Difference between revisions of "Modules:DiffusionMRIWelcome-Documentation-3.6"
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|[[File:Roi tract.jpg|thumb|center|upright=2|Deterministic tractography result produced with the [[Modules:ROISeeding-Documentation-3.6 | Label Seeding]] or [[Modules:FiducialSeeding-Documentation-3.6|Fiducial Seeding]] modules.]]|| |[[File:General.png|thumb|center|upright=2|Stochastic tractography result produced with the [[Modules:StochasticTractography-Documentation-3.6|Python Stochastic Tractography]] module.]] | |[[File:Roi tract.jpg|thumb|center|upright=2|Deterministic tractography result produced with the [[Modules:ROISeeding-Documentation-3.6 | Label Seeding]] or [[Modules:FiducialSeeding-Documentation-3.6|Fiducial Seeding]] modules.]]|| |[[File:General.png|thumb|center|upright=2|Stochastic tractography result produced with the [[Modules:StochasticTractography-Documentation-3.6|Python Stochastic Tractography]] module.]] | ||
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− | *[[Modules:ROISeeding-Documentation-3.6 | Label Seeding]]: | + | *[[Modules:ROISeeding-Documentation-3.6 | Label Seeding]]: Deterministic tracing of the white matter fibers traversing a specified labeled region of the diffusion tensor image. |
− | *[[Modules:FiducialSeeding-Documentation-3.6|Fiducial Seeding]]: | + | *[[Modules:FiducialSeeding-Documentation-3.6|Fiducial Seeding]]: Deterministic tracing of the white matter fibers traversing each [[Modules:Fiducials-Documentation-3.6|fiducial]] from a [[Modules:Fiducials-Documentation-3.6|fiducial list]]. |
− | *[[Modules:DTIDisplay-Documentation-3.6|FiberBundles]] | + | *[[Modules:DTIDisplay-Documentation-3.6|FiberBundles]]: Tuning of the visualization options for the deterministic tractography results produced with the [[Modules:ROISeeding-Documentation-3.6 | Label Seeding]] or [[Modules:FiducialSeeding-Documentation-3.6|Fiducial Seeding]] modules. |
*[[Modules:StochasticTractography-Documentation-3.6|Python Stochastic Tractography]]: | *[[Modules:StochasticTractography-Documentation-3.6|Python Stochastic Tractography]]: | ||
*[[Modules:ROISelect-Documentation-3.6|ROI Select]]: | *[[Modules:ROISelect-Documentation-3.6|ROI Select]]: |
Revision as of 17:39, 8 May 2010
Home < Modules:DiffusionMRIWelcome-Documentation-3.6Contents
Diffusion MRI in 3D Slicer
An rich set of tools is available within 3D Slicer to perform Diffusion MRI image visualization and analysis. There are three categories of modules: DWI filtering for denoising the Diffusion Weighted (DW) images; Diffusion tensor utilities for estimating diffusion tensors (DT) from DW images and calculating scalar invariants like the fractional anisotropy (FA) and resampling the DT images; and Tractography to trace and analyse white matter fibers from DT images and assess connectivity between regions from DW images. |
Diffusion MRI Modules
DWI Filtering
Three techniques are provided for denoising DW images:
- Unbiased Non Local Means filter for DWI: Is the one providing the most visually appealing results. However, it is very time consuming and may mix information from remote areas of the image.
- Rician LMMSE Image Filter: Estimates Rician noise and uses this estimation and spatial coherence to perform the denoising. It processes each gradient direction individually.
- Joint Rician LMMSE Image Filter: Estimates Rician noise and uses this estimation, spatial and orientational coherence to perform the denoising. It jointly processes several gradient directions.
Diffusion Tensor Utilities
- Diffusion Tensor Estimation: Produces a diffusion tensor image from a DW image.
- Diffusion Tensor Scalar Measurements: Calculates scalar invariants such as the fractional anisotropy (FA) or the linear measure (LM) from a diffusion tensor image.
- Resample DTI Volume: Increases or decreases the resolution of a diffusion tensor image.
Tractography
- Label Seeding: Deterministic tracing of the white matter fibers traversing a specified labeled region of the diffusion tensor image.
- Fiducial Seeding: Deterministic tracing of the white matter fibers traversing each fiducial from a fiducial list.
- FiberBundles: Tuning of the visualization options for the deterministic tractography results produced with the Label Seeding or Fiducial Seeding modules.
- Python Stochastic Tractography:
- ROI Select: