title: Unifying the analyses of anatomical and diffusion tensor images using volume-preserved warping.
abstract: OBJECTIVE
  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).
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: MRI Unit, College of Physicians and Surgeons, Columbia University Medical Center, Columbia University, New York, New York, USA. dx2103@columbia.edu
authors.forename: Dongrong
authors.initials: D
authors.lastname: Xu
authors.forename: Xuejun
authors.initials: X
authors.lastname: Hao
authors.forename: Ravi
authors.initials: R
authors.lastname: Bansal
authors.forename: Kerstin J
authors.initials: KJ
authors.lastname: Plessen
authors.forename: Weidong
authors.initials: W
authors.lastname: Geng
authors.forename: Kenneth
authors.initials: K
authors.lastname: Hugdahl
authors.forename: Bradley S
authors.initials: BS
authors.lastname: Peterson
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D002648:Child; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D009412:Nerve Fibers; D009434:Neural Pathways / Q000033:anatomy & histology / Q000473:pathology; D012016:Reference Values; D015203:Reproducibility of Results; D005879:Tourette Syndrome / Q000175:diagnosis*