A Patient-Specific Computational Fluid Dynamic Model for Hemodynamic Analysis of Left Ventricle Diastolic Dysfunctions

This work presents a computational fluid dynamic (CFD) model to simulate blood flows through the human heart's left ventricles (LV), providing patient-specific time-dependent hemodynamic characteristics from reconstructed MRI scans of LV. These types of blood flow visualization can be of great asset to the medical field helping medical practitioners better predict the existence of any abnormalities in the patient, hence offer an appropriate treatment. The methodology employed in this work processed geometries obtained from MRI scans of patient-specific LV throughout a cardiac cycle using computer-aided design tool. It then used unstructured mesh generation techniques to generate surface and volume meshes for flow simulations; thus provided flow visualization and characteristics in patient-specific LV. The resulting CFD model provides three dimensional velocity streamlines on the geometries at specific times in a cardiac cycle, and they are compared with existing literature findings, such as data from echocardiography particle image velocimetry. As an important flow characteristic, vortex formation of the blood flow of healthy as well as diseased subjects having a LV dysfunction condition are also obtained from simulations and further investigated for potential diagnosis. The current work established a pipeline for a non-invasive diagnostic tool for diastolic dysfunction by generating patient-specific LV models and CFD models in the spatiotemporal dimensions. The proposed framework was applied for analysis of a group of normal subjects and patients with cardiac diseases. Results obtained using the numerical tool showed distinct differences in flow characteristics in the LV between patient with diastolic dysfunction and healthy subjects. In particular, vortex structures do not develop during cardiac cycles for patients while it was clearly seen in the normal subjects. The current LV CFD model has proven to be a promising technology to aid in the diagnosis of LV conditions leading to heart failures.