Numerical Insights for AAA Growth Understanding and Predicting: Morphological and Hemodynamic Risk Assessment Features and Transient Coherent Structures Uncovering

Les anévrismes de l'aorte abdominale (AAA) sont des dilatations localisées et fréquentes de l'aorte. En cas de rupture, seul un traitement immédiat peut prévenir la morbidité et la mortalité. Le diamètre maximal AAA ($D_{max}$) et la croissance sont les paramètres actuels pour évaluer le risque associé et planifier l'intervention, avec des seuils inférieurs pour les femmes. Cependant, ces critères ne sont pas personnalisés ; la rupture peut se produire à un diamètre inférieur et les patients vivre avec un AAA important. Si l'on sait que la maladie est associée à une modification de la morphologie et de la circulation sanguine, à un dépôt de thrombus intra-luminal et à des symptômes cliniques, les mécanismes de croissance ne sont pas encore entièrement compris. Dans cette étude longitudinale, une analyse morphologique et des simulations de flux sanguins sont effectuées et comparées aux sujets témoins chez 32 patients ayant reçu un diagnostic clinique d'AAA et au moins 3 tomodensitogrammes de suivi par patient. L'objectif est d'abord d'examiner quels paramètres stratifient les patients entre les groupes sains, à faible risque et à risque élevé. Les corrélations locales entre les paramètres hémodynamiques et la croissance de l'AAA sont également explorées, car la croissance hétérogène de l'AAA n'est actuellement pas comprise. Enfin, les paramètres composites sont construits à partir de données cliniques, morphologiques et hémodynamiques et de leur capacité à prédire si un patient sera soumis à un test de risque. La performance de ces modèles construits à partir de l'apprentissage supervisé est évaluée par les ROC AUC : ils sont respectivement de 0.73 ± 0.09, 0.93 ± 0.08 et 0.96 ± 0.10 . En incorporant tous les paramètres, on obtient une AUC de 0.98 ± 0.06. Pour mieux comprendre les interactions entre la croissance et la topologie de l'écoulement de l'AAA, on propose un worflow spécifique au patient pour calculer les exposants de Lyapunov en temps fini et extraire les structures lagrangiennes-cohérentes (SLC). Ce modèle de calcul a d'abord été comparé à l'imagerie par résonance magnétique (IRM) par contraste de phase 4-D chez 5 patients. Pour mieux comprendre l'impact de la topologie de l'écoulement et du transport sur la croissance de l'AAA, des SLC hyperboliques répulsives ont été calculées chez un patient au cours d'un suivi de 8 ans, avec 9 mesures morphologiques volumétriques de l'AAA par tomographie-angiographie. Les SLC ont défini les frontières du jet entrant dans l'AAA. Les domaines situés entre le SLC et le mur aortique ont été considérés comme des zones de stagnation. Leur évolution a été étudiée lors de la croissance de l'AAA. En plus des SLC hyperboliques (variétés attractives et répulsives) découvertes par FTLE, les SLC elliptiques ont également été considérées. Il s'agit de régions dominées par la rotation, ou tourbillons, qui sont de puissants outils pour comprendre les phénomènes de transport dans les AAA.Abdominal aortic aneurysms (AAA) are localized, commonly-occurring dilations of the aorta. In the event of rupture only immediate treatment can prevent morbidity and mortality. The AAA maximal diameter ($D_{max}$) and growth are the current metrics to evaluate the associated risk and plan intervention, with lower thresholds for women. However, these criteria lack patient specificity; rupture may occur at lower diameter and patients may live with large AAA. If the disease is known to be associated with altered morphology and blood flow, intra-luminal thrombus deposit and clinical symptoms, the growth mechanisms are yet to be fully understood. In this longitudinal study, morphological analysis and blood flow simulations for 32 patients with clinically diagnosed AAA and at least 3 follow-up CT-scans per patient, are performed and compared to control subjects. The aim is first to investigate which metrics stratify patients between healthy, low risk and high risk groups. Local correlations between hemodynamical metrics and AAA growth are also explored, as AAA heterogeneous growth is currently not understood. Finally, composite metrics are built from clinical, morphological, and hemodynamical data, and their ability to predict if a patient will become at risk tested. Performance of these models built from supervised learning is assessed by ROC AUCs: they are respectively, 0.73 ± 0.09, 0.93 ± 0.08 and 0.96 ± 0.10. Mixing all metrics, an AUC of 0.98 ± 0.06 is obtained. For further insights into AAA flow topology/growth interaction, a workout of patient-specific computational flow dynamics (CFD) is proposed to compute finite-time Lyapunov exponents and extract Lagrangian-coherent structures (LCS). This computational model was first compared with 4-D phase-contrast magnetic resonance imaging (MRI) on 5 patients. To better understand the impact of flow topology and transport on AAA growth, hyperbolic, repelling LCS were computed in 1 patient during 8-years follow-up, including 9 volumetric morphologic AAA measures by computed tomography-angiography (CTA). LCS defined barriers to Lagrangian jet cores entering AAA. Domains enclosed between LCS and the aortic wall were considered to be stagnation zones. Their evolution was studied during AAA growth. In addition to hyperbolic (attracting and repelling) LCS uncovered by FTLE, elliptic LCS were also considered. Those encloses rotation-dominated regions, or vortices, which are powerful tools to understand the flow transport in AAA

