liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Repeated Tractography of a Single Subject: How High Is the Variance?
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, Department of Computer and Information Science, The Division of Statistics and Machine Learning. 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).ORCID iD: 0000-0002-9091-4724
2017 (English)In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer, 2017, p. 331-354Chapter in book (Other academic)
Abstract [en]

We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.

Place, publisher, year, edition, pages
Springer, 2017. p. 331-354
Series
Mathematics and Visualization (MATHVISUAL), ISSN 1612-3786, E-ISSN 2197-666X
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-142047DOI: 10.1007/978-3-319-61358-1_14ISBN: 978-3-319-61357-4 (print)ISBN: 978-3-319-61358-1 (electronic)OAI: oai:DiVA.org:liu-142047DiVA, id: diva2:1150570
Available from: 2017-10-19 Created: 2017-10-19 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

Open Access in DiVA

fulltext(4114 kB)80 downloads
File information
File name FULLTEXT01.pdfFile size 4114 kBChecksum SHA-512
b15559327c60765937e6cb761cabb167bbd9068e1c21aa0ea4c746ef9468fb0457c24f4d0f34dead57a65b1031e8d564a9be61cfe8982854a70198c1db389b0c
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Gu, XuanEklund, Anders

Search in DiVA

By author/editor
Gu, XuanEklund, AndersKnutsson, Hans
By organisation
Division of Biomedical EngineeringFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)The Division of Statistics and Machine Learning
Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 80 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 221 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf