Unifying the analyses of anatomical and diffusion tensor images using volume-preserved warping.

Details

Serval ID
serval:BIB_CB2304052A24
Type
Article: article from journal or magazin.
Collection
Publications
Title
Unifying the analyses of anatomical and diffusion tensor images using volume-preserved warping.
Journal
Journal of magnetic resonance imaging
Author(s)
Xu D., Hao X., Bansal R., Plessen K.J., Geng W., Hugdahl K., Peterson B.S.
ISSN
1053-1807 (Print)
ISSN-L
1053-1807
Publication state
Published
Issued date
03/2007
Peer-reviewed
Oui
Volume
25
Number
3
Pages
612-624
Language
english
Notes
Publication types: Controlled Clinical Trial ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
To introduce a framework that automatically identifies regions of anatomical abnormality within anatomical MR images and uses those regions in hypothesis-driven selection of seed points for fiber tracking with diffusion tensor (DT) imaging (DTI).
Regions of interest (ROIs) are first extracted from MR images using an automated algorithm for volume-preserved warping (VPW) that identifies localized volumetric differences across groups. ROIs then serve as seed points for fiber tracking in coregistered DT images. Another algorithm automatically clusters and compares morphologies of detected fiber bundles. We tested our framework using datasets from a group of patients with Tourette's syndrome (TS) and normal controls.
Our framework automatically identified regions of localized volumetric differences across groups and then used those regions as seed points for fiber tracking. In our applied example, a comparison of fiber tracts in the two diagnostic groups showed that most fiber tracts failed to correspond across groups, suggesting that anatomical connectivity was severely disrupted in fiber bundles leading from regions of known anatomical abnormality.
Our framework automatically detects volumetric abnormalities in anatomical MRIs to aid in generating a priori hypotheses concerning anatomical connectivity that then can be tested using DTI. Additionally, automation enhances the reliability of ROIs, fiber tracking, and fiber clustering.
Keywords
Adolescent, Algorithms, Brain/anatomy & histology, Brain/pathology, Brain Mapping/methods, Child, Diffusion Magnetic Resonance Imaging/methods, Humans, Image Processing, Computer-Assisted/methods, Imaging, Three-Dimensional/methods, Nerve Fibers, Neural Pathways/anatomy & histology, Neural Pathways/pathology, Reference Values, Reproducibility of Results, Tourette Syndrome/diagnosis
Pubmed
Web of science
Create date
21/02/2019 10:51
Last modification date
20/08/2019 16:45
Usage data