Difference between revisions of "Modules:DiffusionMRIWelcome-Documentation-3.6"
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== Diffusion Tensor Utilities== | == Diffusion Tensor Utilities== | ||
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| + | |[[File:Tensor2.png|thumb|center|upright=2|Diffusion tensor image estimated from a DW image using the [[Modules:DiffusionTensorEstimation-Documentation-3.6|Diffusion Tensor Estimation]] module.]] | ||
| + | |[[File:Fa2.png|thumb|center|upright=2|Fractional anisotropy image calculated from a diffusion tensor image using the [[Modules:DiffusionTensorScalarMeasurements-Documentation-3.6 | Diffusion Tensor Scalar Measurements]] module.]] | ||
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*[[Modules:DiffusionTensorEstimation-Documentation-3.6|Diffusion Tensor Estimation]]: Produces a diffusion tensor image from a DW image. | *[[Modules:DiffusionTensorEstimation-Documentation-3.6|Diffusion Tensor Estimation]]: Produces a diffusion tensor image from a DW image. | ||
*[[Modules:DiffusionTensorScalarMeasurements-Documentation-3.6 | Diffusion Tensor Scalar Measurements]]: Calculates scalar invariants such as the fractional anisotropy (FA) or the linear measure (LM) from a diffusion tensor image. | *[[Modules:DiffusionTensorScalarMeasurements-Documentation-3.6 | Diffusion Tensor Scalar Measurements]]: Calculates scalar invariants such as the fractional anisotropy (FA) or the linear measure (LM) from a diffusion tensor image. | ||
Revision as of 17:49, 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
Result of the different DWI filtering algorithms. From left to right: Original images, Unbiased Non Local Means filter for DWI; Rician LMMSE Image Filter; Joint Rician LMMSE Image Filter. Image from A. Tristán-Vega and S. Aja-Fernández. DWI filtering using joint information for DTI and HARDI. Medical Image Analysis, 2009.
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 image estimated from a DW image using the Diffusion Tensor Estimation module. |
 Fractional anisotropy image calculated from a diffusion tensor image using the Diffusion Tensor Scalar Measurements module. |
- 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
 |
 Stochastic tractography result produced with the Python Stochastic Tractography module. |
- 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:
