Trans-valvular Flow in the Human Aorta
Visualization of vortex structures and streamlines, colored using contours of axial (streamwise) velocity, for flow through (left) healthy tricuspid and (right) type-1 bicuspid aortic valves, during the deceleration phase of ventricular systole. This figure was featured in UT Austin’s Texascale magazine.
For my PhD dissertation, I have been researching the influence of aortic valve (AV) function on blood flow in the human aorta. The goal of my work is to understand how different pathological conditions of the AV influence blood flow patterns in the proximal aorta and determining whether local hemodynamic information can be used to predict the condition and mobility of the valve leaflets.
To illustrate the differences in blood flow patterns arising from healthy and dysfunctional AVs, the alongside figure shows a comparison of vortex structures colored by axial velocity contours resulting from healthy tricuspid and partially fused bicuspid AVs. Regions colored in red correspond to forward flow while those in blue represent reverse flow. The healthy tricuspid valve results in an axisymmetric aortic jet with little reverse flow close to the aortic sinus, while the bicuspid valve leads to a deflected, elliptical jet which impacts the aorta lumen and results in a large recirculation bubble shown in blue. These differences in hemodynamic characteristics can have severe implications for the health of the proximal aorta and are described in detail in my recently published work.
A Novel modality for remote continuous monitoring of transcatheter aortic valve function
3D model of a generic self-expanding transcatheter aortic valve (TAV) implanted in a model aorta. The metallic cage represents the stent inside which the bioprosthetic valve (yellow) is mounted.
Transcatheter aortic valve replacement (TAVR) is a recently developed, minimally-invasive valve replacement procedure which is projected to become the standard-of-care for valve replacements in the near future. However, the bioprosthetic valves used in TAVR are susceptible to acute and chronic leaflet thrombosis which lead to reduced leaflet motion (RLM) and consequently disturbed flow and increased risk of thromboembolic events like strokes and heart attacks. My work focuses on testing the possibility of employing TAVs with embedded pressure sensors for detection of onset of valve failure, and facilitating early treatment. The way such a technology is expected to function is illustrated in the figure below:
Patients with failing aortic valves receive a sensorized TAV. When a person with such a TAV experiences RLM (symptomatic or asymptomatic), the change in leaflet mobility will result in downstream disturbed flow.
The embedded sensors will detect changes in hemodynamic pressure in the valve vicinity and transmit the signals periodically to an external, internet-enabled device.
This device will process the signals to make a prediction of the valve health and communicate this prediction to the patient’s assigned cardiologist.
The cardiologist can initiate appropriate diagnostic/ therapeutic action.
Schematic describing the function of sensorized TAV technology.
To test whether such a technology could work, I generated computer simulation data for physiological flow through healthy and dysfunctional TAVs (see left figure). From a dataset of 28 cases (84 leaflets), I gathered hemodynamic pressure measurements at strategic locations on the TAV stent. From these measurements I create hemodynamic signatures of healthy and RLM leaflets (see right figure) and train supervised learning methods to establish correlation between these signatures and leaflet status (Healthy/ RLM).
The supervised learning models are tuned to optimal performance via cross validation and these models are capable of accurate retrospective and prospective detection of RLM, even in very mildly stenotic cases.
Processed signals from Healthy (blue) and RLM (red) leaflets show natural class separation and facilitate detection of leaflet dysfunction.
This research won the 10th Mirowski Discovery Award for innovative cardiovascular research by the Johns Hopkins School of Medicine (PI: Dr. Stefano Schena). You can read more about this project in my recent paper in Cardiovascular Engineering and Technology. Or if you prefer to watch a presentation, check the following YouTube video I recorded for the 73rd annual meeting of the American Physical Society’s Division of Fluid Dynamics conference.