Hemodynamic Assessment of the Pathological Left Ventricle Function under Rest and Exercise Conditions

Purpose: The analysis of pathological human left ventricular hemodynamics using high-resolved image-based blood flow simulations shows a major potential for examining mitral valve insufficiency (MI) under exercise conditions. Since capturing and simulating the patient-specific movement of the left ventricle (LV) during rest and exercise is challenging, this study aims to propose a workflow to analyze the hemodynamics within the pathologically moving LV. Methods: Patient-specific ultrasound (US) data of ten patients with MI in different stages were captured with three-dimensional real-time echocardiography. US measurements were performed while patients were resting and while doing handgrip exercise (2-4 min work). Patient-specific hemodynamic simulations were carried out based on the captured ventricular wall movement. Velocity and kinetic energy were analyzed for rest and exercise and for the different MI stages. Results: The results reveal a dependency of the kinetic energy over time in the ventricular volume curves. Concerning the comparison between rest and exercise, the left ventricular function reveals lower systolic kinetic energy under exercise (kinetic energy normalized by EDV; mean +/- standard deviation: rest = 0.16 +/- 0.14; exercise = 0.06 +/- 0.05; p-value = 0.04). Comparing patients with non-limiting (MI I) and mild/moderate (MI II/III) MI, lower velocities (mean +/- standard deviation: non-limiting = 0.10 +/- 0.03; mild/moderate = 0.06 +/- 0.02; p-value = 0.01) and lower diastolic kinetic energy (kinetic energy normalized by EDV; mean +/- standard deviation: non-limiting = 0.45 +/- 0.30; mild/moderate = 0.20 +/- 0.19; p-value = 0.03) were found for the latter. Conclusion: With the proposed workflow, the hemodynamics within LVs with MI can be analyzed under rest and exercise. The results reveal the importance of the patient-specific wall movement when analyzing intraventricular hemodynamics. These findings can be further used within patient-specific simulations, based on varying the imaging and segmentation methods.