Diffusion k-tensor estimation from Q-ball imaging using discretized principal axes

Ø. Bergmann, G. Kindlmann, A. Lundervold, C.-F. Westin
Nineth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'06)
Pages 268-275
October, 2006

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Abstract

A reoccurring theme in the diffusion tensor imaging literature is the per-voxel estimation of a symmetric 3×3 tensor describing the measured diffusion. In this work we attempt to generalize this approach by calculating 2 or 3 or up to k diffusion tensors for each voxel. We show that our procedure can more accurately describe the diffusion particularly when crossing fibers or fiber-bundles are present in the datasets.

Two tensor approximation in each voxel of a real DTI dataset. The upper right hand panel shows the position of the regions (a)-(c) visualized in the upper left, lower left and lower right panels, respectively.

Reference

Bergmann Ø, Kindlmann G, Lundervold A, Westin CF. Diffusion k-tensor estimation from q-ball imaging using discretized principal axes. In Nineth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'06), Lecture Notes in Computer Science 4191. Copenhagen, Denmark, 2006;268-275.

Bibtex entry

@InProceedings{bergmannMICCAI06,
  author         = {{\O}. Bergmann and G. Kindlmann and A. Lundervold and C.-F.
                   Westin},                                                    
  title          = {Diffusion k-tensor estimation from Q-ball imaging using    
                   discretized  principal axes},                               
  booktitle      = {Nineth International Conference on Medical Image Computing 
                   and  Computer-Assisted Intervention (MICCAI'06)},           
  pages          = {268--275},                                                 
  year           = {2006},                                                     
  series         = {Lecture Notes in Computer Science 4191},                   
  address        = {Copenhagen, Denmark},                                      
  month          = {October}
}

Grants

NIH T32-EB002177, NIH P41-RR13218 (NAC), NIH R01-AG20012, NIH P41-RR15241

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