Periodic Reporting for period 4 - iHEART (An Integrated Heart Model for the simulation of the cardiac function)
Okres sprawozdawczy: 2022-06-01 do 2023-05-31
In this project we have realized the "iHEART simulator," a meticulously crafted computational tool designed for the comprehensive numerical simulation of human cardiac function. Rooted in robust mathematical foundations, this simulator integrates diverse facets of the heart's behavior. This includes electrophysiology, active and passive mechanics, fluid dynamics, valve kinetics, blood circulation, myocardial perfusion, and the conduction of electric signals across the torso. By harmonizing these elements, we have forged a unified and physically coherent model.
Our work is underpinned by the twin pillars of mathematical and numerical models, as well as their simulation. These cornerstones have allowed us to achieve a breadth of mathematical innovations. Among these are original analyses of core models, the conception of high-order approximation techniques, the establishment of streamlined algebraic solvers, optimal and scalable parallel preconditioners, and multifaceted coupling strategies encompassing partitioned, staggered, and monolithic approaches. Furthermore, we've pioneered novel paradigms for model order reduction and uncertainty quantification, while also introducing avant-garde concepts and methodologies in the realm of scientific machine learning.
To democratize the accessibility of our developed models and methodologies, we've introduced the versatile and user-friendly "lifex" library. Designed to be wielded effortlessly by both end-users and developers, this library furnishes a robust platform for performing cardiac numerical simulations, seamlessly harnessed within a large-scale parallel computing framework.
The iHEART simulator acts as an enlightening window into the intricate realm of human cardiac function. Its implications extend to the medical arena, bestowing indispensable assistance to clinicians in formulating medical decisions and devising strategies for maladies afflicting the human heart. These afflictions carry a profound societal and economic burden, with cardiovascular diseases being held accountable for an estimated 45% of fatalities in Europe, either directly or indirectly.
Through our dedicated research, we've delved into the mechanisms underpinning cardiac arrhythmias, valve irregularities, coronary ailments, and related conditions. In parallel, we've engaged in the mathematical refinement of treatment strategies tailored to these pathological scenarios.
Our work is underpinned by the twin pillars of mathematical and numerical models, as well as their simulation. These cornerstones have allowed us to achieve a breadth of mathematical innovations. Among these are original analyses of core models, the conception of high-order approximation techniques, the establishment of streamlined algebraic solvers, optimal and scalable parallel preconditioners, and multifaceted coupling strategies encompassing partitioned, staggered, and monolithic approaches. Furthermore, we've pioneered novel paradigms for model order reduction and uncertainty quantification, while also introducing avant-garde concepts and methodologies in the realm of scientific machine learning.
To democratize the accessibility of our developed models and methodologies, we've introduced the versatile and user-friendly "lifex" library. Designed to be wielded effortlessly by both end-users and developers, this library furnishes a robust platform for performing cardiac numerical simulations, seamlessly harnessed within a large-scale parallel computing framework.
The iHEART simulator acts as an enlightening window into the intricate realm of human cardiac function. Its implications extend to the medical arena, bestowing indispensable assistance to clinicians in formulating medical decisions and devising strategies for maladies afflicting the human heart. These afflictions carry a profound societal and economic burden, with cardiovascular diseases being held accountable for an estimated 45% of fatalities in Europe, either directly or indirectly.
Through our dedicated research, we've delved into the mechanisms underpinning cardiac arrhythmias, valve irregularities, coronary ailments, and related conditions. In parallel, we've engaged in the mathematical refinement of treatment strategies tailored to these pathological scenarios.
Extensive efforts have focused on developing and integrating core cardiac models, particularly in ventricular electrophysiology. However, as there is a notable lack of atrial models in the literature, our research emphasizes electrophysiological atrial models for both normal and pathological states. While collagen fiber-related anisotropic properties have been extensively studied in ventricles, we have refined active and passive mechanical contraction models for better accuracy.
Novel models for active force generation, including reduced force models through Artificial Neural Networks, have been introduced to balance computational efficiency with cellular intricacy. This work extends to active tension models for both atria and ventricles, essential for cardiac electromechanical simulations. Moreover, our investigation has delved into poroelastic and perfusion models for ventricles, shedding light on oxygen and nutrient supply via coronary arteries. We have also formulated comprehensive models for cardiac valve dynamics, covering leaflet movement within blood through 0D and differential models.
Our research revolves around merging electrophysiology and mechanics models, spanning various integration schemes—monolithic, partitioned, and staggered—encompassing ventricles, atria, and the entire heart. A remarkable feat is the creation of a comprehensive electromechanical heart model, representing crucial biomarkers. This progression ranges from basic electromechanical synchronization to intricate fluid-structure interaction and fluid-perfusion coupling.
To surmount algebraic challenges in space-time discretization, we have designed and implemented preconditioners. Solving the electromechanical model involved devising intergrid transfer operators, addressing electrophysiology and mechanics across distinct Finite Element meshes, capturing multiscale complexities. This extends to torso-heart, poroelastic, and perfusion models integrated with the iHEART simulator.
