A revolutionary finite element model that integrates our knowledge of the human heart across multiple scales and physical domains.
Explore the TechnologyImagine a future where your cardiologist could test dozens of personalized treatments on a precise digital replica of your heart before ever prescribing you a single medication. This vision is rapidly becoming reality thanks to groundbreaking computational models known as heart simulators.
Among these, one system stands out for its unprecedented complexity and accuracy: UT-Heart, a revolutionary finite element model that integrates our knowledge of the human heart across multiple scales and physical domains. By bridging the critical gap between microscopic cellular processes and whole-organ function, UT-Heart represents a paradigm shift in how we understand, diagnose, and treat cardiovascular diseases—which remain the leading cause of death worldwide, claiming an estimated 23.6 million lives annually by 2030 1 .
From molecular interactions to organ-level function
Personalized treatment testing and surgical planning
The fundamental challenge in understanding the heart lies in its nature as what scientists call a multiscale system. Consider what happens with each heartbeat: it begins with electrical impulses at the molecular level (nanometers), which trigger biochemical reactions inside individual heart cells (micrometers), causing them to contract in coordination with their neighbors to form tissue (millimeters), ultimately resulting in the deformation of the entire heart (centimeters) and the ejection of blood into circulation 4 .
UT-Heart addresses this complexity through finite element modeling, a computational technique that divides the heart into manageable segments called elements, each governed by mathematical equations describing the relevant physics at that specific location and scale. Unlike earlier models that simulated only isolated aspects of heart function—such as electrical signals or mechanical contraction—UT-Heart integrates electrophysiology, tissue mechanics, and blood fluid dynamics into a unified framework 4 .
Electrical signal propagation
Muscle contraction and deformation
Blood flow and pressure
UT-Heart's computational framework consists of several interconnected modules, each representing a different aspect of cardiac function:
This component simulates the heart's intricate electrical conduction network, modeling how action potentials travel from cell to cell to coordinate contraction.
At the cellular level, UT-Heart incorporates sophisticated models of calcium-induced calcium release and cross-bridge cycling.
This module calculates how the coordinated contraction of billions of individual cells produces the twisting and squeezing motion of the heart.
The most challenging aspect incorporates computational fluid dynamics to simulate how blood moves through the heart chambers.
A particularly powerful feature of UT-Heart is its ability to incorporate patient-specific data through a process called personalization 2 . By integrating medical imaging data from MRI or CT scans, researchers can create a geometrically accurate digital replica of an individual patient's heart.
| Scale | Biological Components | Physical Principles | Mathematical Approach |
|---|---|---|---|
| Molecular | Ion channels, signaling proteins | Electrochemistry, protein dynamics | Ordinary differential equations |
| Cellular | Cardiomyocytes, fibroblasts | Electrophysiology, contraction | Systems of ODEs/PDEs |
| Tissue | Myocardial sheets, collagen network | Soft tissue mechanics, electrical propagation | Partial differential equations |
| Organ | Heart chambers, valves | Solid mechanics, fluid dynamics | Multiphysics coupling algorithms |
| System | Circulation, nervous control | Hemodynamics, control systems | Lumped parameter models |
Researchers used a specialized version of the UT-Heart framework called MyoFE to explore how cellular-level abnormalities lead to the disorganized muscle fiber patterns (fiber disarray) characteristic of HCM.
Some heart cells generating excessive contractile force
Deficient force generation in certain cell populations
Replacement of healthy muscle cells with stiff scar tissue
The simulations revealed several crucial insights into HCM pathophysiology. The model demonstrated that all three types of cellular abnormalities could independently lead to significant fiber disarray, though through different mechanical mechanisms 7 .
| Abnormality Type | Degree of Fiber Disarray | Primary Location of Disarray | Reduction in Ejection Fraction |
|---|---|---|---|
| Hypercontractility | Moderate | Epicardial predominance | 12-18% |
| Hypocontractility | Moderate to Severe | Transmural | 15-22% |
| Fibrosis | Severe | Border zones of scar tissue | 20-30% |
| Combined Pathology | Most Severe | Diffuse | 25-35% |
| Simulation Type | Number of Finite Elements | Typical Runtime | Parallel Processing Cores |
|---|---|---|---|
| Electrophysiology Only | 500,000 - 1,000,000 | 4-6 hours | 64-128 |
| Electromechanics | 1,000,000 - 2,000,000 | 12-24 hours | 128-256 |
| Full Multiphysics | 2,000,000 - 5,000,000 | 2-5 days | 256-512 |
| Long-term Remodeling | 1,500,000 - 3,000,000 | 1-2 weeks | 512-1024 |
Behind sophisticated heart simulations like UT-Heart lies an array of specialized computational tools and mathematical approaches.
| Tool Category | Specific Examples | Function in Cardiac Modeling | Real-World Analogy |
|---|---|---|---|
| Mathematical Frameworks | Finite element method, Ordinary differential equations, Partial differential equations | Discretize continuous biological systems into solvable mathematical problems | Laboratory instrumentation |
| Coupling Algorithms | Loosely coupled schemes, Tightly coupled iterations, Monolithic approaches | Manage interactions between different physics domains (electrical, mechanical, fluid) | Experimental protocols |
| Personalization Methods | Image registration, Parameter estimation, Machine learning | Customize generic models to represent specific individuals | Patient-derived samples |
| Visualization Systems | Volume rendering, Streamline visualization, Tensor display | Interpret and communicate complex simulation results | Microscopy and imaging |
This computational toolkit enables researchers to perform virtual experiments that would be impossible, impractical, or unethical in living subjects.
Investigators can selectively "knock out" specific ion channels throughout the entire heart—all reversible interventions with the click of a mouse.
The UT-Heart framework continues to evolve through ongoing research at the University of Twente's Cardiovascular Health Technology Centre and other institutions worldwide. Current development focuses on enhancing the model's clinical applicability through improved personalization techniques and expanded pathology modeling 3 .
UT-Heart has been used to optimize electrode placement and timing patterns for individual patients, potentially improving the notoriously variable success rate of this therapy 2 .
The model shows great promise in planning complex surgeries for congenital heart diseases, allowing surgeons to test various surgical approaches virtually before operating on actual patients.
Despite its impressive capabilities, UT-Heart faces several significant challenges on the path to routine clinical adoption. The immense computational demands of full-heart, multiphysics simulations currently limit real-time applications.
UT-Heart represents far more than an academic curiosity—it embodies a fundamental shift in how we understand and interact with the human heart.
Connects disparate specialties in cardiovascular science
Speeds development of new cardiovascular therapies
Customizes treatments to individual patient characteristics
"Such a simulator could be used as a tool not only in basic science but also in clinical settings" 2 . From revealing the fundamental mechanisms of deadly heart conditions to guiding life-saving interventions, UT-Heart and similar multiscale models are poised to revolutionize cardiovascular medicine in the decades to come.