Final Report Summary - COMPCARDMECH (Computational Tools for Cardiac Mechanics)
1.1. OBJECTIVES
Cardiovascular disease is the leading cause of death and disability in Europe and in the World. Heart failure itself, primarily caused by myocardial infarction, has now reached epidemic proportions. If past clinical therapies have been developed by trial-and-error, new therapies must now be designed through the scientific understanding of the functional and structural changes in diseased hearts. The ultimate aspiration of my research project is to use theoretical modeling, in combination with new imaging modalities and modern simulation tools, to provide greater insight into cardiac disease and thereby guide the design of new successful treatment strategies.
1.2. WORK PERFORMED
I have developed several computational tools to achieve the goals of my project. First, I developed a framework to simulate cardiac mechanics within the finite element software Abaqus/Standard. It includes both passive and active behaviors, based on the decomposition of stress into passive and active parts.
I also developed a pipeline to create patient-specific ventricular models from magnetic resonance images. The pipeline consists of manually segmenting the ventricles from the cine magnetic resonance images using MeVisLab, followed by generating finite element meshes using TrueGrid or GMSH. Then I create synthetic myofiber and myosheet orientation maps onto the mesh using custom vtkpython scripts. I also developed a finite element digital image correlation toolbox, which can be used to track the motion and deformation of the heart from cine or tagged images, as well as for non-rigid registration in order to combine anatomical, strain and viability data. When in vivo diffusion tensor imaging data is available, I developed a data filtering and densification technique, which extracts the low rank modes from the data, and then extrapolates them to the whole ventricles. Finally, I personalize the material behavior using the optimization package LS-Opt.
Another important development was to extend my cardiac mechanics framework with the finite growth theory. My implementation is based on the multiplicative decomposition of the transformation into growth and elastic parts. It is rather general, in terms of both the growth kinematic and growth kinetics. This framework has just been introduced within an existing cardiac electromechanics code, leading to one of the first cardiac simulations that include short term electrical activation, mechanical response, and long term tissue adaptation.
1.3. MAIN RESULTS
The patient specific pipeline had first been applied to a set of normal humans [1]. It allowed to derive the range of normal human myocardial mechanical properties, in both diastole and systole. These parameters are now used within the Living Heart Project [2]. It also allowed to create normal human maps for myocardial stress at end-diastole and end-systole, which are to be used as targets for the in silico optimization of cardiac therapies. The pipeline was then applied to patients suffering from myocardial infarction [3]. Here we were able to establish a relationship between measured viability maps and computed contractility maps, thus allowing to more rigorously characterize the ventricular mechanics in the infarcted and border zone areas. Similar infarcted ventricular models were used to investigate the impact of biopolymer injection [4]. We were able to characterize the residual stress pattern generated around the injectates, as illustrated in Figure 1.
The growth and remodeling framework was first applied to model adverse and reverse growth [5]. We were able to establish a single law that predicts adverse growth under over-physiological pressure, and reverse growth under pressure normalization. I also used my growth model to investigate the problem of residual stress in the heart [6]. I was able to show that growth-induced residual stress is compatible with the classical opening angle experiment, as illustrated on Figure 2. I also found that this prestrain was actually making the ventricle more compliant, thus improving diastolic function, which could be another potential benefit of residual stress. And to further demonstrate my framework’s ability to predict cardiac growth, I used it with the context of the Living Heart Project to study pulmonary and systemic hypertension-induced hypertrophy, using multiple growth laws, including longitudinal and transverse growth [7]. I was able to show that the model was able to reproduce the main features of cardiac hypertrophy, such as reverse septum and left ventricular “D-shape” in pulmonary hypertension, as illustrated in Figure 3. This work was extended to simulate the effect of annuloplasty [8]. Coupled to an established electromechanics simulation code, this growth framework opens the door to patient-specific, objective and quantitative prognosis of cardiac diseases [9].
1.4. REFERENCES
[1] M. Genet, ... and J. M. Guccione, “Distribution of normal human left ventricular myofiber stress at end-diastole and end-systole‒a target for in silico studies of cardiac procedures,” J. Appl. Physiol., vol. 117, pp. 142–52, 2014.
