Titre
Unifying the analyses of anatomical and diffusion tensor images using volume-preserved warping.
Type
article
Institution
Externe
Périodique
Auteur(s)
Xu, D.
Auteure/Auteur
Hao, X.
Auteure/Auteur
Bansal, R.
Auteure/Auteur
Plessen, K.J.
Auteure/Auteur
Geng, W.
Auteure/Auteur
Hugdahl, K.
Auteure/Auteur
Peterson, B.S.
Auteure/Auteur
Liens vers les personnes
ISSN
1053-1807
Statut éditorial
Publié
Date de publication
2007-03
Volume
25
Numéro
3
Première page
612
Dernière page/numéro d’article
624
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Controlled Clinical Trial ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Résumé
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.
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.
PID Serval
serval:BIB_CB2304052A24
PMID
Date de création
2019-02-21T08:51:46.273Z
Date de création dans IRIS
2025-05-21T00:25:44Z