Diffusion Tensor Field Registration in the Presence of Uncertainty

@article{Irfanoglu2009DiffusionTF,
  title={Diffusion Tensor Field Registration in the Presence of Uncertainty},
  author={M. Okan Irfanoglu and Cheng Guan Koay and Sinisa Pajevic and Raghu Machiraju and Peter J. Basser},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2009},
  volume={12 Pt 1},
  pages={
          181-9
        }
}
  • M. Irfanoglu, C. Koay, P. Basser
  • Published 2 October 2009
  • Mathematics
  • Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
We propose a novel method for deformable tensor-to-tensor registration of Diffusion Tensor Imaging (DTI) data. Our registration method considers estimated diffusion tensors as normally distributed random variables whose covariance matrices describe uncertainties in the mean estimated tensor due to factors such as noise in diffusion weighted images (DWIs), tissue diffusion properties, and experimental design. The dissimilarity between distributions of tensors in two different voxels is computed… 
Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images
In this paper, we propose three metrics to quantify the differences between the results of diffusion tensor magnetic resonance imaging (DT-MRI) fiber tracking algorithms: the area between
Uncertainty visualization in HARDI based on ensembles of ODFs
TLDR
A new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI), which elucidates the complex heteroscedastic structural variation in diffusion shapes.
Fully Automated Medical Image Analysis Facilitating Subsequent User Analysis
TLDR
Novel techniques for automatically processing medical images are presented, with the goal of facilitating later analysis by a human expert, and each of the techniques presented focus on encoding meaningful uncertainty information, which can guide human experts to potential errors or pathologies.
NOVEL PHANTOMS AND POST- PROCESSING FOR DIFFUSION SPECTRUM IMAGING

References

SHOWING 1-10 OF 19 REFERENCES
Error Propagation Framework for Diffusion Tensor Imaging via Diffusion Tensor Representations
TLDR
An analytical framework of error propagation for diffusion tensor imaging (DTI) is presented and elucidates the underlying geometric relationship between the variability of a tensor-derived quantity andThe variability of the diffusion weighted signals through the nonlinear least squares objective function of DTI.
Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors
TLDR
This work introduces principal geodesic analysis, a generalization of principal component analysis, to symmetric spaces and applies it to the computation of the variability of diffusion tensor data, and develops methods for producing statistics, namely averages and modes of variation, in this space.
Diffusion Tensor Image Registration Using Tensor Geometry and Orientation Features
TLDR
The robustness of the method makes it potentially useful for group-based analysis of DT images acquired in large studies to identify disease-induced and developmental changes.
Continuous Tensor Field Approximation of Diffusion Tensor MRI data
TLDR
A piecewise continuous approximation based on Non-Uniform Rational B-Splines (NURBS), which addresses some of the shortcomings of the previous implementation of the NURBS, is proposed.
A normal distribution for tensor-valued random variables: applications to diffusion tensor MRI
TLDR
A new normal distribution is proposed, p(D) /spl prop/ exp(-1/2 D : A : D), which describes the variability of D in an ideal DT-MRI experiment, and a new criterion for an optimal experimental design is proposed: that A be an isotropic fourth-order tensor.
Log‐Euclidean metrics for fast and simple calculus on diffusion tensors
TLDR
A new family of Riemannian metrics called Log‐Euclidean is proposed, based on a novel vector space structure for tensors, which can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms.
DTI registration with exact finite-strain differential
TLDR
It is shown with 40 pairwise DTI registrations that using the exact gradient achieves significantly better registration, and is compared with a classic alternative that does not take into account the reorientation in the gradient computation.
Spatial transformations of diffusion tensor magnetic resonance images
TLDR
One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective and improve the consistency between registered and target images over naive warping algorithms.
Diffeomorphic Matching of Diffusion Tensor Images
This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in
...
...