national alliance for medical image computing unc: quantitative dti analysis guido gerig, isabelle...

National Alliance for Medical Image Computing http://na-mic.org

UNC: Quantitative DTI Analysis

Guido Gerig, Isabelle CorougeStudents: Casey Goodlett, Clement Vachet, Matthieu Jomier


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


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

Patient

Control

Quantitative DTI Analysis

UNC NA-MIC Approach:

• Quantitative Analysis of Fiber Tracts

• DTI Tensor Statistics across/along fiber bundles

• Statistics of tensors

Tracking/

clustering

selection

FA FA along tract


Processing Tools

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

FiberViewer: Clustering, Bundling, Parametrization, Statistics, Visualization


Example: Fiber-tract Measurements

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

uncinate fasciculus

uncinate fasciculus FA along uncinate

cingulumFA along cingulate

Major fiber tracts


Processing Steps

• Tractography– Data structure for sets of

attributed streamlines

• Clustering• Parametrization• 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) Display/Files Biostatistics / ev. DTI

hypothesis testing (UTAH)

Concept: Statistics along fiber tracts

Origin (anatomical landmark)

FA


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


ITK Polyline Datastructure


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

Uncinate fasciculus

Longitudinal fasciculus

Clustering can separate neighboring bundles

Not possible with region-based processing

501 streamlines


3D Curve Clustering

Whole longitudinal fasciculus: 2312 streamlines 6 clusters

seeding


Scan1

Scan2…

T B01B02

… Scan6

DTI Average

Validation: 6 repeated DTI

T B01B06

Extraction

Extraction

Extraction

Scan 2…

…Scan 6

DTI Average

Direct Average of the 6 scans

Selection of a ROI

Registration of ROI


Scans' Comparison: FA (ROI: essai3-th69)

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Points

FA

Mean Mean+std Mean-std

Scans' Comparison: ADC (ROI: essai3-th69)

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AD

C

Mean Mean+std Mean-std

Statistics across 6 repeated scans:

Curves of MeanFA and MeanADC, with Standard Deviation

FA

FA

ADC

Tract-based Diffusion Properties


Tract-based Diffusion Properties

Curves of MeanFA/ MeanADC in comparison to the Average DTI

FA

Scans' Comparison: FA (ROI: essai3-th69)

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Points

FA

Mean-FA DTIAverage-FA

Scans' Comparison: ADC (ROI: essai3-th69)

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AD

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MeanADC DTIAverage-ADC

FA

ADC


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??

• Papers: “Bad slices were eliminated from processing”

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



• UNC Solution: ITK DTIchecker (Matthieu Jomier)

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

• Writes report / Script file



• 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

• 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

• 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

• Geometric characterization of fiber bundles

• Local shape descriptors: curvature and torsion

Adults Neonate

Max. curvature positions: Possible candidates for curve matching

national alliance for medical image computing unc: quantitative dti analysis guido gerig, isabelle...

Documents

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uncgraph cutnational

normalized graph cutsngc

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development casey goodlett

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itk version