Documentation/4.0/Modules/DWIToDTIEstimation
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Introduction and Acknowledgements
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This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website. | |||||
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Module Description
Performs a tensor model estimation from diffusion weighted images.
There are three estimation methods available: least squares, weigthed least squares and non-linear estimation. The first method is the traditional method for tensor estimation and the fastest one. Weighted least squares takes into account the noise characteristics of the MRI images to weight the DWI samples used in the estimation based on its intensity magnitude. The last method is the more complex.
Use Cases
- Use Case 1: Calculate the DT image from a DWI image.
Tutorials
Links to tutorials that use this module
Panels and their use
Parameters:
- IO
- Input DWI Volume: Input DWI volume
- Diffusion Tensor Mask: Mask where the tensors will be computed
- Output DTI Volume: Estimated DTI volume
- Output Baseline Volume: Estimated baseline volume
- Estimation Parameters
- Estimation Parameters: LS: Least Squares, WLS: Weighted Least Squares
- Shift Negative Eigenvalues: Shift eigenvalues so all are positive (accounts for bad tensors related to noise or acquisition error)
Similar Modules
- Point to other modules that have similar functionality
References
- Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J., & Aldroubi, A. (2000). In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44(4), 625-632. John Wiley & Sons, Inc. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11025519
Information for Developers
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Section under construction. |
