Quantification of cardiac magnetic resonance imaging perfusion in the clinical setting at 3T
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Date
29/11/2016Author
Papanastasiou, Georgios
Metadata
Abstract
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.