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UNC: Quantitative DTI Analysis. Guido Gerig, Isabelle Corouge Students: Casey Goodlett, Clement Vachet, Matthieu Jomier. UNC: Quantitative DTI Analysis. Clinical needs: Access to fiber tract properties: WM “Integrity”

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Unc quantitative dti analysis
UNC: Quantitative DTI Analysis

Guido Gerig, Isabelle Corouge

Students: Casey Goodlett, Clement Vachet, Matthieu Jomier

Unc quantitative dti analysis1
UNC: Quantitative DTI Analysis

  • Clinical needs:

    • Access to fiber tract properties: WM “Integrity”

    • Fibertract-oriented measurements: Diffusion properties within cross-sections and along bundles

    • Statistics of diffusion tensors: Beyond FA/ADC

  • Approaches:

    • Replace voxel-based by fiber-tract-based analysis

    • FiberViewer: Set of tools for quantitative fiber tract analysis: Geometry and Diffusion Properties

      • Clustering, Outlier Detection, Parametrization, Establishing inter-subject correspondence

    • Statistical analysis of DTI

Quantitative dti analysis

  • UNC NA-MIC Approach:

  • Quantitative Analysis of Fiber Tracts

  • DTI Tensor Statistics across/along fiber bundles

  • Statistics of tensors

Conventional Analysis: ROI or voxel-based group tests after alignment







FA along tract

Quantitative DTI Analysis

Processing tools
Processing Tools

FiberViewer: Clustering, Bundling, Parametrization, Statistics, Visualization

FibTrac: Input DT-MRI, Filtering, Tensor Calc., FA, ADC, Tractography

Example fiber tract measurements
Example: Fiber-tract Measurements


Major fiber tracts

FA along cingulate

uncinate fasciculus

uncinate fasciculus

FA along uncinate

Corouge, Isabelle, Gouttard, Sylvain and Gerig, Guido, "Towards a Shape Model of White Matter Fiber Bundles using Diffusion Tensor MRI" , Proc. IEEE Computer Society, Int. Symp. on Biomedical Imaging, to appear April 2004

Gerig, Guido, Gouttard, Sylvain and Corouge, Isabelle, "Analysis of Brain White Matter via Fiber Tract Modeling", full paper IEEE Engineering in Medicine and Biology Society EMBS 2004, Sept. 2004

Processing steps


Data structure for sets of attributed streamlines



Diffusion properties across/along bundles

Graph/Text Output

Statistical Analysis

Slicer (?)

ITK Polyline data structure (J. Jomier)

Normalized Cuts (ITK)

B-splines (ITK)

NEW: DTI stats in nonlinear space (UTAH)


Biostatistics / ev. DTI hypothesis testing (UTAH)

Processing Steps

Concept statistics along fiber tracts
Concept: Statistics along fiber tracts

Origin (anatomical landmark)


Accomplished 09 04 02 05
Accomplished 09/04 – 02/05

FiberViewer Prototype System (ITK)

  • Clustering (various metrics, normalized graph cut)

  • Parametrization

  • FA/ADC/Eigen-value Statistics

  • Uses SpatialObjects and SpatialObject-Viewer

  • ITK Datastructure for attributed streamlines

  • Tests in two UNC clinical studies (neonates, autism)

  • Validation of reproducibility: ISMRM’05

3d curve clustering with normalized graph cuts
3D Curve Clustering with Normalized Graph Cuts

  • NGC: Shi and Malik, IEEE 2000

  • Set-up of Matrix: Metric: Mean of distances at corresponding points of parametrized curves

  • Matlab prototype ready, ITK version in development (Casey Goodlett, UNC)

Graph Cut

3d curve clustering
3D Curve Clustering

Longitudinal fasciculus

501 streamlines

Uncinate fasciculus

Clustering can separate neighboring bundles

Not possible with region-based processing

3d curve clustering1
3D Curve Clustering


Whole longitudinal fasciculus: 2312 streamlines

6 clusters

Validation 6 repeated dti
Validation: 6 repeated DTI

Registration of ROI


T B01B02

Selection of a ROI

Scan 2…


T B01B06



… Scan6

…Scan 6


Direct Average of the 6 scans

DTI Average

DTI Average

Tract based diffusion properties
Tract-based Diffusion Properties

Statistics across 6 repeated scans:

