Neural Tractography Using an Unscented Kalman Filter
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Two-Tensor Tractography Using a Constrained Filter
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
On approximation of orientation distributions by means of spherical ridgelets
IEEE Transactions on Image Processing
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Symmetric positive-definite cartesian tensor orientation distribution functions (CT-ODF)
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Detection of crossing white matter fibers with high-order tensors and rank-k decompositions
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Approximating Symmetric Positive Semidefinite Tensors of Even Order
SIAM Journal on Imaging Sciences
Visualizing white matter fiber tracts with optimally fitted curved dissection surfaces
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
A maximum enhancing higher-order tensor glyph
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Topological features in 2D symmetric higher-order tensor fields
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Most Tensor Problems Are NP-Hard
Journal of the ACM (JACM)
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Diffusion weighted magnetic resonance imaging is a unique tool for non-invasive investigation of major nerve fiber tracts. Since the popular diffusion tensor (DT-MRI) model is limited to voxels with a single fiber direction, a number of high angular resolution techniques have been proposed to provide information about more diverse fiber distributions. Two such approaches are Q-Ball imaging and spherical deconvolution, which produce orientation distribution functions (ODFs) on the sphere. For analysis and visualization, the maxima of these functions have been used as principal directions, even though the results are known to be biased in case of crossing fiber tracts. In this paper, we present a more reliable technique for extracting discrete orientations from continuous ODFs, which is based on decomposing their higher-order tensor representation into an isotropic component, several rank-1 terms, and a small residual. Comparing to ground truth in synthetic data shows that the novel method reduces bias and reliably reconstructs crossing fibers which are not resolved as individual maxima in the ODF. We present results on both Q-Ball and spherical deconvolution data and demonstrate that the estimated directions allow for plausible fiber tracking in a real data set.