Multi-scale computational modeling of coronary blood flow: application to fractional flow reserve.

Introduction. Fractional flow reserve (FFR) is presently an invasive coronary clinical index. Non-invasive CT imaging combined with computational coronary flow modelling may reduce the patient’s burden of undergoing invasive testing. Research statement. The ability to obtain information of the hemodynamic significance of detected lesions would streamline decision making in escalation to invasive angiography. Methods. A reduced order (lumped parameter) model of the coronary vasculature was further developed. The model was used in the assessment of the roles of structure and function on the FFR. Sophisticated methods were used to elicit numerical solutions. Further, CT imaging (n = 10) provided multiple porcine geometries based upon algorithms encoded within an existing scientific platform. Results. It was found that the length of large vessel stenosis and presence of microvascular disease are primary regulators of FFR. Further, the CT data provided a basis to investigate relationships between coronary geometry (structure) and blood flow (function) attributes. Discussion. The presented model, upon personalization, may compliment and streamline ongoing imaging efforts by guiding FFR assessment. It is likely to assist in preliminary data generation for future projects. The computational geometries will contribute to an open source service that will be made available to our University’s researchers.

Name thesis:degree_name

M Sc

Discipline thesis:degree_discipline

Medical Biophysics

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2022

Author dc:creator

• Joseph, Jermiah

Advisors dc:contributor.advisor

• McIntyre, Christopher W.
• Goldman, Daniel.

• Keywords: Computational fluid dynamics
• coronary vasculature
• lumped parameter modelling
• data driven modelling.

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/32511

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/32511