Curves of MeanFA and MeanADC, with Standard Deviation




Tract based diffusion properties1
Tract-based Diffusion Properties

Curves of MeanFA/ MeanADC in comparison to the Average DTI




Work in progress statistics of tensors utah unc
Work in Progress: Statistics of Tensors (UTAH & UNC)

  • Statistics of DTI requires new math and tools

  • Linear Statistics does not preserve positive-definit.

  • Tom Fletcher UNC PhD 2004 (w. Joshi/Pizer), now UTAH

    • Riemannian symmetric (nonlinear) space

    • New similarity measure

    • Method for interpolation of tensors

We all like to pick the highlights who picks the dirty reality problems
we all like to pick the highlights, who picks the “dirty reality” problems??

  • Papers: “Bad slices were eliminated from processing”

  • But: +12 dir/ +4 averages / +25 slices:1200 images????

We all like to pick the highlights who picks the dirty reality problems1
we all like to pick the highlights, who picks the “dirty reality” problems??

  • UNC Solution: ITK DTIchecker (Matthieu Jomier)

  • Automatic screening for intensity artifacts, motion artifacs, missing/corrupted slices

  • Writes report / Script file

We all like to pick the highlights who picks the dirty reality problems2
we all like to pick the highlights, who picks the “dirty reality” problems??

  • Lucas MRI and MRS Center, Stanford University, CA : Spin echo EPI dti_epi Pulsed Gradient/Stejskal-Tanner diffusion weighting

  • UNC uses Stanford Bammer/Mosley “tensorcalc” software for DTI processing

  • Eddy Current Distortion Correction (here 23 directions)

  • Tensorcalc (“T1”) DWI/DTI recon toolbox with powerful built-in image registration tools. http://rsl.stanford.edu/research/software.html / http://www-radiology.stanford.edu/majh/

  • http://snarp.stanford.edu/dwi/maj/

The diffusion weighted images are unwarped using the method described in de Crespigny, A.J. and Moseley, M.E.: "Eddy Current Induced Image Warping in Diffusion Weighted EPI", Proc , ISMRM 6th Meeting, Sydney 661 (1998) and Haselgrove, J.C. and Moore, J.R., "Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient", MRM 1996, 36:960-964 ( Medline citation).

Next 6 months
Next 6 months reality” problems??

  • Methodology Development:

    • DTI tensor statistics: close collab. with UTAH

    • Deliver ITK tools for clustering/parametrization to Core 2

    • Feasibility tests with tractography from Slicer

    • Deliver FiberViewer prototype platform to Core 2 to discuss integration into Slicer

  • Clinical Study: DTI data from Core 3

    • Check feasibility of tract-based analysis w.r.t. DTI resolution (isotropic voxels(?)), SNR

    • Apply procedure to measure properties of:

      • Cingulate (replicate ROI findings of Shenton/Kubiki)

      • Uncinate fasciculus (replicate ROI findings)

      • Dartmouth 3mm DTI data

Na mic dti processing needs
NA-MIC DTI Processing Needs reality” problems??

  • Generic DTI reconstruction

    • Arbitrary #directions

    • Artifact checking/removal

    • Eddy-current distortion correction

    • Tensor calculation

  • Tensor Filtering (nonlinear, geodesic space)

  • Tensor interpolation, linear- and nonlinear registration

  • Tensor+ reconstruction/representation (DSI)

  • Standards for datastructures (DTI, tensors, streamlines, diffusion-gradient-file)

Local shape properties of wm tracts
Local shape properties of wm tracts reality” problems??

  • Geometric characterization of fiber bundles

  • Local shape descriptors: curvature and torsion



Max. curvature positions: Possible candidates for curve matching