Patient-specific computational fluid dynamics-assessment of aortic hemodynamics in a spectrum of aortic valve pathologies.

OBJECTIVES: The complexity of aortic disease is not fully exposed by aortic dimensions alone, and morbidity or mortality can occur before intervention thresholds are met. Patient-specific computational fluid dynamics (CFD) were used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress (WSS), and oscillatory shear index (OSI) in the thoracic aorta. METHODS: A total of 45 subjects were divided into 5 groups: volunteers, aortic regurgitation-tricuspid aortic valve (AR-TAV), aortic stenosis-tricuspid aortic valve (AS-TAV), aortic stenosis-bicuspid aortic valve right-left cusp fusion (BAV[RL]), and aortic stenosis-right-non cusp fusion (AS-BAV[RN]). Subjects underwent magnetic resonance angiography, with phase-contrast magnetic resonance imaging at the sino-tubular junction to define patient-specific inflow velocity profiles. Hemodynamic recordings were used alongside magnetic resonance imaging angiographic data to run patient-specific CFD. RESULTS: The BAV groups had larger mid-ascending aorta diameters (P < .05). Ascending aorta flow was more eccentric in BAV (flow asymmetry = 78.9% ± 6.5% for AS-BAV(RN), compared with 4.7% ± 2.1% for volunteers, P < .05). Helicity was greater in AS-BAV(RL) (P < .05). Mean WSS was elevated in AS groups, greatest in AS-BAV(RN) (37.1 ± 4.0 dyn/cm2, compared with 9.8 ± 5.4 for volunteers, P < .05). The greater curvature of the ascending aorta experienced highest WSS and lowest OSI in AS patients, most significant in AS-BAV(RN) (P < .05). CONCLUSIONS: BAV displays eccentric flow with high helicity. The presence of AS, particularly in BAV-RN, led to greater WSS and lower OSI in the greater curvature of the ascending aorta. Patient-specific CFD provides noninvasive functional assessment of the thoracic aorta, and may enable development of a personalized approach to diagnosis and management of aortic disease beyond traditional guidelines

Hemodynamic Effects of Entry and Exit Tear Size in Aortic Dissection Evaluated with In Vitro Magnetic Resonance Imaging and Fluid-Structure Interaction Simulation

Understanding the complex interplay between morphologic and hemodynamic features in aortic dissection is critical for risk stratification and for the development of individualized therapy. This work evaluates the effects of entry and exit tear size on the hemodynamics in type B aortic dissection by comparing fluid-structure interaction (FSI) simulations with in vitro 4D-flow magnetic resonance imaging (MRI). A baseline patient-specific 3D-printed model and two variants with modified tear size (smaller entry tear, smaller exit tear) were embedded into a flow- and pressure-controlled setup to perform MRI as well as 12-point catheter-based pressure measurements. The same models defined the wall and fluid domains for FSI simulations, for which boundary conditions were matched with measured data. Results showed exceptionally well matched complex flow patterns between 4D-flow MRI and FSI simulations. Compared to the baseline model, false lumen flow volume decreased with either a smaller entry tear (-17.8 and -18.5 %, for FSI simulation and 4D-flow MRI, respectively) or smaller exit tear (-16.0 and -17.3 %). True to false lumen pressure difference (initially 11.0 and 7.9 mmHg, for FSI simulation and catheter-based pressure measurements, respectively) increased with a smaller entry tear (28.9 and 14.6 mmHg), and became negative with a smaller exit tear (-20.6 and -13.2 mmHg). This work establishes quantitative and qualitative effects of entry or exit tear size on hemodynamics in aortic dissection, with particularly notable impact observed on FL pressurization. FSI simulations demonstrate acceptable qualitative and quantitative agreement with flow imaging, supporting its deployment in clinical studies.Comment: Judith Zimmermann and Kathrin B\"aumler contributed equall

Towards Predicting Patient-Specific Flow-Diverter Treatment Outcomes for Bifurcation Aneurysms: From Implantation Rehearsal to Virtual Angiograms

Despite accounting for the majority of all cerebral aneurysm cases, bifurcation aneurysms present many challenges to standard endovascular treatment techniques. This study examines the treatment of bifurcation aneurysms endovascularly with flow-diverting stents and presents an integrative computational modeling suite allowing for rehearsing all aspects of the treatment. Six bifurcation aneurysms are virtually treated with 70% porosity flow-diverters. Substantial reduction (>50%) in aneurysm inflow due to device deployment is predicted in addition to reductions in peak and average aneurysm wall shear stress to values considered physiologically normal. The subsequent impact of flow-diverter deployment on daughter vessels that are jailed by the device is investigated further, with a number of simulations conducted with increased outlet pressure conditions at jailed vessels. Increased outlet pressures at jailed daughter vessels are found to have little effect on device-induced aneurysm inflow reduction, but large variation (13-86%) is seen in the resulting reduction in daughter vessel flow rate. Finally, we propose a potentially powerful approach for validation of such models, by introducing an angiographic contrast model, with contrast transport modeled both before and after virtual treatment. Virtual angiograms and contrast residence curves are created, which offer unique clinical relevance and the potential for future in vivo verification of simulated results

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

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

Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

International audienceThe January 2013 Special Issue of IEEE transactions on medical imaging discusses papers on medical imaging and image computing in computational physiology. Aslanid and co-researchers present an experimental technique based on stained micro computed tomography (CT) images to construct very detailed atrial models of the canine heart. The paper by Sebastian proposes a model of the cardiac conduction system (CCS) based on structural information derived from stained calf tissue. Ho, Mithraratne and Hunter present a numerical simulation of detailed cerebral venous flow. The third category of papers deals with computational methods for simulating medical imagery and incorporate knowledge of imaging physics and physiology/biophysics. The work by Morales showed how the combination of device modeling and virtual deployment, in addition to patient-specific image-based anatomical modeling, can help to carry out patient-specific treatment plans and assess alternative therapeutic strategies