Keshav Kohli

BME PhD Thesis Proposal Presentation

Date: 4/01/2020

Time: 9:30AM

Location: BlueJeans (call information below)

Committee Members:

- John Oshinski, PhD (Advisor, BME, Emory University)

- Ajit Yoganathan, PhD (Co-advisor, BME, Georgia Tech)

- Lakshmi Prasad Dasi, PhD (BME, Georgia Tech)

- Vasilis Babaliaros, MD (Cardiology, Emory University)

- Carlo De Cecco, MD, PhD (Radiology, Emory University)

Title: Image-based Modeling of Hemodynamic Outcome in Patients undergoing Transcatheter Mitral Valve Replacement

Abstract:

Mitral valve disease affects over 4 million people in the United States and is a significantly debilitating condition if left untreated. 49% of patients diagnosed with mitral valve disease are deemed at too high risk for conventional surgical options due to old age and commodities. These patients are candidates for a new procedure called transcatheter mitral valve replacement (TMVR) which has emerged as a potential alternative to surgery. During this procedure, a prosthetic mitral valve is implanted in place of the diseased valve through a minimally invasive approach without the need for cardiopulmonary bypass. While overall clinical results have been promising, up to 54% of patients can have a potentially fatal obstruction of blood flow leaving the heart due to the size of the replacement valve device. This research proposal aims to develop and evaluate novel imaging and in silico modeling techniques to better predict which patients will have this severe blood flow obstruction. Computed tomography and 4D flow magnetic resonance imaging will be used in combination with computational fluid dynamics modeling to generate personalized predictions of blood flow in patients after a virtual TMVR procedure. The outcomes of this project will enable clinicians to better understand the patient-specific risk of blood flow obstruction in patients being evaluated for TMVR. Using the developed techniques, clinicians may better customize the TMVR procedure for each individual patient to ultimately improve clinical outcomes. 

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125 312 672 0

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