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=== Quantitative Analysis of Fiber Tract Bundles === | === Quantitative Analysis of Fiber Tract Bundles === | ||
| − | + | DTI tractography can be used as a coordinate system for computing statistics of diffusion tensor data. The quantitative analysis of diffusion tensors takes into account the space of tensor measurements using a nonlinear Riemannian symmetric space framework. Tracts of interest are represented as a medial spline attributed with cross-sectional statistics. | |
[[Algorithm:UNC:DTI:Quantitative_Tract_Analysis|More...]] | [[Algorithm:UNC:DTI:Quantitative_Tract_Analysis|More...]] | ||
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=== Population Analysis from Deformable Registration === | === Population Analysis from Deformable Registration === | ||
| − | + | Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. | |
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[[Algorithm:UNC:DTI:Population_Analysis|'''Description''']] - [[Algorithm:UNC:DTI:Population_Analysis#Publications|'''Publications''']] - [[Algorithm:UNC:DTI:Population_Analysis#Software|'''Software''']] | [[Algorithm:UNC:DTI:Population_Analysis|'''Description''']] - [[Algorithm:UNC:DTI:Population_Analysis#Publications|'''Publications''']] - [[Algorithm:UNC:DTI:Population_Analysis#Software|'''Software''']] | ||
Revision as of 16:25, 2 April 2007
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Contents
Diffusion Tensor Imaging
Quantitative Analysis of Fiber Tract Bundles
DTI tractography can be used as a coordinate system for computing statistics of diffusion tensor data. The quantitative analysis of diffusion tensors takes into account the space of tensor measurements using a nonlinear Riemannian symmetric space framework. Tracts of interest are represented as a medial spline attributed with cross-sectional statistics.
Description - Publications - Software
Population Analysis from Deformable Registration
Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties.
Description - Publications - Software
Shape Analysis of Brain Structures Across Groups
Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This project focuses on developing novel methodology and a comprehensive set of tools for the computation of 3D structural statistical shape analysis. There are several open problems in this area, ranging from multi-object analysis, enhanced shape correspondence to statistical analysis of shape with clinical covariates.
UNC Shape Analysis Framework using SPHARM-PDM
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The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. More... New:
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Population Based Correspondence
We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. More...
New:
- Software available as part of UNC Neurolib open source (website)
- Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.
- Submission to MICCAI 2007 conference
- Tobias Heimann, I. Oguz, I. Wolf, M. Styner, HP. Meinzer. Implementing the Automatic Generation of 3D Statistical Shape Models with ITK. Accepted to MICCAI 2006 Open Source Workshop. More...
Description - Publications - Software
Local Statistical Analysis via Permutation Tests
We have further developed a set of statistical testing methods that allow the analysis of local shape differences using the Hotelling T 2 two sample metric. Permutatioin tests are employed for the computation of statistical p-values, both raw and corrected for multiple comparisons. Resulting significance maps are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Ongoing research focuses on incorporating covariates such as clinical scores into the testing scheme. More...
New:
- Available as part of Shape Analysis Toolset in UNC Neurolib open source (download).
- M. Styner, I. Oguz, S. Xu, C. Brechbuehler, D. Pantazis, J. Levitt, M. Shenton, G. Gerig. Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM. Accepted to MICCAI 2006 Open Source Workshop. More...
Description - Publications - Software
Collaborations with other groups in NAMIC
- Algorithms:
- Shape Analysis
- Joint pipeline I/O formulation and development with Kitware (Brad Davis, Jim Miller) and MIT (Polina Golland)
- Use of UNC statistical analysis for spherical wavelet shape with GeorgiaTech (Delphine Nain) and Utah (Tom Fletcher)
- Use of UNC statistical analysis for combined multi-object correspondence establishment with Utah (Josh Cates, Tom Fletcher)
- DTI
- Statistics of tensors and noise in diffusion weighted imaging with Utah (Tom Fletcher)
- Shape Analysis
- Clinical:
- Collaboration with Harvard on shape analysis and DTI analysis.
Old content, delete after full update
Nov 30, 2004
DTI: Quantitative analysis of fiber bundles
Components of FiberViewer Project
Input: Sets of streamlines from tractography:
ITK polyline format with DTI attributes
Clustering of sets of streamlines to sets of
strong fiber bundles using various curve
distance metrics
Parametrization of sets of lines and
reparametrization for equidistant sampling
with arc-length
Calculation of local geometric properties of
streamlines (Frenet frame, curvature, torsion)
Attributing streamlines at each sample point
with DTI measurements (ADC, FA, lambda1..3,
ev. whole tensor) via trilinear interpolation
Calculation of DTI statistics within cross-
sections along fiber tracts after selection
of coordinate origin
Write statistics (DTI properties as a function
of arclength) to a text file for statistical
analysis.
Output: Processed, cleaned and clustered fiber
bundles (ITK polyline format) / Statistics
of DTI properties per selected bundle
Software
- Algorithms written in ITK. GUI of prototype software written in QT (FiberViewer software). Prototype software tested in clinical studies at UNC. Validation tests with repeated DTI of same subject (6 cases).
- Additionally available: ITK compatible fibertracking prototype tool FiberTracking to be used to study overlap/dissimilarity with other tools already available to NA-MIC: Functionality: reads raw MRI-DT data (6 direction Basser scheme), fiber tracking based on user-selected source and regions (S. Mori scheme), display of fibertracts and volumetric data, output: sets of streamlines in ITK polyline format attributedwith DTI properties and display parameteres (radiusof tubes, local color, etc.).
DTI Training Tools
DTI Training Tools (Downloadable in zip package File:DTI-Training-Tools.zip)
- Glyphs - displays FA slices and tensor field
- Fiber - simplistic tractography from single voxel
- Conn - display of Riemannian flow from voxel
- MRIWatcher - displays a set of MRI volumes simultaneously. MRTWatcher can also overlay segmentation mask.
The manual for DTI Training Tools is included in zip file. Source code for the programs is available from UNC NeuroLib.
Recent Activities
- UNC has developed a DTIFiberClass, which is now available as part of ITK
- UNC has initiated the discussion for a standard format within NAMIC of representing DTI Tensor data and DTI Fibers
- All software is available for download via anonymous download from our CVS server
- pserver:anonymous@demeter.ia.unc.edu . Dashboard and Testing procedures have been installed and are operational.
Plans
- Feasibility tests on DTI data from NAMIC clinical partners
- DTI standardization issues (AHM SLC and follow-up)
- Evaluation of Slicer integration
Long term
- DTI Fiber shape representation tools
- Correspondence via fiber bundles (shape, parametrization)
- Fiber clustering via Normalized Cuts (ITK filter)
Relationship to other NA-MIC partners
DTI preprocessing (smoothing, interpolation):
MGH, Utah: Improve quality of raw DTI
data, Resampling DTI including tensor
re-orientation and full-tensor interpolation
DTI tensor statistics using Lie Group analysis:
Utah (Tom Fletcher), will replace the
simple averaging of FA, ADC, lambda1..3
as shown in "Averaging" step above
DTI tensor calculation, tractography, combined
display: Slicer, DoDTI (C.-F. Westin):
Processing of DTI to get fiber tracts of
interest for subsequent FiberViewer analysis
DTI atlas: probabilistic DTI atlas of normal
controls (MGH): To be used as reference
DTI atlas of normal controls
DTI annotated fiber tract atlas-template:
M. Farlow: To be used to calculate
fiber bundle properties for each annonated
tract, to serve as refrence template for
geometry and location of bundles as well
as for DTI properties as a function of
bundles.
DTI clinical data schizophrenia: M. Shenton
