Heart Failure; Computational Modelling; Computational Mechanics; Magnetic Resonance Imaging; Cardiovascular Magnetic Resonance
Gorkum Robbert J. H., Deuster Constantin, Guenthner Christian, Stoeck Christian T., Kozerke Sebastian (2020), Analysis and correction of off‐resonance artifacts in echo‐planar cardiac diffusion tensor imaging, in Magnetic Resonance in Medicine
Stoeck Christian T., von Deuster Constantin, van Gorkum Robbert J. H., Kozerke Sebastian (2020), Motion and eddy current-induced signal dephasing in in vivo cardiac DTI, in Magn Reson Med
, 84(1), 277-288.
Stoeck Christian T., Scott Andrew D., Ferreira Pedro F., Tunnicliffe Elizabeth M., Teh Irvin, Nielles-Vallespin Sonia, Moulin Kevin, Sosnovik David E., Viallon Magalie, Croisille Pierre, Kozerke Sebastian, Firmin David N., Ennis Daniel B., Schneider Jurgen E. (2020), Motion-Induced Signal Loss in In Vivo Cardiac Diffusion-Weighted Imaging, in J Magn Reson Imaging
, 51(1), 319-320.
Berberoğlu Ezgi, Stoeck Christian, Moireau Philippe, Kozerke Sebastian, Genet Martin (2019), Validation of Finite Element Image Registration‐based Cardiac Strain Estimation from Magnetic Resonance Images, in PAMM
, 19(1), 1-4.
Joyce Thomas, Kozerke Sebastian (2019), 3D Medical Image Synthesis by Factorised Representation and Deformable Model Learning, in MICCAI
Spinner Georg R., Stoeck Christian T., Mathez Linda, von Deuster Constantin, Federau Christian, Kozerke Sebastian (2019), On probing intravoxel incoherent motion in the heart-spin-echo versus stimulated-echo DWI, in Magn Reson Med
, 82(3), 1150-1163.
Genet M., Stoeck C. T., von Deuster C., Lee L. C., Kozerke S. (2018), Equilibrated warping: Finite element image registration with finite strain equilibrium gap regularization, in Med Image Anal
, 50, 1-22.
Spinner Georg R., von Deuster Constantin, Tezcan Kerem C., Stoeck Christian T., Kozerke Sebastian (2017), Bayesian intravoxel incoherent motion parameter mapping in the human heart, in J Cardiovasc Magn Reson
, 19(1), 85-85.
Nagler Andreas, Bertoglio Cristóbal, Stoeck Christian T., Kozerke Sebastian, Wall Wolfgang A. (2017), Maximum likelihood estimation of cardiac fiber bundle orientation from arbitrarily spaced diffusion weighted images, in Med Image Anal
, 39, 56-77.
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.