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Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
KTH, School of Technology and Health, Huddinge, Sweden.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
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2017 (English)In: VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine, The Eurographics Association , 2017, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.

Place, publisher, year, edition, pages
The Eurographics Association , 2017. p. 1-5
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778, E-ISSN 2070-5786 ; 2017
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-140739DOI: 10.2312/vcbm.20171244ISBN: 9783038680369 (print)OAI: oai:DiVA.org:liu-140739DiVA, id: diva2:1139945
Conference
Eurographics Workshop on Visual Computing for Biology and Medicine, September 7-8, 2017, Bremen, Germany
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2019-11-19Bibliographically approved
In thesis
1. Advanced analysis of diffusion MRI data
Open this publication in new window or tab >>Advanced analysis of diffusion MRI data
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 93
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2017
Keywords
Diffusion MRI, Distortion Correction, Deep Learning, Uncertainty Estimation
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-161288 (URN)10.3384/diss.diva-161288 (DOI)9789175190037 (ISBN)
Public defence
2019-12-06, Hugo Theorell, Building 448, Campus US, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-11-19Bibliographically approved

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Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions(4285 kB)21 downloads
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Gu, XuanÖzarslan, Evren

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Shakya, SnehlataGu, XuanÖzarslan, EvrenKnutsson, Hans
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