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

Data Model

MRML Node definition for different data representations involved in DTI analysis

• Diffusion Weighted Images: vtkMRMLDiffusionWeightedVolumeNode and vtkMRMLDiffusionWeightedVolumeDisplayNode.
• Diffusion Tensor Images: vtkMRMLDiffusionTensorVolumeNode, vtkMRMLDiffusionTensorVolumeDisplayNode and vtkMRMLDiffusionTensorDisplayPropertiesNode.
• Fiber Bundles: vtkMRMLFiberBundleNode and vtkMRMLFiberBundleDisplayNode.

Storage and I/O

• DWI and DTI I/O: NRRD is the format supported by Slicer 3 for storing DWI and DTI images.
  • NNRD reader/writer: vtkNRRDReader and vtkNRRDWriter.
  • Storage node: vtkMRMLNRRDStorageNode.

Displaying Logic

• Slicer Layer Logic
• Geometry Layer Logic:

Diffusion Modelling

• 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