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Updated: Jun 25, 2026
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Unifying the analyses of anatomical and diffusion tensor images using volume-preserved warping
Dongrong Xu1, Xuejun Hao, Ravi Bansal
1MRI Unit, College of Physicians and Surgeons, Columbia University Medical Center, Columbia University, New York, New York, USA. dx2103@columbia.edu
Purpose:
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).
Materials And 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.
Conclusion:
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.
