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Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

Ravikumar, Nishant, Gooya, Ali, Beltrachini, Leandro, Frangi, Alejandro F. and Taylor, Zeike A. T 2019. Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data. Medical Image Analysis 53 , pp. 47-63. 10.1016/j.media.2019.01.001
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Abstract

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM).

Item Type: Article
Date Type: Publication
Status: Published
Schools: Cardiff University Brain Research Imaging Centre (CUBRIC)
Physics and Astronomy
Psychology
Publisher: Elsevier
ISSN: 1361-8415
Date of First Compliant Deposit: 11 January 2019
Date of Acceptance: 4 January 2019
Last Modified: 27 Jun 2019 22:26
URI: http://orca.cf.ac.uk/id/eprint/118318

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