Anisotropy Creases Delineate White Matter Structure in Diffusion Tensor MRI

G. Kindlmann, Xavier Tricoche, C.-F. Westin
Ninth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'06)
Pages 126-133
October, 2006

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Abstract

Current methods for extracting models of white matter architecture from diffusion tensor MRI are generally based on fiber tractography. For some purposes a compelling alternative may be found in analyzing the first and second derivatives of diffusion anisotropy. Anisotropy creases are ridges and valleys of locally extremal anisotropy, where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. We describe a crease extraction algorithm that generates high-quality polygonal models of crease surfaces, then demonstrate the method on a measured diffusion tensor dataset, and visualize the result in combination with tractography to confirm its anatomic relevance.

anisotropy creases with tractography
Alternating layers of anisotropy ridge surfaces (RGB-color) and valley surfaces (white) define the structure of the human brainstem, as visualized in combination with fiber tractography.

Reference

Kindlmann G, Tricoche X, Westin CF. Anisotropy creases delineate white matter structure in diffusion tensor MRI. In Ninth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'06), Lecture Notes in Computer Science 4190. Copenhagen, Denmark, 2006;126-133.

Bibtex entry

@InProceedings{kindlmannMICCAI06,
  author         = "Gordon Kindlmann and Xavier Tricoche and Carl-Fredrik      
                   Westin",                                                    
  title          = "Anisotropy Creases Delineate White Matter Structure in     
                   Diffusion Tensor {MRI}",                                    
  booktitle      = "Ninth International Conference on Medical Image Computing  
                   and Computer-Assisted Intervention (MICCAI'06)",            
  month          = "October",                                                  
  year           = "2006",                                                     
  pages          = "126--133",                                                 
  series         = "Lecture Notes in Computer Science 4190",                   
  address        = "Copenhagen, Denmark"}

Grants

NIH T32-EB002177, NIH P41-RR13218 (NAC), NCRR P41-RR12553 (CIBC),

Research area

DTI

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