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Research Overview

Current projects in the Marsden Lab

This is a list of some of our ongoing projects.   Our research spans numerical methods development to clinical application of cardiovacsular modeling methods in pediatric and adult cardiovascular disease.  

Pediatric and Congenital Heart Disease

Collaborators: Dr. Jeffrey Feinstein, Tain Yen Hsia, Richard Figliola, Marlene Rabinovitch, Doff McElhinney, John Eaton
Lab members:   Aekaansh Verma, Vijay Vedula, Nicole Schiavone, Weiguang Yang, Chris  Elkins, Melody Dong
We apply computational modeling to pediatric and congenital heart disease for surgial planning, medical device design, and understanding biomechanics of disease progression.  Current applications include single-ventricle physiology, Tetralogy of Fallot, and pulmonary hypertension.  In single ventricle physiology, we have developed novel surgical methods, including the Fontan Y-graft and the Assisted Bidirectional Glenn.   In pulmonary hypertension, we are using multiscale modeling to understand mechanical forces leading to disease progression and remodeling of the vessel wall in the proximal and distal pulmonary arterial bed.   In Tetralogy of Fallot, we are combining experimental and computational methods to understand the wide variation of valve performance and longevity in children recieving surgical replacement valves. 

Coronary Artery Hemodynamics

Collaborators: Andrew Kahn, MD, PhD, Jane Burns, MD, Jack Boyd, MD

Lab members:  Owais Khan, Justin Tran, Noelia Grande Gutierrez, Siqi Xue


Our lab is performing patient specific modeling of coronary artery hemodynamics in children and adults.  In adults, we are simulating coronary bypass graft surgery to elucidate causes of vein graft failure.   Vein grafts fail at alarmingly high rates of nearly 50% within 5-10 years of surgery.   We use multiscale modeling tools to characterize and compare the in vivo environment of arterial and vein grafts to uncover mechanisms for vein graft failure.  We also use virtual surgery to evaluate optimal surgical methods for individual patients.  In children, we are using patient specific modeling to evaluate the risk of thrombosis in patients with coronary artery aneurysms caused by Kawasaki disease.  

Optimization and Uncertainty Quantfication

Collaborators: Daniele Schiavazzi, John Dennis
Lab members:  Aekaansh Verma, Casey Fleeter, Justin Tran, Jongmin Seo
We are developing tools for optimization of cardiovascular geometries, building on our previous optimization work using derivative-free surrogate methods.  We are investigating a range of clinically relevant cost functions and constraints including energy efficiency, wall shear stress, flow distribution and surgical feasibility.   We have recently extended our surrogate optimization code to run in parallel in a high performance computing environment.   We are also developing a framework to perform comprehensive uncertainty analysis for cardiovascular simulations that can give confidence intervals on simulation outputs, taking into account sources of noise in simulation parameters such as noise in image data, variable flow rates, and uncertainty in outflow resistances.   These tools can then be efficiently incorporated into an optimization algorithm   for robust design.

Computational methods for Patient-Specific Modeling

Collaborators: Yuri Bazilevs, Irene Vignon-Clementel
Lab members:  Vijay Vedula, Justin Tran, Gabriel Maher, Ju Liu
We are developing novel computational methods for patient-specific modeling and blood flow simulation.   We are working to improve image segmentation methods using machine learning and convolutional neural networks.  We are also developing fluid strucutre interaction methods to account for ventricular motion and valves.   Multi-domain modeling allows us to couple 3D fluid dynamics simulations to reduced order models of the circulatory physiology, and we have developednovel coupling methods and linear solver methods to enhance efficiency.  We have recently proposed a novel unified formulation for fluid structure interaction using finite elements.