Project

Discipline

Heart Failure; Computational Modelling; Computational Mechanics; Magnetic Resonance Imaging; Cardiovascular Magnetic Resonance

Lead
Das vorliegende Projekt hat das Ziel, patientenspezifische Computermodelle des Herzens zu entwickeln, um die Remodellierung des Herzens hinzu einer Herzinsuffizienz ausgehend von nicht-invasiven Magnetresonanzbildgebungsdaten vorherzusagen. Zu diesem Zweck werden modernste Magnetresonanzverfahren, die neben anatomischen und funktionellen Parametern auch die Erfassung der Mikrofaserstruktur des Herzens erlauben, implementiert und validiert. Ausgehend von diesen Daten werden biomechanische Modelle entwickelt und an die in-vivo Daten angepasst. Anhand dieser Modelle werden in der Folge Vorhersagen zur Veränderung des Herzens berechnet und diese durch weitere Magnetresonanzmessungen optimiert. Die Ergebnisse und Werkzeuge, die im Rahmen dieser Forschungsarbeit entstehen, sollen zukünftig dazu beitragen, optimale Therapieverfahren für Patienten mit einer Herzinsuffizienz zu entwickeln.
Lay summary
While advances in diagnostics and therapy have improved survival after heart failure diagnosis, 50% of the patients die within five years upon diagnosis. In heart failure, the heart remodels and cardiac function deteriorates. Without timely treatment and intervention, fatal consequences develop. Current non-invasive diagnostic tests provide an assessment of the overall function of the heart. However, they do not reveal insights into microstructural changes and fail to predict outcome. Critically, alterations at microstructural level are seen as early hallmarks of deteriorating cardiac function. Detailed insights potentially enable the prediction of the structural and mechanical state of the heart given diagnostic image information in a patient. To this end, a framework will be developed in the present project which ultimately permits improved risk stratification of an individual patient’s heart by the combination of advanced non-invasive Magnetic Resonance imaging and patient-specific and image-guided computational modelling of the heart. The project involves data collection in pig models of chronic cardiac infarction using advanced non-invasive Magnetic Resonance imaging methods. Thereby information of the anatomy, function and microstructure can be obtained and used to develop and build computational models of the heart. Using these models future state and function of the heart will be predicted. These prediction will subsequently validated by repeated Magnetic Resonance imaging. Once the predictive performance of the models is established, the methodology will be translated into a clinical setting to the benefits of patients suffering from heart failure.

Direct link to Lay Summary

Last update: 30.06.2016

Active participation

Active participation

Communication	Title	Media	Place	Year

Number	Title	Start	Funding scheme

In spite of significant advances in diagnosis and treatment, ischemic heart disease remains a leading cause of death worldwide. The loss of functional cardiac muscle tissue due to myocardial infarction causes ventricular remodeling including myofiber disarray, myocardial wall thinning and dilatation, compromising the overall pump function of the heart. Without timely treatment these conditions often lead to heart failure. At a global level 26 million patients suffer from heart failure with an incidence that is steadily increasing with age. While survival has improved over the last decades, 50% of heart failure patients still die within five years upon diagnosis.Critical aspects of the heart failure management are early diagnosis and long-term prognosis, which define the patients’ risk stratification and associated preventative therapy options. To improve early diagnosis, treatment efficiency and ultimately long-term prognosis, it is essential to better understand the cascade of events during disease onset and progression. Computational modeling, in combination with modern simulation tools and new imaging and experimental modalities, now offer the potential to provide greater insight into structural and functional changes of tissue growth and remodeling.The overall objective of the present proposal is to develop image-guided and experimentally validated computational models of cell aggregate-level structural and tissue-level mechanical changes in pathologic cardiac growth and remodeling. To this end, a growth and remodeling law is formulated and implemented, which will be calibrated and validated using longitudinal in-vivo Magnetic Resonance (MR) imaging data and ex-vivo experimental data of cardiac microstructure, tissue and functional parameters in pig models of chronic myocardial infarction and left ventricular volume overload. The proposed program is highly interdisciplinary as it combines in-vivo and ex-vivo imaging in a translational setting, mechanical modeling and numerical simulation. The key work packages of the project are defined as follows:WP1. In-vivo tissue- and organ-level MR and pressure data collection in pig models of chronic myocardial infarction and volume overload•MR cine, displacement and diffusion imaging to quantify cardiac kinematics and microstructure•Application of MR extracellular volume mapping to quantify tissue viability and extracellular volume•Adaptation of our data analysis and processing pipeline to derive kinematic and parametric maps•Longitudinal control study in healthy pigs without intervention to establish reference values •Longitudinal comprehensive MR and pressure measurements during chronic myocardial infarction•Longitudinal comprehensive MR and pressure measurements during chronic volume overloadWP2. Formulation, implementation and calibration of cardiac mechanical model relating cell aggregate, tissue and organ level properties of remodeling heart•Implementation of microstructure-based constitutive law for the myocardium•Formulation of microstructure-based growth evolution law within our existing framework•Formulation and implementation of remodeling law to describe structural change of myofibers•Building of animal-specific bi-ventricular models based on longitudinal in-vivo data •Calibration and validation of growth & remodeling law based on longitudinal in-vivo dataWP3. Validation of the proposed structure-properties relationship using high-resolution ex-vivo imaging and histology •Post-mortem high-resolution MR diffusion tensor imaging for detailed tissue analysis•Implementation and application of ex-vivo MR elastography to quantify mechanical tissue stiffness•Histological staining to quantify distribution of collagen and cardiac muscle•Correlation of model prediction with longitudinal in-vivo and experimental post-mortem data•Validation of proposed structure-properties relationship in health and diseaseThe project proposed herein addresses the lack of data and understanding of cardiac growth and remodeling upon infarction and ventricular volume overload. Using a novel computational modeling framework to combine data from comprehensive MR imaging, a bridge between the remodeling mechanisms taking place at the cell aggregate level over time and the tissue level, which can be probed non-invasively in-vivo, will be established. The concept is expected to pave the way towards quantitative diagnosis and prognostic tools to optimize and personalize treatment strategies in heart failure patients.