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Title: Towards reliable diffusion MRI of the in vivo human heart
Author: Von Deuster, Constantin Karl Viktor
ISNI:       0000 0004 5994 5914
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2016
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In vivo cardiac diffusion tensor imaging (DTI) is a non-invasive method to map the complex, three-dimensional fibre architecture of the beating heart. It allows the assessment and characterisation of the myocardium and has been employed successfully to image myocardial tissue alterations in a number of relevant diseases. Cardiac diffusion imaging has been primarily performed using stimulated echo based pulse sequences. With recent developments in magnetic resonance hardware and pulse sequence design, spin echo based approaches have become attractive alternatives. The following work presents a comprehensive comparison of stimulated echo and spin echo based cardiac diffusion imaging approaches. Signal-to-noise ratio (SNR) and diffusion metrics in phantoms and in the in vivo human heart are analysed and a modification to previous diffusion encoding schemes is proposed. In vivo cardiac DTI is implemented and applied to study dynamic fibre reorientation between heart phases in a patient population with dilated cardiomyopathy. Diffusion tensor metrics are compared relative to a healthy control group and correlated to cardiac motion parameters. To address long acquisition times, dual-slice excitation and dedicated image reconstruction are proposed and implemented in a separate study of healthy volunteers. The impact of microvascular perfusion on the diffusion-weighted signal is investigated in a porcine model of myocardial infarction. The intravoxel incoherent motion (IVIM) model is employed to obtain perfusion metrics which are correlated to dynamic contrast enhanced perfusion measurements. A validation of the IVIM model is performed by comparing in vivo IVIM parameters relative to post mortem reference measurements without motion and perfusion effects. Additionally, Bayesian inference is proposed to reduce variability of diffusion and perfusion parameter estimation.
Supervisor: Kozerke, Sebastian ; Prieto Vasquez, Claudia Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available