[2] B. Baillargeon, ... M. Genet, ... and J. M. Guccione, “Human cardiac function simulator for the optimal design of a novel sub-valvular device for correcting mitral regurgitation,” Cardiovasc. Eng. Technol., 2015.
[3] M. Genet, ... and J. M. Guccione, “A Novel Method for Quantifying Smooth Regional Variations in Myocardial Contractility Within an Infarcted Human Left Ventricle Based on Delay-Enhanced Magnetic Resonance Imaging,” J. Biomech. Eng., vol. 137, no. 8, Aug. 2015.
[4] L. C. Lee, S. T. Wall, M. Genet, A. Hinson, and J. M. Guccione, “Bioinjection treatment: Effects of post-injection residual stress on left ventricular wall stress,” J. Biomech., vol. 47, no. 12, pp. 3115–9, Sep. 2014.
[5] L. C. Lee, M. Genet, ... and E. Kuhl, “A computational model that predicts reverse growth in response to mechanical unloading,” Biomech. Model. Mechanobiol., vol. 14, no. 2, pp. 217–229, Jun. 2014.
[6] M. Genet, ... and E. Kuhl, “Heterogeneous growth-induced prestrain in the heart,” J. Biomech., vol. 48, no. 10, pp. 2080–2089, Jul. 2015.
[7] M. Genet, ... and E. Kuhl, “Modeling Pathologies of Diastolic and Systolic Heart Failure,” Ann. Biomed. Eng., Jun. 2015.
[8] M. K. Rausch, A. Zoellner, M. Genet, ... and E. Kuhl, “A virtual sizing tool for mitral valve annuloplasty,” Submitt. to Int. J. Numer. Methods Biomed. Eng., 2015.
[9] L. C. Lee, J. Sundnes, M. Genet, J. F. Wenk, and S. T. Wall, “An integrated electromechanical-growth heart model for simulating cardiac therapies,” Biomech. Model. Mechanobiol., 2015.
Cardiovascular disease is the leading cause of death and disability in Europe and in the World. Heart failure itself, primarily caused by myocardial infarction, has now reached epidemic proportions. If past clinical therapies have been developed by trial-and-error, new therapies must now be designed through the scientific understanding of the functional and structural changes in diseased hearts. The ultimate aspiration of my research project is to use theoretical modeling, in combination with new imaging modalities and modern simulation tools, to provide greater insight into cardiac disease and thereby guide the design of new successful treatment strategies.
1.2. WORK PERFORMED
I have developed several computational tools to achieve the goals of my project. First, I developed a framework to simulate cardiac mechanics within the finite element software Abaqus/Standard. It includes both passive and active behaviors, based on the decomposition of stress into passive and active parts.
I also developed a pipeline to create patient-specific ventricular models from magnetic resonance images. The pipeline consists of manually segmenting the ventricles from the cine magnetic resonance images using MeVisLab, followed by generating finite element meshes using TrueGrid or GMSH. Then I create synthetic myofiber and myosheet orientation maps onto the mesh using custom vtkpython scripts. I also developed a finite element digital image correlation toolbox, which can be used to track the motion and deformation of the heart from cine or tagged images, as well as for non-rigid registration in order to combine anatomical, strain and viability data. When in vivo diffusion tensor imaging data is available, I developed a data filtering and densification technique, which extracts the low rank modes from the data, and then extrapolates them to the whole ventricles. Finally, I personalize the material behavior using the optimization package LS-Opt.
Another important development was to extend my cardiac mechanics framework with the finite growth theory. My implementation is based on the multiplicative decomposition of the transformation into growth and elastic parts. It is rather general, in terms of both the growth kinematic and growth kinetics. This framework has just been introduced within an existing cardiac electromechanics code, leading to one of the first cardiac simulations that include short term electrical activation, mechanical response, and long term tissue adaptation.