Morphologic evaluation of ruptured abdominal aortic aneurysm by 3D modeling

This thesis was created in Word and converted to PDF using Mac OS X 10.7.5 Quartz PDFContext.Abdominal aortic aneurysm (AAA) is defined as a dilatation of the abdominal aorta exceeding the normal diameter by more than 50%. The standard and widely used approach to assess AAA size is by measuring the maximal diameter (Dmax). Currently, the main predictors of rupture risk are the Dmax, sex, and the expansion rate of the aneurysm. Yet, Dmax has some limitations. AAAs of vastly different shapes may have the same maximal diameter. Dmax lacks sensitivity for rupture risk, especially among smaller AAAs. Thus, there is a need to evaluate the susceptibility of a given AAA to rupture on a patient-specific basis. We present the design concept and workflow of the AAA segmentation software developed at our institution. We describe the previous validation steps in which we evaluated the reproducibility of manual Dmax, compared software Dmax against manual Dmax, validated reproducibility of software Dmax and volume in cross-sectional and longitudinal studies for detection of AAA growth, and evaluated the reproducibility of software measurements in unenhanced computed tomographic angiography (CTA) and in the presence of stent-graft. In order to define new geometric features associated with rupture, we performed a case-control study in which we compared 63 cases with ruptured or symptomatic AAA and 94 controls with asymptomatic AAA. Univariate logistic regression analysis revealed 14 geometric indices associated with AAA rupture. In the multivariate logistic regression analysis, adjusting for Dmax and sex, the AAA with a higher bulge location and higher mean averaged surface area were associated with AAA rupture. Our preliminary results suggest that incorporating geometrical indices obtained by segmentation of CT shows a trend toward improvement of the classification accuracy of AAA with high rupture risk at CT over a traditional model based on Dmax and sex alone. Larger longitudinal studies are needed to verify the validity of the proposed model. Addition of flow and biomechanical simulations should be investigated to improve rupture risk prediction based on AAA modeling.Un anévrysme de l'aorte abdominale (AAA) est défini par une dilatation de plus de 50% par rapport au diamètre normal. La méthode standard et largement répandue pour mesurer la dimension d'un AAA consiste à mesurer le diamètre maximal (Dmax). Présentement, les principaux prédicteurs de risque de rupture sont le Dmax, le sexe et le taux d'expansion d'un anévrysme. Toutefois, le Dmax a certaines limitations. Des AAAs de formes très différentes peuvent avoir le même diamètre maximal. Le Dmax manque de sensibilité pour détecter le risque de rupture, en particulier pour les petits anévrysmes. Par conséquent, il y a un besoin d'évaluer de manière spécifique et individuelle la susceptibilité de rupture d'un AAA. Nous présentons le concept et le flux de travail d'un logiciel de segmentation des AAAs développé à notre institution. Nous décrivons les étapes antérieures de validation: évaluation de la reproductibilité du Dmax manuel, comparaison de Dmax par logiciel avec Dmax manuel, validation de la reproductibilité du Dmax et volume par logiciel dans des études transversale et longitudinale pour la détection de croissance et évaluation de la reproductibilité de mesures sur angiographie par tomodensitométrie et en présence d'endoprothèse. En vue d’identifier de nouveaux paramètres géométrique associés avec le risque de rupture, nous avons réalisé une étude cas-témoin comparant 63 cas avec AAA rompu ou symptomatique et 94 contrôles avec AAA asymptomatique. Une analyse de régression logistique univariée a identifié 14 indices géométriques associés avec une rupture de AAA. Dans l'analyse de régression logistique multivariée, en ajustant pour le Dmax et le sexe, les AAA avec un bombement plus haut situé et une surface moyenne plus élevée étaient associés à une rupture. Nos résultats préliminaires suggèrent que l'inclusion d'indices géométriques obtenus par segmentation de tomodensitométrie tend à améliorer la classification de AAA avec un risque de rupture par rapport à un modèle traditionnel seulement basé sur le Dmax et le sexe. De plus larges études longitudinales sont requises pour vérifier la validité du modèle proposé. Des simulations de flux et biomécaniques devraient être envisagées pour améliorer la prédiction du risque de rupture basée sur la modélisation d'anévrysmes

