13 March 2017 Subject-specific left ventricular dysfunction modeling using composite material mechanics approach
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Abstract
Diverse cardiac conditions such as myocardial infarction and hypertension can lead to diastolic dysfunction as a prevalent cardiac condition. Diastolic dysfunctions can be diagnosed through different adverse mechanisms such as abnormal left ventricle (LV) relaxation, filling, and diastolic stiffness. This paper is geared towards evaluating diastolic stiffness and measuring the LV blood pressure non-invasively. Diastolic stiffness is an important parameter which can be exploited for more accurate diagnosis of diastolic dysfunction. For this purpose, a finite element (FE) LV mechanical model, which works based on a novel composite material model of the cardiac tissue, was utilized. Here, this model was tested for inversion-based applications where it was applied for estimating the cardiac tissue passive stiffness mechanical properties as well as diastolic LV blood pressure. To this end, the model was applied to simulate diastolic inflation of the human LV. The start-diastolic LV geometry was obtained from MR image data segmentation of a healthy human volunteer. The obtained LV geometry was discretized into a FE mesh before FE simulation was conducted. The LV tissue stiffness and diastolic LV blood pressure were adjusted through optimization to achieve the best match between the calculated LV geometry and the one obtained from imaging data. The performance of the LV mechanical simulations using the optimal values of tissue stiffness and blood pressure was validated by comparing the geometrical parameters of the dilated LV model as well as the stress and strain distributions through the LV model with available measurements reported on the LV dilation.
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Seyed Mohammad Hassan Haddad, Seyed Mohammad Hassan Haddad, Elham Karami, Elham Karami, Abbas Samani, Abbas Samani, } "Subject-specific left ventricular dysfunction modeling using composite material mechanics approach", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101371V (13 March 2017); doi: 10.1117/12.2254625; https://doi.org/10.1117/12.2254625
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