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One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR) / KEVIN MOHEE

Swansea University Author: KEVIN MOHEE

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Abstract

Fractional flow reserve (FFR) improves the assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it requires additional interventional techniques and equipment to perform. Multiple proposed virtual functional indices are derived from corona...

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Published: Swansea 2022
Institution: Swansea University
Degree level: Master of Research
Degree name: MSc by Research
Supervisor: Halcox, Julian P. and Nithiarasu, Perumal
URI: https://cronfa.swan.ac.uk/Record/cronfa60376
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first_indexed 2022-07-04T11:54:16Z
last_indexed 2023-01-13T19:20:27Z
id cronfa60376
recordtype RisThesis
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spelling 2022-07-04T13:01:23.0142939 v2 60376 2022-07-04 One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR) 36b7e1b55cb1c56de48b3336f9eb71a8 KEVIN MOHEE KEVIN MOHEE true false 2022-07-04 Fractional flow reserve (FFR) improves the assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it requires additional interventional techniques and equipment to perform. Multiple proposed virtual functional indices are derived from coronary imaging alone but require complex computational fluid dynamics modelling which is time-consuming and hence cannot influence immediate clinical management. The Zienkiewicz Centre for Computational Engineering at Swansea University has developed a reduced order one-dimensional model that generates a “virtual” value of FFR in considerably less time but its accuracy is unknown and has not been validated against invasive FFR. This MSc by research project aims to refine this 1D model with patient specific data and use it to predict the severity of blood flow reduction caused by individual plaques. Clinical data obtained from coronary angiography will be used and patient-specific data will be generated and then validated by already measured invasive derived FFR data. The study will cover blood flow modelling in different arterial network and assess fractional flow reserve (a physiological index determined from the ratio of the pressure distal to a stenosis relative to that before the stenosis). E-Thesis Swansea 10 6 2022 2022-06-10 COLLEGE NANME COLLEGE CODE Swansea University Halcox, Julian P. and Nithiarasu, Perumal Master of Research MSc by Research 2022-07-04T13:01:23.0142939 2022-07-04T12:51:10.0319231 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine KEVIN MOHEE 1 60376__24440__4762defee99d4cfa8b143cc4c8e2c579.pdf Mohee_Kevin_R_MSc_Research_Thesis_Final_Cronfa.pdf 2022-07-04T12:58:14.4835751 Output 1510783 application/pdf E-Thesis – open access true Copyright: The author, Kevin R. Mohee, 2022. true eng
title One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
spellingShingle One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
KEVIN MOHEE
title_short One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
title_full One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
title_fullStr One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
title_full_unstemmed One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
title_sort One-dimensional computational flow modelling using patient specific geometry to assess fractional flow reserve (FFR)
author_id_str_mv 36b7e1b55cb1c56de48b3336f9eb71a8
author_id_fullname_str_mv 36b7e1b55cb1c56de48b3336f9eb71a8_***_KEVIN MOHEE
author KEVIN MOHEE
author2 KEVIN MOHEE
format E-Thesis
publishDate 2022
institution Swansea University
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description Fractional flow reserve (FFR) improves the assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it requires additional interventional techniques and equipment to perform. Multiple proposed virtual functional indices are derived from coronary imaging alone but require complex computational fluid dynamics modelling which is time-consuming and hence cannot influence immediate clinical management. The Zienkiewicz Centre for Computational Engineering at Swansea University has developed a reduced order one-dimensional model that generates a “virtual” value of FFR in considerably less time but its accuracy is unknown and has not been validated against invasive FFR. This MSc by research project aims to refine this 1D model with patient specific data and use it to predict the severity of blood flow reduction caused by individual plaques. Clinical data obtained from coronary angiography will be used and patient-specific data will be generated and then validated by already measured invasive derived FFR data. The study will cover blood flow modelling in different arterial network and assess fractional flow reserve (a physiological index determined from the ratio of the pressure distal to a stenosis relative to that before the stenosis).
published_date 2022-06-10T04:18:26Z
_version_ 1763754226572328960
score 11.012678

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