Slicer3:DTMRI

From NAMIC Wiki
Revision as of 18:54, 4 January 2007 by Lauren (talk | contribs)
Jump to: navigation, search
Home < Slicer3:DTMRI

Goal

Development of the infrastructure for DT-MRI processing and visualization and fiber processing and visualization. A secondary goal is the integration of new and existing methods and algorithms for DT-MRI processing using the provided infrastructure. This integration will have as goal the porting of the current DT-MRI capabilities existing in Slicer 2.x and the addition of new features.

Global Features

The general features can be grouped in:

  • Core features for DTMRI processing
  • Solution enviroments for DTMRI analysis

The first group will provide the necessary tools to build the Solutions that will be the user front-end.

Core features

  • Tensor Estimation from DWI: this part is a clear candidate for the an implementation using CLP. A desired feature would be the possibility of estimating tensors using different methods, namely:
    • Least Squares
    • Weighted Least Squares
    • Non-linear methods
    • Maximum Likelihood approach

Teem currently provides a clean interface to do this estimation in a voxel by voxel fashion. Gordon and Raul have worked on a vtk filter to encapsulate the estimation process.

  • Diffusion Weighted Images preprocessing: another candidate for CLP. Integration of Rician noise filtering done at Utah.
  • Tools for
    • Computation of scalar measurements from tensor fields
    • Fast rendering of tensor fields using glyphs: line, box, ellipsoid, superquadric.
    • Fiber Tracking using integration techniques
    • Statistics along fiber tracts
    • Multiple ROI seeding and logic interconnections between ROIs
    • Fiber clustering techniques
  • Algorithms for DT-MRI registration: Xiadoing et al from GE have presented a nice method for DWI registration that has great potential and deals in a clean way with many of the technical difficulties of registering only tensor fields.
  • Algorithms for DT-MRI segmentation.

Solution enviroments

  • Connectivity solution: enviroment for ROI definition and fiber bundling based on clustering techniques or logic operations.

Multiple ROI seeding and logical interconnection between ROIs.

  • Fiber editing solution: enviroment for manually editing individual fibers/bundles, reassignation of fibers to bundles.
  • Fiber analysis solution: enviroment to run statistical analysis on fiber bundles.
  • DT-MRI segmentation: enviroment for segmentation of DT-MRI fields
  • DT-MRI registration: enviroment for registration of DT-MRI fields (possibly via DWI registration -- work done at GE and presented in MICCAI '06).

Plan

We will achieve the aforementioned goal in two stages:

Stage 1

  • Design and Implementation of the basic infrastructure to handle DWI datasets and DT-MRI datasets
    • Development of the hierchachy of MRML nodes for the DWI and Tensor dataset representation
    • Development of Storage nodes to I/O these new datasets. Given the current limitation of the Archtype readers, we will temporally fall back on the vtkNRRDReader/Writer existing in Slicer2.x for I/O operations.
    • Definition of the basic logic for the display of DWI datasets and Tensor datasets
  • Design and Implementation of the basic infrastructure to handle fiber and fiber bundles.
    • Development of Fiber MRML nodes for Fiber and Fiber bundles representation.
    • Development of logic componets for fiber optimal rendering. There is a need for finding a good trade off between performance (real time interaction with fibers) and number of actors assigned to the fibers. This is an area that Kitware might contribute on.
    • Tracking method porting/implementation. It is argueable that we want to incorporate this as a CLP module if we want to keep real-time performance in terms of interactive tractography.

Stage 2

  • Implementation of core features based on the infrastructure.
  • Development of solution enviroments.


Applications/Use Cases for DTI in Slicer3

  • Quantitative measurement
    • Tract-based
    • Region of interest-based
  • fMRI seeding
  • Surgical planning
  • anatomical investigation/atlas creation