Title: functional analysis of a large scale database of EVAR to address long term outcome failures
Background: Secondary interventions after endovascular repair of AAA (EVAR) remain significant
despite substantial improvements of aortic endografts over multiple generations. Understanding the
interactions between the aortic anatomy, aortic wall characteristics, biomechanical forces and the
endograft to be implanted could potentially predict poor outcomes and future secondary
interventions. With the evolution of stent technology and the continued development of
fenestrated/branched stent-grafts, predicting outcomes has become increasingly important.
Additionally, expounding on this interaction has the potential to promote the development of
modified stent-grafts.
Goal:
We are developing a cyber-physical and community system, with systematic long term follow-up and
comprehensive analysis of patients undergoing endovascular repair of abdominal aortic aneurysms in
order to assess long-term complications.
We propose to use our computational surgical methodology in order to:
(i)
Provide relevant data to understand and predict poor outcomes.
(ii)
Increase computational surgery awareness in the community and improve surgical
outcomes.
(iii)
Engage both medical trainees and computational science students on a global stage to
learn by practicing and producing, at no cost to the stakeholder, a post processing analysis
of the long term follow-up data.
(iv)
Improve industry collaboration to a level that can promote synergistic work among all
stakeholders and support healthy competition.
Research Work:
We intend first to use FreeFem as the core engine of a distributed computing solution of
hemodynamic flow adapted to our data base of patients. We propose second to use the volunteer
computing model to get a scalable solution with long term follow up of EVAR’s big data.
We have developed a semi-automatic segmentation software, that produces the 3D reconstruction
and co-registration to quantitatively assess stent-graft changes over time, correct segmentation of
major collateral arteries and correct exclusion of calcifications and of the stentgraft. We are currently
developing a web interface to make that analysis processed by volunteers in selected academic
partner medical schools.
This PhD project addressing hemodynamic solution and the construction of functional indicators will
be the natural complement of that initial effort in quantitative image analysis.
Our hypothesis is that functional indicators extracted from hemodynamic flow combined to anatomic
indicators annotating our large data base of patients will have predictive value provided that we use
the proper reduction model and classification of patients.
To achieve that goal we will need a large amount of flow calculation for each patient CTs.
The goal of the PhD will be to use the FreeFEM framework to get an efficient Navier Stokes
hemodynamic solution that is robust to a broad spectrum of geometry and boundary conditions,
works on PC platform with limited memory, and can be used to compute functional indicators with a
given level of accuracy. This software module will be then embedded into the BOINC framework or
similar.
This PhD project that is part of an international network coordinated effort with a number of senior
vascular surgeons and computational scientist will get support from the NSF Industry Academic
Center cybhor.cs.uh.edu .
The PhD candidate will need to have a balanced skill set of solid applied mathematics and rigorous
software development.
The PhD candidate will integrate an interdisciplinary team at the Houston Methodist Hospital, and
will be supported as a Research Assistant from University of Houston during his stay in the USA.
A dual PhD of University of Paris 6 and University of Houston will be granted to a successful
candidate.
Marc Garbey
http://www.computationalsurgery.org