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

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

Deterministic tractography result produced with the Label Seeding or Fiducial Seeding modules.

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: