Statistical Analysis of Diffusion Tensor Imaging - Zhou, Diwei
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This thesis tackles three major challenges in diffusion tensor imaging analysis with statistical methodologies. We firstly develop a novel Bayesian multi-tensor model with reparameterisation for capturing water diffusion at voxels with one or more distinct fibre orientations. A mixture Markov chain Monte Carlo (MCMC) algorithm is then developed to study the uncertainty of fibre orientations. Secondly, we apply non-Euclidean statistics to define the sample mean of diffusion tensor data which are employed for tensor field processing. In particular, Procrustes analysis, a powerful statistical…mehr

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Produktbeschreibung
This thesis tackles three major challenges in diffusion tensor imaging analysis with statistical methodologies. We firstly develop a novel Bayesian multi-tensor model with reparameterisation for capturing water diffusion at voxels with one or more distinct fibre orientations. A mixture Markov chain Monte Carlo (MCMC) algorithm is then developed to study the uncertainty of fibre orientations. Secondly, we apply non-Euclidean statistics to define the sample mean of diffusion tensor data which are employed for tensor field processing. In particular, Procrustes analysis, a powerful statistical shape analysis tool, is compared with the Log-Euclidean, Riemannian, Cholesky and power Euclidean approaches. A new anisotropy measure, Procrustes anisotropy, is defined. We finally use directional statistics to design uniformly distributed diffusion gradient direction schemes with different numbers of directions. All methods are illustrated through synthetic examples as well as white matter tractography of a healthy human brain.
Autorenporträt
Dr. Diwei Zhou received her PhD in Statistics from University of Nottingham (2010). She worked as a Marie Curie fellow for a medical image analysis project in School of Mathematical Sciences in University of Nottingham (2006 - 2009) . Now she is a lecturer in Statistics in University of Wolverhampton.