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Asymmetric Orientation Distribution Functions (AODFs) revealing intravoxel geometry in diffusion MRI
Brigham and Womens Hosp, MA 02115 USA; Harvard Med Sch, MA USA; Sabanci Univ, Turkey.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Istanbul Tech Univ, Turkey.
2018 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 49, p. 145-158Article in journal (Refereed) Published
Abstract [en]

Characterization of anisotropy via diffusion MRI reveals fiber crossings in a substantial portion of voxels within the white-matter (WM) regions of the human brain. A considerable number of such voxels could exhibit asymmetric features such as bends and junctions. However, widely employed reconstruction methods yield symmetric Orientation Distribution Functions (ODFs) even when the underlying geometry is asymmetric. In this paper, we employ inter-voxel directional filtering approaches through a cone model to reveal more information regarding the cytoarchitectural organization within the voxel. The cone model facilitates a sharpening of the ODFs in some directions while suppressing peaks in other directions, thus yielding an Asymmetric ODF (AODF) field. We also show that a scalar measure of AODF asymmetry can be employed to obtain new contrast within the human brain. The feasibility of the technique is demonstrated on in vivo data obtained from the MGH-USC Human Connectome Project (HCP) and Parkinsons Progression Markers Initiative (PPMI) Project database. Characterizing asymmetry in neural tissue cytoarchitecture could be important for localizing and quantitatively assessing specific neuronal pathways.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2018. Vol. 49, p. 145-158
Keywords [en]
ODF regularization; Asymmetric fiber orientations; Fiber asymmetry measure; Asymmetric ODF (AODF); Directional spatial filtering; Steerable filtering; Diffusion MRI; HARDI
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-148645DOI: 10.1016/j.mri.2018.03.006ISI: 000433526000020PubMedID: 29550369OAI: oai:DiVA.org:liu-148645DiVA, id: diva2:1219999
Note

Funding Agencies|Linkoping University Center for Industrial Information Technology (CENIIT)

Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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