From SlicerWiki
Jump to: navigation, search
Home < Documentation < Nightly < Modules < DWIToDTIEstimation

For the stable Slicer documentation, visit the 4.6 page.

Introduction and Acknowledgements

Title: Diffusion Tensor Estimation
Author(s)/Contributor(s): Raul San Jose, Lauren O'Donnell, Demian Wassermann, Isaiah Norton, Alex Yarmarkovich (SPL, LMI, BWH, SlicerDMRI)
License: 3D Slicer Contribution and Software License Agreement
Acknowledgements: This module is based on the estimation functionality provided by the Teem library.

The SlicerDMRI developers gratefully acknowledge funding for this project provided by NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research), NIH P41EB015898 (National Center for Image-Guided Therapy) and NIH P41EB015902 (Neuroimaging Analysis Center), as well as 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.


Surgical Planning Laboratory  

Module Description

Estimates the diffusion tensor model from diffusion weighted images.

There are two estimation methods available: least squares and weighted least squares. Least squares 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 based on their intensity magnitude.

Use Cases

  • Use Case 1: Calculate the Diffusion Tensor image from a Diffusion Weighted image.


Links to tutorials that use this module

Panels and their use


  • IO: Input/output parameters
    • Input DWI Volume (inputVolume): Input Diffusion Weighted Image (DWI) volume
    • Input Brain Mask (inputMaskVolume): Brain mask to restrict tensor computation region [optional]
    • Output DTI Volume (outputTensor): Estimated Diffusion Tensor Image (DTI) volume
    • Output Baseline Volume (outputBaseline): Estimated baseline (non-Diffusion Weighted) volume
  • Advanced Settings: Advanced estimation settings
    • Fitting Method ([Weighted] Least Squares) (estimationMethod): Fitting method. LS: Least Squares, WLS: Weighted Least Squares
    • Shift Negative Eigenvalues (ShiftNegativeEigenvalues): Shift eigenvalues so all are positive (accounts for unuseable tensor solutions related to noise or acquisition error)

List of parameters generated transforming this XML file using this XSL file. To update the URL of the XML file, edit this page.

Similar Modules


Information for Developers