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| − | == Goal ==
| + | <big>'''Note:''' We are migrating this content to the slicer.org domain - <font color="orange">The newer page is [https://www.slicer.org/wiki/Slicer3:DTMRI here]</font></big> |
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| − | 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.
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| − | == Global Features ==
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| − | The general features can be grouped in:
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| − | * Core features for DTMRI processing
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| − | * Solution enviroments for DTMRI analysis
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| − | The first group will provide the necessary tools to build the Solutions that will be the user front-end.
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| − | === Core features ===
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| − | * 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:
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| − | ** Least Squares
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| − | ** Weighted Least Squares
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| − | ** Non-linear methods
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| − | ** Maximum Likelihood approach
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| − | 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.
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| − | * Diffusion Weighted Images preprocessing: another candidate for CLP. Integration of Rician noise filtering done at Utah.
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| − | * Tools for
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| − | ** Computation of scalar measurements from tensor fields
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| − | ** Fast rendering of tensor fields using glyphs: line, box, ellipsoid, superquadric.
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| − | ** Fiber Tracking using integration techniques
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| − | ** Statistics along fiber tracts
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| − | ** Multiple ROI seeding and logic interconnections between ROIs
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| − | ** Fiber clustering techniques
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| − | * 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.
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| − | * Algorithms for DT-MRI segmentation.
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| − | === Solution enviroments === | |
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| − | * Connectivity solution: enviroment for ROI definition and fiber bundling based on clustering techniques or logic operations.
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| − | Multiple ROI seeding and logical interconnection between ROIs.
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| − | * Fiber editing solution: enviroment for manually editing individual fibers/bundles, reassignation of fibers to bundles.
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| − | * Fiber analysis solution: enviroment to run statistical analysis on fiber bundles.
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| − | * DT-MRI segmentation: enviroment for segmentation of DT-MRI fields
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| − | * DT-MRI registration: enviroment for registration of DT-MRI fields (possibly via DWI registration -- work done at GE and presented in MICCAI '06).
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| − | == Plan ==
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| − | We will achieve the aforementioned goal in two stages:
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| − | === Stage 1 ===
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| − | * Design and Implementation of the basic infrastructure to handle DWI datasets and DT-MRI datasets
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| − | ** Development of the hierchachy of MRML nodes for the DWI and Tensor dataset representation
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| − | ** 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.
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| − | ** Definition of the basic logic for the display of DWI datasets and Tensor datasets
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| − | * Design and Implementation of the basic infrastructure to handle fiber and fiber bundles.
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| − | ** Development of Fiber MRML nodes for Fiber and Fiber bundles representation.
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| − | ** 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.
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| − | ** 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.
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| − | === Stage 2 ===
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| − | * Implementation of core features based on the infrastructure.
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| − | * Development of solution enviroments.
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| − | == Applications/Use Cases for DTI in Slicer3 ==
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| − | * Quantitative measurement
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| − | ** Tract-based
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| − | ** Region of interest-based
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| − | * fMRI seeding
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| − | * Surgical planning
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| − | * anatomical investigation/atlas creation
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