Fiber Tracking: Description

Tractography, or fiber tracking, is a process which runs at the end of the DTI processing pipeline, in the DTI Track module of MedINRIA [2]. Among the numerous available methods for tracking fibers, we chose a relatively fast and easy to implement one [6] and show how the tracking can be improved by our variational estimation combined with regularization. Criteria for stopping the tracking are: a threshold on FA (if FA is too low, the tracking is stopped) and on the curvature (to forbid unlikely fibers having a high curvature). Prior to the tracking, tensor fields are resampled to obtain isotropic voxels: in general, the out-plane resolution is very low (e.g., the brain dataset here) and interpolating the tensors improves the regularity of the fibers. Resampling is interpreted as a weighted mean with trilinear coefficients. Such a mean is computed in the logarithmic domain and then mapped back to the tensor space with the matrix exponential:

where wi are classical trilinear weights. We showed in [7] that such an interpolation has good practical properties in the context of DT-MRI, compared to a Euclidean interpolation.


On this figure is shown an example of a set of fiber tracts reconstructed in MedINRIA. Fibers are represented as colorized lines in the 3D view. The color characterize the direction of the fiber (see the color sphere).


  1. D. Ducreux, P. Fillard, D. Facon, A. Ozanne, J.-F. Lepeintre, J. Renoux, M. Tadié, and P. Lasjaunias. Diffusion Tensor Magnetic Resonance Imaging and Fiber Tracking in Spinal Cord Lesions: Current and Future Indications. Neuroimaging Clinics of North America, 17(1):137-147, February 2007. [bibtex-entry]

  2. Pierre Fillard, Vincent Arsigny, Xavier Pennec, and Nicholas Ayache. Clinical DT-MRI Estimation, Smoothing and Fiber Tracking with Log-Euclidean Metrics. IEEE Transactions on Medical Imaging, 26(11):1472-1482, November 2007. Note: PMID: 18041263. [bibtex-entry]

  3. Denis Ducreux, Jean-Francois Lepeintre, Pierre Fillard, C. Loureiro, Marc TadiÄ(c), and Pierre Lasjaunias. MR diffusion tensor imaging and fiber tracking in 5 spinal cord astrocytomas. AJNR Am J Neuroradiol, 27(1):214-6, January 2006. Keyword(s): Adult, Astrocytoma, diagnosis, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Processing Computer-Assisted, Imaging Three-Dimensional, Male, Nerve Fibers, pathology, Spinal Cord, pathology, Spinal Cord Neoplasms, diagnosis. [Abstract] [bibtex-entry]

  4. J Renoux, D Facon, P Fillard, I Huynh, P Lasjaunias, and D Ducreux. MR diffusion tensor imaging and fiber tracking in inflammatory diseases of the spinal cord. AJNR Am J Neuroradiol, 27(9):1947-51, October 2006. Keyword(s): Adult, Anisotropy, Diagnosis Differential, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Enhancement, Image Processing Computer-Assisted, Male, Middle Aged, Multiple Sclerosis, diagnosis, Myelitis, diagnosis, Myelitis Transverse, diagnosis, Nerve Fibers, pathology, Neurologic Examination, Prospective Studies, Reference Values, Sarcoidosis, diagnosis, Spinal Cord, pathology. [Abstract] [bibtex-entry]

  5. D. Facon, A. Ozanne, P. Fillard, J.-F. Lepeintre, C. Tournoux-Facon, and D. Ducreux. MR Diffusion Tensor Imaging and Fiber Tracking in Spinal Cord Compression. American Journal of Neuroradiology (AJNR), 26:1587-1594, 2005. [bibtex-entry]

  6. P. Fillard and G. Gerig. Analysis Tool For Diffusion Tensor MR. In Proc. of MICCAI'03, Part II, volume 2879 of LNCS, pages 979-980, November 2003. Springer. [bibtex-entry]

  7. Vincent Arsigny, Pierre Fillard, Xavier Pennec, and Nicholas Ayache. Log-Euclidean Metrics for Fast and Simple Calculus on Diffusion Tensors. Magnetic Resonance in Medicine, 56(2):411-421, August 2006. Note: PMID: 16788917. Keyword(s): DT-MRI, Tensors, Riemannian geometry, Lie groups, interpolation, Log-Euclidean metrics. [bibtex-entry]

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