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Advanced analysis of diffusion MRI data

Gu, Xuan, 1988- (author)
Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten
Eklund, Anders, Docent, 1981- (thesis advisor)
Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV
Dyrby, Tim, Associate Professor (opponent)
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
 (creator_code:org_t)
ISBN 9789175190037
Linköping : Linköping University Electronic Press, 2019
English 93 s.
Series: Linköping Studies in Science and Technology. Dissertations, 0345-7524 ; 2017
  • Doctoral thesis (other academic/artistic)
Abstract Subject headings
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  • Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson.This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis.A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

Keyword

Diffusion MRI
Distortion Correction
Deep Learning
Uncertainty Estimation

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