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Generating a Database Through Parametric Bi-Ventricular Modeling and Finite Element Analysis of End-Diastolic Mechanical Behavior of Human Heart

Aghigh, Sahand | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53750 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Asghari, Mohsen
  7. Abstract:
  8. Heart failure is one of the leading causes of death around the world and diastolic dysfunction is the main culprit behind about 30 percent of them. End-diastolic pressure-volume relationship is one of the most important indices of ventricles diastolic function, however invasive nature of assessment, has hindered its use in clinical applications. Despite the advances in computational modeling and bio-mechanical simulations, computational cost of these procedures often renders the computational methods unsuitable for clinical implications. Machine learning methods are usually the proper substitute in such cases. However, this method requires a large set of pre-calculated data for training, preparing of which is not feasible by experimental methods. Main objective of this project is to generate a database of end-diastolic pressure-volume relationships through parametric modeling of a bi-ventricular heart model and simulating the passive inflation of ventricles by finite element method. A dimensionally parametric bi-ventricular model is designed and by assuming a hyperelastic orthotropic structure for the ventricle walls, mechanical behavior of the model is defined through a strain energy function and the fiber directions are calculated by a rule-based algorithm. By solving an inverse problem, an average model is chosen as a reference model which is able to reproduce the experimental data of a normal healthy subject with a coefficient of determination of 96 percent. With automating all of the procedures corresponding to modeling, simulation and post-processing a database of more than 82000 finite element results and pressure-volume data with respect to various geometries and myocardium behaviors is generated. Comparing the database of results with experimental data obtained from a diverse population indicates that the extent of variation considered for the database is valid. Creating this database is an important step towards creating a machine learning model for non-invasive estimation of diastolic function. Moreover, by analyzing the data stored in database, the effect of anatomy and physiology of ventricles on diastolic function of left ventricle is studied and a mathematical relationship is proposed for non-invasive assessment of right ventricle’s end-diastolic pressure-volume relationship
  9. Keywords:
  10. Finite Element Method ; Biomechanical Modeling ; Diastolic Function ; Passive Myocardium ; Bi-Ventricular Model ; End-Diastolic Pressure-Volume Relationship ; Heart Diseases

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