national alliance for medical image computing unc: quantitative dti analysis guido gerig, isabelle...
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National Alliance for Medical Image Computing http://na-mic.org
UNC: Quantitative DTI Analysis
Guido Gerig, Isabelle CorougeStudents: Casey Goodlett, Clement Vachet, Matthieu Jomier
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
Processing Tools
FibTrac: Input DT-MRI, Filtering, Tensor Calc., FA, ADC, Tractography
FiberViewer: Clustering, Bundling, Parametrization, Statistics, Visualization
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
ITK Polyline Datastructure
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
3D Curve Clustering
Uncinate fasciculus
Longitudinal fasciculus
Clustering can separate neighboring bundles
Not possible with region-based processing
501 streamlines
National Alliance for Medical Image Computing http://na-mic.org
3D Curve Clustering
Whole longitudinal fasciculus: 2312 streamlines 6 clusters
seeding
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
Scans' Comparison: FA (ROI: essai3-th69)
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Points
FA
Mean Mean+std Mean-std
Scans' Comparison: ADC (ROI: essai3-th69)
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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
National Alliance for Medical Image Computing http://na-mic.org
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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Points
FA
Mean-FA DTIAverage-FA
Scans' Comparison: ADC (ROI: essai3-th69)
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MeanADC DTIAverage-ADC
FA
ADC
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
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????
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
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).
National Alliance for Medical Image Computing http://na-mic.org
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
National Alliance for Medical Image Computing http://na-mic.org
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)
National Alliance for Medical Image Computing http://na-mic.org
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