Patient-Specific Computational Modeling of Cardiac Mechanics
AbstractComputational models are an absolutely necessary tool in many engineering disciplines. For example, computational models are used to predict tomorrow’s weather, to optimize the aerodynamics of new aircraft, and to ensure buildings and bridges are safe. The use of computational models in field of biomedical engineering is emerging, but is still limited to the research level. This limitation is mainly due to the complexity and multi-scale nature of the underlying physiological processes inside the human body. Nevertheless, advances in medical imaging techniques now provide a wealth of information about structure and kinematic, that could potentially be used to parameterize these mathematical models in such a way that it is possible to create a virtual representation of an organ of the individual. With such a calibrated model at hand, we can estimate features that are impossible to measure with medical imaging, and such a model would therefore be useful for diagnostic purposes. Furthermore, we could potentially use this model predict the outcome of different treatment strategies and use it to design and optimize treatment. However, some on the challenges in the creation of such models lies in the lack of methods to accurately and efficiently estimating model parameters that best describes the measured observations. In this thesis we have developed a framework to effectively build a virtual heart of the individual patient, so that measurements made in the clinic can be incorporated into the underlying mathematical model. Such virtual hearts have been used to study the mechanics of the heart in different patient groups. Furthermore, we evaluated different biomarkers that may have potential clinical value, and evaluated the performance of the method. These simulations can be performed on a regular laptop in just a few hours, which means that this framework can potentially be included as a diagnostic toolbox in the clinic.
List of papers
|Paper 1: Gabriel Balban, Henrik Finsberg, Hans Henrik Odland, Marie E. Rognes, Stian Ross, Joakim Sundnes, and Samuel Wall. High-resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle. International journal for numerical methods in biomedical engineering (2017). DOI:10.1002/cnm.2863. The article is included in the thesis. Also available at: http://hdl.handle.net/10852/62016|
|Paper 2: Henrik Finsberg, Gabriel Balaban, Stian Ross, Trine F. Håland, Hans Henrik Odland, Joakim Sundnes, and Samuel Wall. Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model. Journal of Computational Science (2017). DOI: 10.1016/j.jocs.2017.07.013. The article is included in the thesis. Also available at: http://hdl.handle.net/10852/62017|
|Paper 3: Henrik Finsberg, Ce Xi, Ju Le Tan, Liang Zhong, Martin Genet, Joakim Sundnes, Lik Chuan Lee, and Samuel T. Wall. Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization. International journal for numerical methods in biomedical engineering (2018). The paper is not available in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1002/cnm.2982|
|Paper 4: Henrik Finsberg, John Aalen,Camilla K. Larsen, Espen Remme, Joakim Sundnes, Otto A. Smiseth, and Samuel T. Wall. Assessment of regional myocardial work from a patient-specific cardiac mechanics model. To be published. The paper is not available in DUO awaiting publishing.|