Design of a comprehensive modeling, characterization, rupture risk assessment and visualization pipeline for Abdominal Aortic Aneurysms

Abdominal aortic aneurysms (AAA) is a dilation of the abdominal aorta, typically within the infra-renal segment of the vessel that cause an expansion of at least 1.5 times the normal vessel diameter. It is becoming a leading cause of death in the United States and around the world, and consequentially, in 2009, the Society for Vascular Surgery (SVS) practice guidelines expressed the critical need to further investigate the factors associated with the risk of AAA rupture, along with potential treatment methods. For decades, the maximum diameter (Dmax) was introduced as the main parameter used to assess AAA behavior and its rupture risk. However, it has been shown that three main categories of parameters including geometrical indices, such as the maximum transverse diameter, biomechanical parameters, such as material properties, and historical clinical parameters, such as age, gender, hereditary history and life-style affect AAA and its rupture risk. Therefore, despite all efforts that have been undertaken to study the relationship among different parameters affecting AAA and its rupture, there are still limitations that require further investigation and modeling; the challenges associated with the traditional, clinical quality images represent one class of these limitations. The other limitation is the use of the homogenous hyper-elastic material property model to study the entire AAA, when, in fact, there is evidence that different degrees of degradation of the elastin and collagen network of the AAA wall lead to different regions of the AAA exhibiting different material properties, which, in turn, affect its biomechanical behavior and rupture. Moreover, the effects of all three main categories of parameters need to be considered simultaneously and collectively when studying the AAAs and their rupture, so once again, the field can further benefit from such studies. Therefore, in this work, we describe a comprehensive pipeline consisting of three main components to overcome some of these existing limitations. The first component of the proposed method focuses on the reconstruction and analysis of both synthetic and human subject-specific 3D models of AAA, accompanied by a full geometric parameter analysis and their effects on wall stress and peak wall stress. The second component investigates the effect of various biomechanical parameters, specifically the use of various homogeneous and heterogeneous material properties to model the behavior of the AAA wall. To this extent, we introduce two different patient-specific regional material property models to better mimic the physiological behavior of the AAA wall. Finally, the third component utilizes machine learning methods to develop a comprehensive predictive model that incorporates the effect of the geometrical, biomechanical and historical clinical data to predict the rupture severity of AAA in a patient-specific manner. This is the first comprehensive semi-automated method developed for the assessment of AAA. Our findings illustrate that using a regional material property model that mimics the realistic heterogeneity of the vessel’s wall leads to more reliable and accurate predictions of AAA severity and associated rupture risk. Additionally, our results indicate that using only Dmax as an indicator for the rupture risk is insufficient, while a combination of parameters from different sources along with PWS could serve as a more reliable rupture assessment. These methods can help better characterize the severity of AAAs, better predict their associated rupture risk, and, in turn, help clinicians with earlier, patient-customized diagnosis and patient-customized treatment planning approaches, such as stent grafting

A cohort longitudinal study identifies morphology and hemodynamics predictors of abdominal aortic aneurysm growth

International audienceAbdominal aortic aneurysms (AAA) are localized, commonly occurring aortic dilations. Following rupture only immediate treatment can prevent morbidity and mortality. AAA maximal diameter and growth are the current metrics to evaluate the associated risk and plan intervention. Although these criteria alone lack patient specificity, predicting their evolution would improve clinical decision. If the disease is known to be associated with altered morphology and blood flow, intraluminal thrombus deposit and clinical symptoms, the growth mechanisms are yet to be fully understood. In this retrospective longitudinal study of 138 scans, morphological analysis and blood flow simulations for 32 patients with clinically diagnosed AAAs and several follow-up CT-scans, are performed and compared to 9 control subjects. Several metrics stratify patients between healthy, low and high risk groups. Local correlations between hemodynamic metrics and AAA growth are also explored but due to their high inter-patient variability, do not explain AAA heterogeneous growth. Finally, high-risk predictors trained with successively clinical, morphological, hemodynamic and all data, and their link to the AAA evolution are built from supervise learning. Predictive performance is high for morphological, hemodynamic and all data, in contrast to clinical data. The morphology-based predictor exhibits an interesting effort-predictability tradeoff to be validated for clinical translation

Distributed quantitative evaluation of 3D patient specific arterial models

In this paper we describe a new system for the 3D reconstruction and distribution on the net of models for vessels structures. The system is specifically designed to support measurements of medical interest. We describe 2D and 3D segmentation methods implemented and the procedure used to build interactive VRML97 models. The experimental section presents a comparison between segmentation methods, and a first application to surgical planning for endovascular repair of Abdominal Aortic Aneurysms

