Curvilinear Analysis and Approximation of Cardiac DTI In-Vivo

Student thesis: Doctoral ThesisDoctor of Philosophy


Diffusion Tensor MRI can be used to depict the anisotropy of tissue. Translation of this technique to moving objects such as the beating heart has recently become feasible, but remains a challenging task, often leading to high noise levels and limited accuracy. Ultimately, knowledge of the 3D fibre architecture of the myocardium in­ vivo should allow for a better understanding of the cardiac function both in healthy and pathological situations.
The main goal of the work presented in this thesis is to overcome the difficul­
ties that such technology presents, by introducing a combination of image process­ ing and analysis approaches. In particular, the characteristic ellipsoidal shape of the left ventricular chamber is used to introduce a shape-based prolate spheroidal coordinate frame that allows for comprehensive, robust and dedicated analysis of diffusion tensor data within the myocardial wall. It is shown that the description of this information is more compact in this coordinate frame. Furthermore, it is demonstrated that the acquisition limitations can be overcome by introducing an approximation scheme based on this coordinate frame. These techniques are tested on ex-vivo datasets to assess their fidelity and sensitivity. Finally, these techniques are applied in-vivo on a group of healthy volunteers, where 2D DTI slices of the LV were acquired at end diastole and end systole, using cardiac dedicated diffusion MR acquisition. Results demonstrate the advantages of using curvilinear coordinates both for the analysis and the approximation of cardiac DTI information. Resulting in-vivo fibre architectures were found to agree with previously reported studies on ex-vivo specimens. The outcome of this work can open the door for clinical appli­ cations and cardiac electrophysiology modelling, and improve the understanding of the left ventricular structure and dynamics.
Date of Award2013
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorTobias Schaeffter (Supervisor), Sebastian Kozerke (Supervisor) & Philip Batchelor (Supervisor)

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