Franzetti, Gaia;
(2020)
In vitro Tools for the Personalised Haemodynamic Assessment of Aortic Dissection.
Doctoral thesis (Ph.D), UCL (University College London).
Preview |
Text
PhD_Thesis_Franzetti_final.pdf - Accepted Version Download (18MB) | Preview |
Abstract
Diagnosis, management and treatment remain a challenge for Aortic Dissection (AD) because it is a highly patient specific pathology. Clinical practice still relies primarily on crude markers that cannot fully capture the complex haemodynamic features of AD and reliably predict dissection propagation or rupture. Whereas in vitro models would allow for the accurate study of AD flow fields in physical phantoms, they are currently scarce and almost exclusively rely on over simplifying assumptions. The aim of this thesis was to develop an experimental framework for patient-specific in vitro models of AD, in order to help clinical decision making, support the development of numerical methods and test medical devices. A novel mock circulatory loop was designed and developed. It comprises a computer controlled pulsatile pump system, tunable afterload models to reproduce the downstream vasculature and a patient-specific AD phantom. The platform was coupled with mathematical models informed by non-invasive clinical data and was tuned to reproduce a patient-specific case study. The complex haemodynamics reproduced by the platform was characterised by flow rate and pressure acquisitions as well as Particle Image Velocimetry (PIV) derived velocity fields. The pressure and flow rate data measured at the outlets of the AD phantom showed good agreement with both the available clinical data and in silico simulations. Aortic haemodynamics analysis revealed complex flow patterns,including flow reversal, recirculation regions and stagnant flow in the false lumen. Applying Proper Orthogonal Decomposition to PIV data demonstrated that reduced order modelling could be used for the development of computationally efficient in silico AD models. The contributions made in this thesis represent a significant advancement to the state of the art of AD in vitro modelling. There is currently no in vitro platform capable of reproducing patient-specific flows tuned by non-invasive clinical data. The framework that uses in vitro, in silico and in vivo data opens up new possibilities for the development of novel surgical procedures and devices to tackle AD. Ultimately, applying the methods developed in this thesis has the potential to improve the outcome of patients affected by AD.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | In vitro Tools for the Personalised Haemodynamic Assessment of Aortic Dissection |
| Event: | UCL |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10106265 |
Archive Staff Only
 |
View Item |
