Statistical Analysis of Diffusion Tensor Imaging: Statistical Methodologies for Medical Image Analysis - Tapa blanda

Zhou, Diwei

 
9783847307877: Statistical Analysis of Diffusion Tensor Imaging: Statistical Methodologies for Medical Image Analysis
Tapa blanda
ISBN 10: 3847307878 ISBN 13: 9783847307877
Editorial: LAP LAMBERT Academic Publishing, 2011

Sinopsis

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.

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Reseña del editor

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.

Biografía del autor

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.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2011
Idioma
Inglés
ISBN 10 
3847307878
ISBN 13 
9783847307877
Encuadernación
Tapa blanda
Número de páginas
200
Contacto del fabricante
no disponible
Persona responsable
no disponible