Researchers at Georgia Institute of Technology, Emory University and Oklahoma State University developed a left ventricle (LV) model compatible with clinical imaging tools for the study of heart diseases. The study was published in the Journal of Cardiovascular Magnetic Resonance and is entitled “Cardiovascular magnetic resonance compatible physical model of the left ventricle for multi-modality characterization of wall motion and hemodynamics.”
Heart failure is a worldwide health concern, affecting over 2% of the adult population older than 65 years. The condition can result from LV structural anomalies, valvular pathologies, deficient electrical conduction or hypertension. A timely diagnosis of heart failure can be challenging, so studies assessing the complex mechanical interactions between the anatomical structures and the heart’s pumping function are required to improve and create more accurate diagnostic measures for an earlier intervention.
Several studies have reported that one parameter important for physicians to assess is intra-ventricular fluid dynamics, which is linked to LV wall motion and valvular kinematics. However, clinically applicable fluid-structure interaction (FSI) models of the left heart are challenging when applying non-invasive medical imaging techniques like cardiovascular magnetic resonance (CMR) for validation.
The goal of the study was to develop a clinically relevant left heart simulator using a LV physical model that is CMR compatible.
The team developed an LV model from optically clear flexible silicone rubber, and its structure was based on a healthy patient’s LV geometry during peak systole. The LV was attached to a left heart simulator comprising an aorta, atrium, and systemic resistance and compliance elements. LV wall motion and flow field measurements were assessed.
Researchers found that their CMR-compatible LV physical model was able to accurately simulate the physiological hemodynamic environment of the LV and to provide experimental accurate CMR data for FSI model verification and validation applications. The model also allows the analysis of the effects of altering anatomical variables on LV function under normal and disease conditions.
The team concluded that their LV model provided a one-to-one link between data obtained using laboratory experimental modalities and clinical CMR. This is important for validating LV FSI models that use CMR data as an input for simulations. The team believes that their model provides scientists with a platform of structural heart disease and valvular/pathological conditions affecting cardiac function that will ultimately contribute to the translation of computational models of the left heart into the clinical practice, including novel diagnostic indices, and personalized therapeutics and surgical interventions.