AbstractClinical evidence shows that central (aortic) blood pressure (cBP) is a better marker of cardiovascular risk than brachial pressure. However, cBP can only be accurately measured invasively, through catheterisation. Although medical imaging techniques such as magnetic resonance imaging and ultrasound provide good resolution aortic blood flow and geometry, they do not currently provide cBP.
This thesis presents a novel approach to measure cBP non-invasively from medical imaging data and a non-invasive peripheral pressure measurement, using zero and one-dimensional computational models of aortic haemodynamics. The studies reported used both in silico (simulated) and in vivo (clinical) data for algorithm development, testing,and validation. Firstly, three in silico datasets were created using computational models whose inputs were cardiovascular properties from the clinical literature for healthy humans. Secondly, existing and new methods to measure cardiovascular properties from clinical data were investigated. These properties have clinical value for assessing cardio-vascular function and are also used as inputs to computational models to measure cBP non-invasively. Thirdly, the performance of three computational models of aortic haemodynamics for non-invasive cBP measurement was assessed. Three in vivo datasets were used to determine the preferred cBP algorithm: one containing invasive cBP measurements for aortic coarctation patients; and two containing non-invasive cBP measurements for hypertensive patients and normotensive volunteers, respectively. Finally, a ‘cohort-specific’ in silico dataset was created to study the individual effect of cardiovascular properties on the aortic pressure gradients – measured using magnetic resonance imaging – in dilated cardiomyopathy patients. Hence, the main contributions of this thesis were the development and testing of a range of tools to measure cardiovascular properties and cBP from non-invasive clinical measurements; and the study of the individual cardiovascular determinants of the aortic pressure gradients.
In the clinic, the preferred cBP algorithm could be used to augment, at no additional cost, the clinical data obtained from ultrasound and magnetic resonance imaging by providing an accurate, patient-specific, non-invasive measurement of cardiovascular parameters and cBP waves. Furthermore, the assessment of haemodynamic metrics (e.g.aortic pressure gradients, blood pressure or flow waves) using ‘cohort-specific’ in silico datasets could be applied to study other patient groups and to address alternative research questions.
|Date of Award||1 Jul 2020|
|Supervisor||Jordi Alastruey (Supervisor), Spencer J. Sherwin (Supervisor) & Peter Charlton (Supervisor)|