Difference between revisions of "Modules:DiffusionMRIWelcome-Documentation-3.6"

From Slicer Wiki
Jump to: navigation, search
Line 26: Line 26:
 
|[[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.]]
 
|}
 
|}
*[[Modules:ROISeeding-Documentation-3.6 | Label Seeding]]: Traces the white matter fibers traversing a specified labeled region of the diffusion tensor image.  
+
*[[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]]: Traces the white matter fibers traversing each [[Modules:Fiducials-Documentation-3.6|fiducial]] from a [[Modules:Fiducials-Documentation-3.6|fiducial list]].
+
*[[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]] Visualization options for the tractography results produced with the [[Modules:ROISeeding-Documentation-3.6 | Label Seeding]] or [[Modules:FiducialSeeding-Documentation-3.6|Fiducial Seeding]] modules.
+
*[[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-3.6

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

Tractography

Deterministic tractography result produced with the Label Seeding or Fiducial Seeding modules.
Stochastic tractography result produced with the Python Stochastic Tractography module.