Left Ventricular Trabeculations Decrease the Wall Shear Stress and Increase the Intra-Ventricular Pressure Drop in CFD Simulations

The aim of the present study is to characterize the hemodynamics of left ventricular (LV) geometries to examine the impact of trabeculae and papillary muscles (PMs) on blood flow using high performance computing (HPC). Five pairs of detailed and smoothed LV endocardium models were reconstructed from high-resolution magnetic resonance images (MRI) of ex-vivo human hearts. The detailed model of one LV pair is characterized only by the PMs and few big trabeculae, to represent state of art level of endocardial detail. The other four detailed models obtained include instead endocardial structures measuring ≥1 mm2 in cross-sectional area. The geometrical characterizations were done using computational fluid dynamics (CFD) simulations with rigid walls and both constant and transient flow inputs on the detailed and smoothed models for comparison. These simulations do not represent a clinical or physiological scenario, but a characterization of the interaction of endocardial structures with blood flow. Steady flow simulations were employed to quantify the pressure drop between the inlet and the outlet of the LVs and the wall shear stress (WSS). Coherent structures were analyzed using the Q-criterion for both constant and transient flow inputs. Our results show that trabeculae and PMs increase the intra-ventricular pressure drop, reduce the WSS and disrupt the dominant single vortex, usually present in the smoothed-endocardium models, generating secondary small vortices. Given that obtaining high resolution anatomical detail is challenging in-vivo, we propose that the effect of trabeculations can be incorporated into smoothed ventricular geometries by adding a porous layer along the LV endocardial wall. Results show that a porous layer of a thickness of 1.2·10−2 m with a porosity of 20 kg/m2 on the smoothed-endocardium ventricle models approximates the pressure drops, vorticities and WSS observed in the detailed models.This paper has been partially funded by CompBioMed project, under H2020-EU.1.4.1.3 European Union’s Horizon 2020 research and innovation programme, grant agreement n◦ 675451. FS is supported by a grant from Severo Ochoa (n◦ SEV-2015-0493-16-4), Spain. CB is supported by a grant from the Fundació LaMarató de TV3 (n◦ 20154031), Spain. TI and PI are supported by the Institute of Engineering in Medicine, USA, and the Lillehei Heart Institute, USA.Peer ReviewedPostprint (published version

Biomechanical analysis of hypoplastic left heart syndrome and calcific aortic stenosis: a statistical and computational study

2021 Fall.Includes bibliographical references.Cardiovascular diseases are a leading cause of death in the United States. In this dissertation, a congenital heart disease (CHD) and a valvular disease are discussed. CHDs occur in ~5% of live births. Structural CHDs can be complex and difficult to treat, such as hypoplastic left heart syndrome (HLHS) in which the left ventricle is generally underdeveloped, representing ~9% of all congenital heart diseases. Calcific aortic stenosis is one of the most common valvular diseases in which valves thicken and stiffen, and in some cases nodular deposits form, limiting valve function that may result in flow regurgitation and outflow obstruction. The overarching hypothesis of this research is that patient-specific heart geometry and valve characteristics are linked to cardiovascular diseases and may play an important role in regulating hemodynamics within the heart. This hypothesis is studied through three specific aims. In specific aim 1, a computational fluid dynamics study was developed to quantify the hemodynamic characteristics within the right ventricles of healthy fetuses and fetuses with HLHS, using 4D patient-specific ultrasound scans. In these simulations, we find that the HLHS right ventricle exhibits a greater cardiac output than normal; yet, hemodynamics are relatively similar between normal and HLHS right ventricles. Overall, this study provides detailed quantitative flow patterns for HLHS, which has the potential to guide future prevention and therapeutic interventions, while more immediately providing additional functional detail to cardiologists to aid in decision making. The specific aim 2 is a comprehensive review in which we highlight underlying molecular mechanisms of acquired aortic stenosis calcification in relation to hemodynamics, complications related to the disease, diagnostic methods, and evolving treatment practices for calcific aortic stenosis and, bioprosthetic or native aortic scallop intentional laceration (BASILICA) procedure to free coronary arteries from obstruction. In specific aim 3, we use statistical trends and relationships to identify the role of patient-specific aortic valve characteristics in post-BASILICA coronary obstruction. The findings of this study shows that in addition to direct anatomical measurements of the aortic valve, the aspect ratios of the anatomical features are important in determining the cause of post-BASILICA coronary obstruction. The overall significance of this dissertation is that computational and statistical analysis of patient's specific flow hemodynamics and geometric characteristics can provide more insight into the cardiovascular disease and treatment approaches which can ultimately assist surgeons with procedural planning

Pulmonary vein flow split effects in patient-specific simulations of left atrial flow

Disruptions to left atrial (LA) blood flow, such as those caused by atrial fibrillation (AF), can lead to thrombosis in the left atrial appendage (LAA) and an increased risk of systemic embolism. LA hemodynamics are influenced by various factors, including LA anatomy and function, and pulmonary vein (PV) inflow conditions. In particular, the PV flow split can vary significantly among and within patients depending on multiple factors. In this study, we investigated how changes in PV flow split affect LA flow transport, focusing for the first time on blood stasis in the LAA, using a high-fidelity patient-specific computational fluid dynamics (CFD) model. We use an Immersed Boundary Method, simulating the flow in a fixed, uniform Cartesian mesh and imposing the movement of the LA walls with a moving Lagrangian mesh generated from 4D Computerized Tomography images. We analyzed LA anatomies from eight patients with varying atrial function, including three with AF and either a LAA thrombus or a history of Transient Ischemic Attacks (TIAs). Using four different flow splits (60/40% and 55/45% through right and left PVs, even flow rate, and same velocity through each PV), we found that flow patterns are sensitive to PV flow split variations, particularly in planes parallel to the mitral valve. Changes in PV flow split also had a significant impact on blood stasis and could contribute to increased risk for thrombosis inside the LAA, particularly in patients with AF and previous LAA thrombus or a history of TIAs. Our study highlights the importance of considering patient-specific PV flow split variations when assessing LA hemodynamics and identifying patients at increased risk for thrombosis and stroke. This knowledge is relevant to planning clinical procedures such as AF ablation or the implementation of LAA occluders.This work was partially supported by Comunidad de Madrid (Synergy Grant Y2018/BIO-4858 PREFI-CM), Spanish Research Agency (AEI, grant number PID2019-107279RB-I00), Instituto de Salud Carlos III (grant numbers PI15/02211-ISBITAMI and DTS/1900063-ISBIFLOW), and by the EU-European Regional Development Fund . Funding for open access charge: Universidad de Málaga / CBUA

Pulmonary vein flow split effects in patient-specific simulations of left atrial flow

Disruptions to left atrial (LA) blood flow, such as those caused by atrial fibrillation (AF), can lead to thrombosis in the left atrial appendage (LAA) and an increased risk of systemic embolism. LA hemodynamics are influenced by various factors, including LA anatomy and function, and pulmonary vein (PV) inflow conditions. In particular, the PV flow split can vary significantly among and within patients depending on multiple factors. In this study, we investigated how changes in PV flow split affect LA flow transport, focusing for the first time on blood stasis in the LAA, using a high-fidelity patient-specific computational fluid dynamics (CFD) model. We use an Immersed Boundary Method, simulating the flow in a fixed, uniform Cartesian mesh and imposing the movement of the LA walls with a moving Lagrangian mesh generated from 4D Computerized Tomography images. We analyzed LA anatomies from eight patients with varying atrial function, including three with AF and either a LAA thrombus or a history of Transient Ischemic Attacks (TIAs). Using four different flow splits (60/40% and 55/45% through right and left PVs, even flow rate, and same velocity through each PV), we found that flow patterns are sensitive to PV flow split variations, particularly in planes parallel to the mitral valve. Changes in PV flow split also had a significant impact on blood stasis and could contribute to increased risk for thrombosis inside the LAA, particularly in patients with AF and previous LAA thrombus or a history of TIAs. Our study highlights the importance of considering patient-specific PV flow split variations when assessing LA hemodynamics and identifying patients at increased risk for thrombosis and stroke. This knowledge is relevant to planning clinical procedures such as AF ablation or the implementation of LAA occluders.This work was partially supported by Comunidad de Madrid (Synergy Grant Y2018/BIO-4858 PREFI-CM), Spanish Research Agency (AEI, grant number PID2019-107279RB-I00), Instituto de Salud Carlos III (grant numbers PI15/02211-ISBITAMI and DTS/1900063-ISBIFLOW), and by the EU-European Regional Development Fund. Funding for open access charge: Universidad de Málaga /CBU

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

Hemodynamic Modeling of Biological Aortic Valve Replacement Using Preoperative Data Only

Objectives: Prediction of aortic hemodynamics after aortic valve replacement (AVR) could help optimize treatment planning and improve outcomes. This study aims to demonstrate an approach to predict postoperative maximum velocity, maximum pressure gradient, secondary flow degree (SFD), and normalized flow displacement (NFD) in patients receiving biological AVR. Methods: Virtual AVR was performed for 10 patients, who received actual AVR with a biological prosthesis. The virtual AVRs used only preoperative anatomical and 4D flow MRI data. Subsequently, computational fluid dynamics (CFD) simulations were performed and the abovementioned hemodynamic parameters compared between postoperative 4D flow MRI data and CFD results. Results: For maximum velocities and pressure gradients, postoperative 4D flow MRI data and CFD results were strongly correlated (R 2 = 0.75 and R-2 = 0.81) with low root mean square error (0.21 m/s and 3.8 mmHg). SFD and NFD were moderately and weakly correlated at R 2 = 0.44 and R 2 = 0.20, respectively. Flow visualization through streamlines indicates good qualitative agreement between 4D flow MRI data and CFD results in most cases. Conclusion: The approach presented here seems suitable to estimate postoperative maximum velocity and pressure gradient in patients receiving biological AVR, using only preoperative MRI data. The workflow can be performed in a reasonable time frame and offers a method to estimate postoperative valve prosthesis performance and to identify patients at risk of patient-prosthesis mismatch preoperatively. Novel parameters, such as SFD and NFD, appear to be more sensitive, and estimation seems harder. Further workflow optimization and validation of results seems warranted

Novel Applications of Cardiovascular Magnetic Resonance Imaging-Based Computational Fluid Dynamics Modeling in Pediatric Cardiovascular and Congenital Heart Disease

Cardiovascular diseases (CVDs) afflict many people across the world; thus, understanding the pathophysiology of CVD and the biomechanical forces which influence CVD progression is important in the development of optimal strategies to care for these patients. Over the last two decades, cardiac magnetic resonance (CMR) imaging has offered increasingly important insights into CVD. Computational fluid dynamics (CFD) modeling, a method of simulating the characteristics of flowing fluids, can be applied to the study of CVD through the collaboration of engineers and clinicians. This chapter aims to explore the current state of the CMR-derived CFD, as this technique pertains to both acquired CVD (i.e., atherosclerosis) and congenital heart disease (CHD)

Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie - klinische Anwendung bildbasierter in silico Modellierung

Die richtige Therapie zum richtigen Zeitpunkt, nichtinvasiv und patientenindividuell zu identifizieren, ist das Ziel der Präzisionsmedizin. Durch den stetigen Fortschritt sowohl im Bereich der Bildgebung als auch in mathematischen Modellierungstechniken sowie einer zunehmenden Verfügbarkeit von leistungsstarker Informationstechnologie, gewinnen in silico (angelehnt an das Lateinische „in silicio“, also „in silicium“ bzw. im übertragenden Sinne im Computer ablaufende) Modellierungsverfahren eine immer größere Bedeutung auch im Bereich der kardiovaskulären Medizin. Die bildbasierte in silico Modellierung von Hämodynamik und Funktion des Herzens kann dabei einerseits helfen, die diagnostische Aussagekraft unterschiedlicher Bildgebungsmodalitäten zu erweitern, andererseits aber auch, verschiedene Parameter der postinterventionellen bzw. postoperativen Funktion vorherzusagen und so das geeignetste patientenindividuelle Therapieverfahren zu identifizieren. Im Bereich der pädiatrischen Kardiologie, insbesondere bei Patient*innen mit komplexen angeborenen Herzfehlern, ist eine individualisierte Therapieplanung zudem von ganz besonderer Bedeutung. Da die Anatomie des kardiovaskulären Systems in diesem Patientenkollektiv hoch individuell ist, gibt es häufig keine für das jeweilige Krankheitsbild einheitliche Therapie. Die virtuelle Behandlungsplanung bietet hier ein großes Potential für die multimodale Therapiefindung. Die Translation solcher Modellierungsansätze in die Klinik stellt jedoch eine große Hürde dar. Einerseits muss die Genauigkeit der jeweiligen Simulationsmethode quantifiziert und die Methode selbst validiert werden. Dafür benötigt es in der Regel eine hohe Anzahl an Patientendaten, die insbesondere in der Kinderkardiologie, aber auch aufgrund zunehmend strengerer Datenschutzrichtlinien häufig nicht zur Verfügung stehen. Andererseits sind die Simulationsverfahren sehr komplex und verlangen neben einer hohen technischen Expertise auch beachtliche Rechenkapazitäten und -laufzeiten, wodurch sich ihr routinemäßiger Einsatz in der Klinik ebenfalls verkompliziert. Das Problem der hohen Komplexität könnte durch den Einsatz künstlicher Intelligenz (KI) überwunden werden. Fehlende klinische Daten wiederum könnten mittels synthetischer Patientenkohorten augmentiert werden, sodass sowohl für mögliche Validierungsstudien als auch zum Trainieren des maschinellen Algorithmus‘ ein ausreichend großer Datensatz zur Verfügung stünde. In der vorliegenden Habilitationsschrift werden die Inhalte von fünf wissenschaftlichen Arbeiten zum Thema Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie auf Grundlage bildbasierter in silico Modellierung vorgestellt. Dabei wird in Form einer Proof of Concept Studie die prinzipielle Durchführbarkeit der bildbasierten in silico Modellierung am Beispiel verschiedener Parameter der aortalen Hämodynamik gezeigt sowie die Validierung der Methodik gegen den klinischen Goldstandard des Herzkatheters präsentiert. An komplexen Patient*innen aus dem Bereich der Kinderkardiologie wird die bildbasierte in silico Modellierung für eine konkrete klinische Fragestellung angewandt. Zuletzt werden zwei Optimierungsansätze vorgestellt, die einerseits den komplexen Arbeitsablauf der bildbasierten in silico Modellierung mittels KI vereinfachen sowie andererseits das Problem der existierenden klinischen Datenlücken überwinden sollen