2-D and 3-D high frame-rate Pulse Wave Imaging for the characterization of focal vascular disease

Cardiovascular diseases are major causes of morbidity and mortality in Western-style populations. Atherosclerosis and Abdominal Aortic Aneurysms (AAAs) are two prevalent vascular diseases that may progress without symptoms and contribute to acute cardiovascular events such as stroke and AAA rupture, which are consistently among the leading causes of death worldwide. The imaging methods used in the diagnosis of these diseases, have been reported to present several limitations. Given that both are associated with mechanical changes in the arterial wall, imaging of the arterial mechanical properties may improve early disease detection and patient care. Pulse wave velocity (PWV) refers to the velocity at which arterial waves generated by ventricular ejection travel along the arterial tree. PWV is a surrogate marker of arterial stiffness linked to cardiovascular mortality. The foot-to-foot method that is typically used to calculate PWV suffers from errors of distance measurements and time-delay measurements. Additionally, a single PWV estimate is provided over a relatively long distance, thus inherently lacking the capability to provide regional arterial stiffness measurements. Pulse Wave Imaging (PWI) is a noninvasive, ultrasound-based technique for imaging the propagation of pulse waves along the wall of major arteries and providing a regional PWV value for the imaged artery. The aim of this work was to enable PWI to provide more localized PWV and stiffness measurements within the imaged arterial segment and to further extend it into a 2-D and 3-D technique for the detection and monitoring of focal vascular disease at high temporal and spatial resolution. The improved modality was integrated with blood flow imaging modalities aiming to render PWI a comprehensive methodology for the study of arterial biomechanics in vivo. Spatial information was increased with the introduction of piecewise PWI. This novel technique was used to measure PWV within small sub-regions of the imaged vessel in murine aneurysmal (n = 8) and atherosclerotic aortas (n = 11) in vivo. It provided PWV and stiffness maps while capturing the progressive arterial stiffening caused by atherosclerosis. PWI was further augmented with a sophisticated adaptive algorithm, enabling it to optimally partition the imaged artery into relatively homogeneous segments, automatically isolating arterial stiffness inhomogeneities. Adaptive PWI was validated in silicone phantoms consisting of segments of varying stiffness and then tested in murine aortas in vivo. Subsequently, the conventional tradeoff between spatial and temporal resolution was addressed with a plane wave compounding implementation of PWI, allowing the acquisition of full field of view frames at over 2000 Hz. A GPU-accelerated PWI post-processing framework was developed for the processing of the big bulk of generated data. The parameters of coherent compounding were optimized in vivo. The optimized sequences were then used in the clinic to assess the mechanical properties of atherosclerotic carotids (n=10) and carotids of patients after endarterectomy (n=7), a procedure to remove the plaque and restore blood flow to the brain. In the case of atherosclerotic patients undergoing carotid endarterectomy, the results were compared against the histology of the excised plaques. Investigation of the mechanical properties of plaques was also conducted for the first time with a high-frequency transducer (18.5 MHz). Additionally, 4-D PWI was introduced, utilizing high frame rate 3-D plane wave acquisitions with a 2-D matrix array transducer (16x16 elements, 2.5 MHz). A novel methodology for PWV estimation along the direction of pulse wave propagation was implemented and validated in silicone phantoms. 4-D PWI provided comprehensive views of the pulse wave propagation in a plaque phantom and the results were compared against conventional PWI. Finally, its feasibility was tested in the carotid arteries of healthy human subjects (n=6). PWVs derived in 3-D were within the physiological range and showed good agreement with the results of conventional PWI. Finally, PWI was integrated with flow imaging modalities (Color and Vector Doppler). Thus, full field-of-view, high frame-rate, simultaneous and co-localized imaging of the arterial wall dynamics and color flow as well as 2-D vector flow was implemented. The feasibility of both techniques was tested in healthy subjects (n=6) in vivo. The relationship between the timings of the flow and wall velocities was investigated at multiple locations of the imaged artery. Vector flow velocities were found to be aligned with the vessel’s centerline during peak systole in the common carotid artery and interesting flow patterns were revealed in the case of the carotid bifurcation Consequently, with the aforementioned improvements and the inclusion of 3-D imaging, PWI is expected to provide comprehensive information on the mechanical properties of pathological arteries, providing clinicians with a powerful tool for the early detection of vascular abnormalities undetectable on the B-mode, while also enabling the monitoring of fully developed vascular pathology and of the recovery of post-operated vessels

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