In addressing variability and uncertainty, we have enhanced reduced order models and uncertainty quantification methods using machine learning. This approach facilitates swift assessment of cardiac biomarkers by adjusting physiological parameters. Scientific Machine Learning has been employed for electrophysiology and electromechanics in physiological and pathological scenarios.
In pursuing personalized treatment, we have integrated patient-specific elements such as anatomies and functional data through refined computational pipelines. These pipelines automate simulations from image capture to mesh-based domain generation. Scientific Machine Learning techniques have been advanced, integrating electrophysiological data to personalize models for arrhythmic disorders.
Through extensive clinical collaborations, we have examined various heart pathologies including electrical dysfunctions (ischemia, tachycardia, atrial fibrillation), valve pathologies (aortic and mitral), and coronary occlusions. Our studies encompass numerical analyses and broader impacts on cardiac functionality, mechanics, and fluid dynamics. Innovative solutions have been proposed for compromised ventricular tissue perfusion.
Our project had a substantial impact, spanning academia, healthcare, and public awareness. Initiatives have been launched to promote Mathematics and Science, highlighting the influential role of the iHEART simulator in society's well-being.
Novel models for active force generation, including reduced force models through Artificial Neural Networks, have been introduced to balance computational efficiency with cellular intricacy. This work extends to active tension models for both atria and ventricles, essential for cardiac electromechanical simulations. Moreover, our investigation has delved into poroelastic and perfusion models for ventricles, shedding light on oxygen and nutrient supply via coronary arteries. We have also formulated comprehensive models for cardiac valve dynamics, covering leaflet movement within blood through 0D and differential models.
Our research revolves around merging electrophysiology and mechanics models, spanning various integration schemes—monolithic, partitioned, and staggered—encompassing ventricles, atria, and the entire heart. A remarkable feat is the creation of a comprehensive electromechanical heart model, representing crucial biomarkers. This progression ranges from basic electromechanical synchronization to intricate fluid-structure interaction and fluid-perfusion coupling.
To surmount algebraic challenges in space-time discretization, we have designed and implemented preconditioners. Solving the electromechanical model involved devising intergrid transfer operators, addressing electrophysiology and mechanics across distinct Finite Element meshes, capturing multiscale complexities. This extends to torso-heart, poroelastic, and perfusion models integrated with the iHEART simulator.
In addressing variability and uncertainty, we have enhanced reduced order models and uncertainty quantification methods using machine learning. This approach facilitates swift assessment of cardiac biomarkers by adjusting physiological parameters. Scientific Machine Learning has been employed for electrophysiology and electromechanics in physiological and pathological scenarios.
In pursuing personalized treatment, we have integrated patient-specific elements such as anatomies and functional data through refined computational pipelines. These pipelines automate simulations from image capture to mesh-based domain generation. Scientific Machine Learning techniques have been advanced, integrating electrophysiological data to personalize models for arrhythmic disorders.
Through extensive clinical collaborations, we have examined various heart pathologies including electrical dysfunctions (ischemia, tachycardia, atrial fibrillation), valve pathologies (aortic and mitral), and coronary occlusions. Our studies encompass numerical analyses and broader impacts on cardiac functionality, mechanics, and fluid dynamics. Innovative solutions have been proposed for compromised ventricular tissue perfusion.
Our project had a substantial impact, spanning academia, healthcare, and public awareness. Initiatives have been launched to promote Mathematics and Science, highlighting the influential role of the iHEART simulator in society's well-being.
The research in this project introduces significant advancements surpassing the current state-of-the-art. The key achievement lies in the rigorous mathematical rigor, from refining cardiac models to their numerical amalgamation. A standout accomplishment is the integration consolidating core models into a biophysically accurte multiphysics representation of cardiac function. This model serves for investigating cardiovascular pathologies, adapting to specific scenarios. It's also the basis for model personalization, a step towards a cardiac digital twin. This pioneering achievement places the research at the cardiac science forefront, advancing our understanding of dynamics and pathologies.
Collaboration among our research and clinical partners drives mathematical modeling towards impactful clinical application. This partnership addresses cardiac pathologies with societal and economic implications. Outcomes transcend current research, marking a pioneering leap in Math and bioengineering. The novel iHEART simulator introduces transformative healthcare technology, poised to benefit citizens and patients, ushering a new era of improved outcomes.
We have designed and shared software libraries for accurate numerical simulations of complex cardiac models. These efforts aim to transform healthcare by systematically treating cardiac ailments, potentially revolutionizing their approach and management. We are actively working to clinically implement our innovations, striving for substantial advancements in cardiac healthcare.
Collaboration among our research and clinical partners drives mathematical modeling towards impactful clinical application. This partnership addresses cardiac pathologies with societal and economic implications. Outcomes transcend current research, marking a pioneering leap in Math and bioengineering. The novel iHEART simulator introduces transformative healthcare technology, poised to benefit citizens and patients, ushering a new era of improved outcomes.
We have designed and shared software libraries for accurate numerical simulations of complex cardiac models. These efforts aim to transform healthcare by systematically treating cardiac ailments, potentially revolutionizing their approach and management. We are actively working to clinically implement our innovations, striving for substantial advancements in cardiac healthcare.