Diffusion Tensor Image Registration with Combined Tract and Tensor Features

@article{Wang2011DiffusionTI,
  title={Diffusion Tensor Image Registration with Combined Tract and Tensor Features},
  author={Qian Wang and Pew-Thian Yap and Guorong Wu and Dinggang Shen},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2011},
  volume={14 Pt 2},
  pages={
          200-8
        }
}
  • Q. Wang, P. Yap, D. Shen
  • Published 18 September 2011
  • Computer Science
  • Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Registration of diffusion tensor (DT) images is indispensible, especially in white-matter studies involving a significant amount of data. This task is however faced with challenging issues such as the generally low SNR of diffusion-weighted images and the relatively high complexity of tensor representation. To improve the accuracy of DT image registration, we design an attribute vector that encapsulates both tract and tensor information to serve as a voxel morphological signature for effective… 
View on PubMed
link.springer.com
4 Citations
Background Citations
2
Methods Citations
2

4 Citations

Diffusion tensor image registration using hybrid connectivity and tensor features
TLDR
Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone.
Simultaneous Tensor and Fiber Registration (STFR) for Diffusion Tensor Images of the Brain
TLDR
A simultaneous tensor and fiber registration (STFR) algorithm is introduced by matching both Tensor and Fiber tracts at each voxel and considering re-orientation with deformation simultaneously to improve the accuracy of DTI registration.
DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning
TLDR
This work proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning, which can more reasonably trace to the structure of the nerve fibers in diffusion tensor image analysis.
Spatial Transformation of DWI Data Using Non-Negative Sparse Representation
  • P. Yap, D. Shen
  • Computer Science
    IEEE Transactions on Medical Imaging
  • 2012
TLDR
An algorithm to transform and reconstruct diffusion-weighted imaging data for alignment of micro-structures in association with spatial transformations by decomposing the diffusion-attenuated signal profile into a series of weighted diffusion basis functions (DBFs), reorient these weighted DBFs independently based on a local affine transformation, and then recompose the reoriented weightedDBFs to obtain the final transformed signal profile.

References

SHOWING 1-10 OF 21 REFERENCES
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.
Deformable registration of diffusion tensor MR images with explicit orientation optimization
F-TIMER: Fast Tensor Image Morphing for Elastic Registration
TLDR
Experimental results indicate that F-TIMER is capable of achieving comparable accuracy at only a fraction of the computation cost, and a hierarchical strategy where a small subset of voxels with more distinctive attribute vectors are deployed as landmarks to estimate a relatively robust low-degrees-of-freedom transformation.
Fluid Registration of Diffusion Tensor Images Using Information Theory
TLDR
The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.
Spatial normalization of diffusion tensor MRI using multiple channels
Nonrigid registration of 3D tensor medical data
Spatial normalization of diffusion tensor fields
TLDR
The results show a significant improvement in signal‐to‐noise ratio (SNR) after spatial normalization and averaging of tensor fields across individuals, which may be useful for quantitatively characterizing individual variations of white matter structures revealed by DT imaging (DTI) and deviations caused by pathology.
Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data
TLDR
An algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations is provided and it is shown that this algorithm outperforms conventional polynomial methods for the interpolation of sparse vector fields.
Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building
TLDR
A method for automatically finding correspondence in Diffusion Tensor Imaging from deformable registration to a common atlas is presented, which provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.
Automatic Deformable Diffusion Tensor Registration for Fiber Population Analysis
TLDR
Results on brain DTI indicate that the proposed method is very suitable for deformable fiber-to-fiber correspondence and DTI-atlas construction.
...
...