Integrated computational models of the heart have long been developed in order to gain a greater understanding of the normal and pathological function of the heart. A key component of these models is the myofiber architecture of the muscle tissue which dictates the heart's electrical and mechanical functions in health and disease. Due to computational concerns and the lack of high-resolution imaging data of the human myofiber architecture, previous computational models were based on animal data and were defined for just the right and left ventricles. With the development of more efficient algorithms for electromechanical modeling as well as advances in computational power and imaging technologies such as multi-slice CT and diffusion tensor MRI, it is now feasible to develop more complex and detailed models for the human heart. The long term goal of this project is to develop and validate a 4D multi-scale finite-element (FE) computational model of the 4-chamber human heart capable of realistically simulating normal and abnormal cardiac anatomy and function based on state-of-the-art human imaging data. This proposal outlines the first step of the project which will focus on modeling the normal functioning human heart and its variations in a population. This normal cardiac model will provide the necessary foundation from which we may simulate disease processes in future work. The proposed heart model has enormous potential in education and research in biomechanics, biophysics, and physiology, providing a deeper understanding of the complexity of the human heart at multiple levels and the basis of its function in health and disease. It will provide a realistic framework to link structure and function from the cellular level to that of the intact human heart and to a group of anatomical variations found in the general population. The driving application for the cardiac model will be as a simulation tool for imaging research and education. When combined with a digital phantom for the human body, the model will provide realistic, predictive multi-modality patient imaging data from anatomically diverse subjects in health and disease. With this ability, the model will provide a unique and vital tool to quantitatively evaluate and compare current and emerging 4D imaging techniques used in the diagnosis of cardiovascular disease. It may also provide simulated data using various procedures and scanning parameters to train physicians.

Public Health Relevance

The goal of this proposal is to develop a computational model of the human heart, spanning biophysical scales from cell to population, capable of realistically simulating normal and abnormal cardiac anatomy and function. The proposed heart model has enormous potential in education and research, providing a deeper understanding of the complexity of the human heart and the basis of its function in health and disease. It will provide a vital simulation tool for understanding the underlying mechanisms of cardiovascular disease and its effect on cardiac function and for evaluating and improving existing and emerging 4D imaging techniques used in its diagnosis.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL091036-04
Application #
8215727
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Adhikari, Bishow B
Project Start
2009-04-01
Project End
2013-07-31
Budget Start
2012-02-01
Budget End
2013-07-31
Support Year
4
Fiscal Year
2012
Total Cost
$498,049
Indirect Cost
$69,477
Name
Duke University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Vadakkumpadan, Fijoy; Arevalo, Hermenegild; Ceritoglu, Can et al. (2012) Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology. IEEE Trans Med Imaging 31:1051-60
Aguado-Sierra, Jazmin; Krishnamurthy, Adarsh; Villongco, Christopher et al. (2011) Patient-specific modeling of dyssynchronous heart failure: a case study. Prog Biophys Mol Biol 107:147-55
Veress, Alexander I; Segars, W Paul; Tsui, Benjamin M W et al. (2011) Incorporation of a left ventricle finite element model defining infarction into the XCAT imaging phantom. IEEE Trans Med Imaging 30:915-27