1.3. MAIN RESULTS
The patient specific pipeline had first been applied to a set of normal humans [1]. It allowed to derive the range of normal human myocardial mechanical properties, in both diastole and systole. These parameters are now used within the Living Heart Project [2]. It also allowed to create normal human maps for myocardial stress at end-diastole and end-systole, which are to be used as targets for the in silico optimization of cardiac therapies. The pipeline was then applied to patients suffering from myocardial infarction [3]. Here we were able to establish a relationship between measured viability maps and computed contractility maps, thus allowing to more rigorously characterize the ventricular mechanics in the infarcted and border zone areas. Similar infarcted ventricular models were used to investigate the impact of biopolymer injection [4]. We were able to characterize the residual stress pattern generated around the injectates, as illustrated in Figure 1.
The growth and remodeling framework was first applied to model adverse and reverse growth [5]. We were able to establish a single law that predicts adverse growth under over-physiological pressure, and reverse growth under pressure normalization. I also used my growth model to investigate the problem of residual stress in the heart [6]. I was able to show that growth-induced residual stress is compatible with the classical opening angle experiment, as illustrated on Figure 2. I also found that this prestrain was actually making the ventricle more compliant, thus improving diastolic function, which could be another potential benefit of residual stress. And to further demonstrate my framework’s ability to predict cardiac growth, I used it with the context of the Living Heart Project to study pulmonary and systemic hypertension-induced hypertrophy, using multiple growth laws, including longitudinal and transverse growth [7]. I was able to show that the model was able to reproduce the main features of cardiac hypertrophy, such as reverse septum and left ventricular “D-shape” in pulmonary hypertension, as illustrated in Figure 3. This work was extended to simulate the effect of annuloplasty [8]. Coupled to an established electromechanics simulation code, this growth framework opens the door to patient-specific, objective and quantitative prognosis of cardiac diseases [9].
1.4. REFERENCES
[1] M. Genet, ... and J. M. Guccione, “Distribution of normal human left ventricular myofiber stress at end-diastole and end-systole‒a target for in silico studies of cardiac procedures,” J. Appl. Physiol., vol. 117, pp. 142–52, 2014.
[2] B. Baillargeon, ... M. Genet, ... and J. M. Guccione, “Human cardiac function simulator for the optimal design of a novel sub-valvular device for correcting mitral regurgitation,” Cardiovasc. Eng. Technol., 2015.
[3] M. Genet, ... and J. M. Guccione, “A Novel Method for Quantifying Smooth Regional Variations in Myocardial Contractility Within an Infarcted Human Left Ventricle Based on Delay-Enhanced Magnetic Resonance Imaging,” J. Biomech. Eng., vol. 137, no. 8, Aug. 2015.
[4] L. C. Lee, S. T. Wall, M. Genet, A. Hinson, and J. M. Guccione, “Bioinjection treatment: Effects of post-injection residual stress on left ventricular wall stress,” J. Biomech., vol. 47, no. 12, pp. 3115–9, Sep. 2014.
[5] L. C. Lee, M. Genet, ... and E. Kuhl, “A computational model that predicts reverse growth in response to mechanical unloading,” Biomech. Model. Mechanobiol., vol. 14, no. 2, pp. 217–229, Jun. 2014.
[6] M. Genet, ... and E. Kuhl, “Heterogeneous growth-induced prestrain in the heart,” J. Biomech., vol. 48, no. 10, pp. 2080–2089, Jul. 2015.
[7] M. Genet, ... and E. Kuhl, “Modeling Pathologies of Diastolic and Systolic Heart Failure,” Ann. Biomed. Eng., Jun. 2015.
[8] M. K. Rausch, A. Zoellner, M. Genet, ... and E. Kuhl, “A virtual sizing tool for mitral valve annuloplasty,” Submitt. to Int. J. Numer. Methods Biomed. Eng., 2015.
[9] L. C. Lee, J. Sundnes, M. Genet, J. F. Wenk, and S. T. Wall, “An integrated electromechanical-growth heart model for simulating cardiac therapies,” Biomech. Model. Mechanobiol., 2015.