automatic classification and 3D visualisation of abdominal aortic aneurysms to predict aneurysm expansion

Abdominal aortic aneurysms (AAA) are a major cause of death in men above the age of 65 in the western world. Currently decisions for AAA management are based on the size of maximum AAA diameter (>5.5cm), measured using ultrasound imaging. However, as a proportion of AAAs rupture whilst still below this diameter threshold, while larger AAAs may never rupture, better methods for AAA expansion and rupture prediction are required. Previous research suggested that the presence of “hotspots” (focal areas) of inflammation as detected with USPIO-enhanced MRI may have potential in identifying faster-growing AAAs. However, the identification of these USPIO “hotspots” had been up to this point restricted to manual processing of the MRI data in a time-consuming and laborious slice-by-slice method, which only used 2D information. Inter- and intra- observer variability were an issue, as well as the use of empirically-defined signal thresholds which were dependent on each acquisition protocol. The work presented in this thesis aimed to evaluate current methodologies for AAA assessment and growth prediction and to contribute to improved prediction models by introducing novel techniques. Ultrasound was found to under-measure AAA size and the use of maximum AAA diameter was found to be problematic, especially for growth calculations. Automatically calculated alternatives which account for the total size and shape of the AAA, as measured with MRI, were introduced for more reproducible measurements. Furthermore, automation and standardisation of the previously-employed manual methods for hotspot detection and AAA classification were achieved, with the development of an efficient algorithm with excellent agreement levels. Taken a step further, two improved algorithms were introduced, adaptive to the data and USPIO distribution of individual AAAs and eliminating the universal threshold previously used. These algorithms incorporated information on 3D USPIO distribution along the length of the AAAs to detect and visualise 3D hotspots of inflammation for the first time. Novel 2D and 3D metrics were introduced, while the algorithms were also incorporated into a GUI for ease of clinical use. Additional aneurysm metrics automatically derived by the algorithms were incorporated into multiple linear regression models to investigate prediction of AAA growth rate. This investigation introduced three significant predictors which have not been used in previous predictive models of AAA expansion: the “mean thrombus major axis” metric, which reflected baseline size of AAA throughout multiple axial slices of the AAA; the “eccentricity WT” metric which reflected the relationship between wall shape and thrombus; and the presence of “3D hotspots” which may potentially reflect transported USPIO within a network of vascular channels along the length of the aneurysm. In line with previous literature, family history of AAA and high diastolic BP were also found to be significant predictors, but larger cohorts are needed for more reliable assessment of the predictive models suggested in this thesis

Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data

Vascular remodeling after endovascular treatment: quantitative analysis of medical images with a focus on aorta

In the last years, the convergence of advanced imaging techniques and endovascular procedures has revolutionized the practice of vascular surgery. However, regardless the anatomical district, several complications still occur after endovascular treatment and the impact of endovascular repair on vessel morphology remains unclear. Starting from this background, the aim of this thesis is to ll the gaps in the eld of vessel remodeling after endovascular procedure. Main focus of the work will be the repair of the aorta and, in particular thoracic and thoracoabdominal treatments. Furthermore an investigation of the impact of endovascular repair on femoro-popliteal arterial segment will be reported in the present work. Analyses of medical images will been conducted to extract anatomical geometric features and to compare the changes in morphology before treatment and during follow-up. After illustrating in detail the aims and the outline of the dissertation in Chapter 1, Chapter 2 will concern the anatomy and the physiology of the aorta along with the main aortic pathologies and the related surgical treatments. Subsequently, an overview of the medical image techniques for segmentation and vessel geometric quantication will be provided. Chapter 3 will introduce the concept of remodeling of the aorta after endovascular procedure. In particular, two types of aortic remodeling will be considered. On one side remodeling can be seen as the shrinkage of the aneurysmal sac or false lumen thrombosis. On the other side, aortic remodeling could be seen as the changes in the aortic morphology following endograft placement which could lead to complications. Chapter 4 will illustrate a study regarding the analysis of medical images to measure the geometrical changes in the pathological aorta during follow-up in patients with thoracoabdominal aortic aneurysms treated with endovascular procedure using a novel uncovered device, the Cardiatis Multilayer Flow Modulator. Chapter 5 will focus on the geometrical remodeling of the aortic arch and descending aorta in patients who underwent hybrid arch treatment to treat thoracic aneurysms. The goal of the work is to develop a pipeline for the processing of pre-operative and post-operative Computed Tomography images in order to detect the changes in the aortic arch physiological curvature due to endograft insertion. Chapter 6 will focuse on the use of 3D printing technology as valuable tool to support patient's follow-up. In particular, we report a case of a patient originally treated with endovascular procedure for type B aortic dissection and which experimented several complications during follow-up. 3D printing technology is used to show the remodeling of the aortic vasculature during time. Chapter 7 will concern patient-specic nite element simulations of aortic endovascular procedure. In particular, starting from a clinical case where complication developed during followup, the predictive value of computational simulations will be shown. Chapter 8 will illustrate a study concerning the evaluation of morphological changes of the femoro-popliteal arterial segment due to limb exion in patients undergoing endovascular treatment of popliteal artery aneurysms

Molecular imaging of abdominal aortic aneurysms

Abdominal aortic aneurysm (AAA) disease is characterised by an asymptomatic, permanent, focal dilatation of the abdominal aorta progressing towards rupture, which confers significant mortality. Patient management and surgical decisions currently rely on aortic diameter measurements via abdominal ultrasound screening. However, AAA rupture can occur at small diameters or may never occur at large diameters. Therefore, there is a need to develop molecular imaging-based biomarkers independent of aneurysm diameter that may help stratify patients with early-stage AAA to reduced surveillance. AAA uptake of [18F]fluorodeoxyglucose on positron emission tomography (PET) has been demonstrated previously; however, its glucose-dependent uptake may overlook other key mechanisms. The cell proliferation marker [18F]fluorothymidine ([18F]FLT) is primarily used in tumour imaging. The aim of the overall study for this thesis was to explore the feasibility of [18F]FLT PET / computed tomography (CT) to visualise and quantify AAA in the angiotensin II (AngII)-infused mouse model. The experiments presented in this thesis revealed increased uptake of [18F]FLT in the 14-day AngII AAA model than in saline controls, followed by a decrease in this uptake at 28 days. Moreover, in line with the in vivo PET/CT findings, Western blotting of aortic tissue revealed increased levels of thymidine kinase-1 (the substrate of [18F]FLT) and nucleoside transporters in the 14-day AngII AAA model than in saline controls, followed by decreased expression levels at 28 days. A pilot experiment further demonstrated that [18F]FLT PET/CT could be used to detect an early therapeutic response to oral imatinib treatment in the AngII AAA model. Therefore, [18F]FLT PET/CT may be a feasible modality to detect and quantify cell proliferation in the AngII AAA murine model. The findings of this thesis are encouraging for the application of [18F]FLT PET/CT in patients with small AAA