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Title: Quantification of cardiac magnetic resonance imaging perfusion in the clinical setting at 3T
Author: Papanastasiou, Georgios
ISNI:       0000 0004 6351 9264
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2016
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Dynamic contrast enhanced (DCE) cardiac magnetic resonance imaging (MRI) is well-established as a non-invasive method for qualitatively detecting obstructive coronary artery disease (CAD) which can impair myocardial blood flow and may result in myocardial infarction. Mathematical modelling of cardiac DCE-MRI data can provide quantitative assessment of myocardial blood flow. Quantitative assessment of myocardial blood flow may have merit in further stratification of patients with obstructive CAD and to improve the diagnosis and prognostication of the disease in the clinical setting. This thesis investigates the development of a quantitative analysis protocol for cardiac DCE-MRI data. In the first study presented in this thesis, Fermi and distributed parameter (DP) modelling are compared in single bolus versus dual bolus analysis. For model-based myocardial blood flow quantification, the convolution of a model with the arterial input function (i.e. contrast agent concentration-time curve extracted from the left ventricular cavity) is fitted to the tissue contrast agent concentration-time curve. In contrast to dual bolus DCE-MRI protocols, single bolus protocols reduce patient discomfort and acquisition protocol duration/complexity but, are prone to arterial input function saturation caused in the left ventricular cavity by the high concentration of contrast agent during bolus passage. Saturation effects can degrade the accuracy of quantification using Fermi modelling. The analysis presented in this study showed that DP modelling is less dependent on arterial input function saturation than Fermi modelling in eight healthy volunteers. In a pilot cohort of five patients, DP modelling detected for the first time reduced myocardial blood flow in all stenotic vessels versus standard clinical assessments. In the second study, it was investigated whether first-pass DP modelling can give accurate myocardial blood flow, against ideal values generated by numerical simulations. Unlike Fermi modelling which is convolved with only the first-pass range of the arterial input function, DP modelling is convolved with the entire contrast agent concentration-time course. In noisy and/or dual bolus data, it can be particularly challenging to identify the end point of the first-pass in the arterial input function. This study demonstrated that contrary to Fermi modelling, myocardial blood flow analysis using DP modelling does not depend on the number of time points used for fitting. Furthermore, this data suggests that DP modelling can reduce the quantitative variability caused by subjectivity in selection of the first-pass range in cardiac MR data. This in turn may help to facilitate the development of more automated software algorithms for myocardial blood flow quantification. In the third study, Fermi and DP modelling were compared against invasive clinical assessments and visual MR estimates, to assess their diagnostic ability in detecting obstructive CAD. A single bolus DCE-MRI protocol was implemented in twentyfour patients. In per vessel analysis, DP modelling reached superior sensitivity and negative predictive value in detecting obstructive CAD compared to Fermi modelling and visual estimates. In per patient analysis, DP modelling reached the highest sensitivity and negative predictive value in detecting obstructive CAD. These studies show that DP modelling analysis of cardiac single bolus DCE-MRI data can provide important functional information and can establish haemodynamic biomarkers to non-invasively improve the diagnosis and prognostication of obstructive CAD.
Supervisor: Semple, Scott ; Newby, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Functional Magnetic Resonance Imaging ; MRI ; cardiac ; mathematical modelling ; coronary artery disease ; CAD