Cardiac Modeling

In recent years, the field of computational cardiac modeling and simulation has matured in both scope and methodology such that it can contribute significantly to the present understanding of heart physiology and disease. The computational cardiac modeling effort involves mathematicians and computer scientists working collaboratively with both experimentalists and clinicians to address current challenges in cardiology through basic research and industrially driven innovation projects.


Relating Structural Features in ARVC Patients to Clinical Outcomes
McLeod SK, Saberniak J, Leren IS, Haugaa KH, Wall S

Analysing structural features common in a population using computational methods can provide quantitative descriptors of structural abnormalities that can help to better understand disease manifestations. These descriptors can be correlated to clinical outcomes to potentially establish predictors of adverse clinical outcome.

We aim to go beyond current measures of ventricular structure abnormalities such as volumes and ejection fraction by coupling these with automatically identified and structural abnormalities in ARVC patients extracted from CMR. Statistical analysis was performed to determine how structural features are related to clinical diagnostic indices. More specifically, we compute the shape modes most descriptive of clinically derived measures in an ARVC cohort of patients and compare the correlation of these with history of arrhythmias to the correlation with the clinical measures alone.

Results are shown on the right for the correlation with history of VT/VF between the clinical measures alone and shapes extracted from CMR (a) combined with the clinical measures (b), and the RVEDV (right ventricular end diastolic volume) shape given as an example at ±1 standard deviation (c).

Using statistical methods, the dominant structural patterns can be extracted in the population with respect to clinical measures, defining improved methods to relate structure to arrhythmic events.


Patient-Specific Simulation to Measure Mechanical Dysfunction
Finsberg HF, Balaban G, Sundnes J, Ross S, Odland HH, Wall S

Image-based patient-specific cardiac modeling has emerged as a potential tool for future medical diagnostic and treatment planning. By relating mechanical information observed in medical images to physical processes, mathematical models can provide us with additional insight into the cardiac function or dysfunction of the individual.

The need for building adequate patient-specific models that captures the geometrical information as well as the underlying biophysical processes is recognized as one of the key challenges in modern bioengineering. Adjoint-based data assimilation offers a new way of fitting high dimensional parameters to clinical measurements, and makes it possible to create a simulation of a patient’s heart, that moves in the same way as what is observed in the medical images. Moreover, from these simulations we can extract features that are otherwise impossible to measure without surgical interventions. Such features include indices of myocardial contractility and fiber stress. By combining strain data obtained using 4D echocardiography methods, with left ventricular pressure and volume, we have used these methods to show that patients suffering from left bundle branch block have significant decreases in estimated myocardial contractility.

To the left, end systolic elastance estimated from perturbed model for healthy controls and patients with left bundle branch block. Dots show individuals, while bars show the mean.  To the right, Mean and +/- 1 std of time varying relative active fibre shortening for healthy controls (blue) and patients with left bundle branch block (red).

Heart SFI