- Email: [email protected]
Estimation of anatomical connectivity from diffusion tensor data
NeuroImage
13, Number
6, 2001,
Part 2 of 2 Parts ID
E bl@
METHODS
- ANALYSIS
Estimation of anatomical connectivity from diffusion tensor data M. Koch*, V. Glauche*, J. Finsterbuscht, * Universitiitsklinikum Hamburg-Eppendo$
U. Noltet, J. Frahmt,
Neurologie, Martinistr.
C. Biichel*
52, 20246 Hamburg, Germany
tBiomedizinische NMR Forschungs GmbH, 37070 Giittingen, Germany Studies of functional connectivity of grey matter areas in the human brain are expected to benefit significantly from the incorporation of information on the anatomical connections between those areas. Such information can be obtained in vivo by means of diffusion tensor imaging (DTI [l]). Since DTI only provides the mean direction of fibres in white matter (WM), this requires a method to extract the relevant information from DTI data. Previously published fibre tracking algorithms [2] have been designed for this purpose but are likely to fail in voxels with measurement errors or with low anisotropy at fibre crossings. A new algorithm is presented which is expected to overcome this problem.
Methods DTI was performed on the brain of a healthy volunteer, using diffusion-weighted single-shot STEAM imaging [3] on a 1.5 T MR scanner (Siemens). Two consecutively acquired slabs of 20 slices were combined to a full-brain representation with 2X2X3 mm resolution and interpolated to 2X2X2 mm. The calculated diffusion tensor eigenvector corresponding to the largest eigenvalue represents the estimated fibre direction. A Monte-Carlo type algorithm was applied to the tensor data in WM (determined by segmentation of 1 X 1 X 1 mm Tl-weighted 3d data): The random walk of a virtual particle through the set of image voxels was confined preferentially to WM fibre directions by rendering the probabilities for the jump directions dependent on the diffusion tensors. The probability was calculated from the diffusion coefficient (in the current voxel and its neighbours) along the jump
“,lCL‘
\,,,
P
traversed by the particle path. The voxels with the largest frequency correctly reveal the course of the pyramidal tract, through the posterior internal capsule. Calculation time was below 1 min on a standard PC. The method presented is capable of dealing with WM voxels with isotropic diffusion. It reflects the statistical nature of DTl data and, in contrast to eigenvector-based fibre tracking, it uses the full tensor information. The approach aims at determining anatomical connectivity values rather than reconstructing the course of fibre tracts and provides an estimate of the probability that two cortical areas are connected with each other. To improve the performance at fibre crossings, the algorithm can easily be extended to include data from 3-dimensional q-space imaging.
References [l] P. J. Basser et al. Biophys. J. 66, 259 (1994). [2] T. E. Conturo et al. Proc. Natl. Acad. Sci. USA 96, 10422 (1999). [3] U. Nolte et al. In: Proceedings, ISMRM, 8th Annual Meeting, Denver,
S176
p. 807, 2000.
13, Number
6, 2001,
Part 2 of 2 Parts ID
E bl@
METHODS
- ANALYSIS
Estimation of anatomical connectivity from diffusion tensor data M. Koch*, V. Glauche*, J. Finsterbuscht, * Universitiitsklinikum Hamburg-Eppendo$
U. Noltet, J. Frahmt,
Neurologie, Martinistr.
C. Biichel*
52, 20246 Hamburg, Germany
tBiomedizinische NMR Forschungs GmbH, 37070 Giittingen, Germany Studies of functional connectivity of grey matter areas in the human brain are expected to benefit significantly from the incorporation of information on the anatomical connections between those areas. Such information can be obtained in vivo by means of diffusion tensor imaging (DTI [l]). Since DTI only provides the mean direction of fibres in white matter (WM), this requires a method to extract the relevant information from DTI data. Previously published fibre tracking algorithms [2] have been designed for this purpose but are likely to fail in voxels with measurement errors or with low anisotropy at fibre crossings. A new algorithm is presented which is expected to overcome this problem.
Methods DTI was performed on the brain of a healthy volunteer, using diffusion-weighted single-shot STEAM imaging [3] on a 1.5 T MR scanner (Siemens). Two consecutively acquired slabs of 20 slices were combined to a full-brain representation with 2X2X3 mm resolution and interpolated to 2X2X2 mm. The calculated diffusion tensor eigenvector corresponding to the largest eigenvalue represents the estimated fibre direction. A Monte-Carlo type algorithm was applied to the tensor data in WM (determined by segmentation of 1 X 1 X 1 mm Tl-weighted 3d data): The random walk of a virtual particle through the set of image voxels was confined preferentially to WM fibre directions by rendering the probabilities for the jump directions dependent on the diffusion tensors. The probability was calculated from the diffusion coefficient (in the current voxel and its neighbours) along the jump
“,lCL‘
\,,,
P
traversed by the particle path. The voxels with the largest frequency correctly reveal the course of the pyramidal tract, through the posterior internal capsule. Calculation time was below 1 min on a standard PC. The method presented is capable of dealing with WM voxels with isotropic diffusion. It reflects the statistical nature of DTl data and, in contrast to eigenvector-based fibre tracking, it uses the full tensor information. The approach aims at determining anatomical connectivity values rather than reconstructing the course of fibre tracts and provides an estimate of the probability that two cortical areas are connected with each other. To improve the performance at fibre crossings, the algorithm can easily be extended to include data from 3-dimensional q-space imaging.
References [l] P. J. Basser et al. Biophys. J. 66, 259 (1994). [2] T. E. Conturo et al. Proc. Natl. Acad. Sci. USA 96, 10422 (1999). [3] U. Nolte et al. In: Proceedings, ISMRM, 8th Annual Meeting, Denver,
S176
p. 807, 2000.