Quantitative MRI of the carotid artery

Quantitative MRI of the carotid artery: Simultaneous T1 and T2 mapping on complex image data using maximum likelihood parameter estimation

van Hespen, K.M.

Poot, D.H.J. (mentor)
Klein, S. (mentor)
Nieuwstadt, H.A. (mentor)

Mechanical, Maritime and Materials Engineering

Biomechanical Engineering

2016-09-28

Carotid atherosclerotic plaque rupture is a major cause in ischemic strokes. The luminal narrowing caused by the growing of the plaque has been commonly used for identification of plaque rupture risk. However, there have been signs that plaque composition is also important in the identification of plaque rupture risk. Quantitative Magnetic Resonance Imaging is able to characterize and quantify plaque composition by estimating magnetic resonance tissue properties, T1 and T2. In this thesis we evaluate the reproducibility of T1 and T2 estimation on data acquired from nine healthy volunteers. For reproducibility purposes, these volunteers were scanned twice with the same protocol. Several challenges are imposed on the fitting of T1 and T2 on the acquired images. Firstly, the number of acquired images is low, which may result in inaccuracy of the estimated parameters. Hereto, the fitting was performed on the complex MR signal, as opposed to the magnitude MR signal which is more commonly used. The second challenge is due to a translation between the images due to patient movement, as well as translation between the images acquired in both scan sessions. In this thesis we propose three image registration methods that enable us to decrease the translation between images. Our results show that the reproducibility of T1 and T2 with our proposed methods is sufficient to potentially allow the characterization of several plaque components. For further analysis of our method on the estimation of T1 and T2, patients need to be scanned.

quantitative MRI
image processing
carotid artery
atherosclerotic plaque
maximum likelihood estimation
image registration
T1 and T2 estimation

http://resolver.tudelft.nl/uuid:b14307ac-fa9d-464d-adbd-9176790eb04a

Student theses

master thesis

(c) 2016 van Hespen, K.M.