Transcript Diffusion Tensor Image Analysis

Diffusion Weighted Imaging
Tensor Analysis
Vincent A. Magnotta
Associate Professor
March 21, 2011
Diffusion Tensor Analysis Flow Chart
DTIPrep
DTI
Data
Collection
(DICOM)
Resample
Images
Into
ACPC
Space
Images
Format
Conversion
Non-Rigid
Co-Register
With AC-PC
Aligned T1
1. Verify Acquisition
2. Artifact Detection
3. Motion Correction
4. Update Gradient
Directions
5. Remove Bad Data
Concatenate
Data
Rigid
Co-Register
With AC-PC
Aligned T1
Extract
B0
Image
Create
Diffusion
Scalar
Images
Generation
Of Diffusion
Tensor
Diffusion Tensor Image Analysis
•
Image format conversion
– Change from DICOM to Nifti or NRRD image formats
– Rotate applied diffusion gradients
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Motion Correction
– Account for patient motion and eddy current artifacts
•
Generation of Diffusion Tensor
– Includes possible edge preserving low pass spatial filtering
– Use rotated diffusion directions
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Create Diffusion Tensor scalar maps
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Mean diffusivity
Fractional Anisotropy
Relative anisotropy
Radial Diffusivity
Axial Diffusivity
Co-register with anatomical image
– Rigid
– Non-Rigid (B-Spline)
Image Format Conversion
• Convert from DICOM to NRRD format
– Nearly Raw Raster Data
– Defines origin, spacing, orientation, Diffusion
Gradients, and Measurement Frame
– Coordinate frame for the applied diffusion gradients
• All information obtained from DICOM header
– Siemens, Philips, and GE scanners
DicomToNrrdConverter \
--inputDicomDirectory /home/vince/images/dti_images \
--outputDirectory /home/vince/images \
--outputVolume /home/vince/images/SUBJECT_DWI.nhdr
DTI Concatenation
• Concatenate multiple DTI runs together
– Improve SNR of tensor estimation
– Runs can contain any number of gradient
directions and orientations
gtractConcatDwi --outputVolume dti.nhdr \
--inputVolume dti_parta.nhdr,dti_partb.nhdr
Artifacts
Improving DTI measures: DTIPrep
• From UNC, Zhexing Liu
• Purpose of DTIPrep: provide individual and group
quality control of DWI/DTI data sets in GUI and
command line mode
– Detect and remove artifacts that often appear in DWI data
– Prevent artifacts from creating DTI estimation errors in
tensor principle orientation (premature fiber tracking
termination) and scalars
– Prevent low consistency in quality control associated with
current visual checking of DWI data sets
DTIPrep: Quality Control Pipeline
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Image information checking
Diffusion information checking
Slice-wise intensity artifact checking
Interlace-wise venetian blind artifact checking
Baseline averaging
Eddy-current and head motion artifact correction
Gradient-wise checking (motion artifact checking)
DTIPrep: Quality Control Pipeline
• Image information
checking
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Image space
Image directions
Image size
Image spacing
Image origin
Cropping
• Diffusion information
checking
– b value
– Diffusion gradient
vectors
– Tolerance tests
– Replacement of diffusion
gradient vectors with
those in acquisition
protocol
DTIPrep: Quality Control Pipeline
• Venetian blind artifact detection
• Baseline averaging
– Motion between baseline scans is removed by rigidly
registering all baseline scans and averaging them together
– The averaged baseline image is used as a reference for
subsequent eddy-current and head motion artifact
correction for all gradients
• Eddy-current and head motion artifacts correction
• Resulting image is SUBJECT_DWI_Qced.nhdr
DTIPrep –DWINrrdFile /home/vince/images/SUBJECT_DWI.nhdr \
--xmlProtocol /home/vince/images/default.xml \
--default --check --outputFolder /home/vince/images
DTIPrep Outputs
• NRRD file containing
– Single baseline average image (motion corrected)
– Corrected Diffusion gradients
• Passed quality control (slice-wise & interlace)
• Head motion corrected (Rigid register to baseline with gradient direction
adjustments relative to anatomical frame of reference)
• Eddy current corrected (Affine register to baseline)
– SUBJECT_DWI_Qced.nhdr
• Report on excluded diffusion gradients
– SUBJECT_DWI_QcReport.txt
• Optional outputs: NRRD files of excluded diffusion gradients from
each quality control step
• DTIPrep outputs  GTRACT
DTIPrep GUI
DTIPrep: Quality Control Pipeline
Slice-to-slice correlation value
2.4 Slice-wise intensity related artifacts checking
Analysis region
Slice number
We propose to use Normalized Correlation (NC) between successive slices across
all the diffusion gradients for screening the intensity related artifacts.
Translation (mm), Angle of rotation (degrees)
DTIPrep: Quality Control Pipeline
2.5 Interlace-wise Venetian blind artifact checking
Gradient number
Venetian blind like artifacts can be detected via correlations and motion
parameters between the interleaved parts for each gradient volume.
DTIPrep: Quality Control Pipeline
2.8 Gradient-wise checking
Motion artifact residuals after eddy-current and head motion corrections can be
detected via motion parameters between baseline and each of the gradients.
DTIPrep Impact on FA values
• Exclusion of optimal number of gradients minimized
the standard deviation in FA values
Standard deviation in FA
Standard deviation in FA
– Standard deviation was lowered in a single scan processed
by DTIPrep
Without DTIPrep
9%
21%
26%
27%
With DTIPrep
Create Diffusion Tensor
• Create Tensor representation of diffusion process
– Defined by 6 unique parameters
– Allows for edge preserving low pass filtering (median)
whose radius is defined in voxels
– Removal of background signal
gtractTensor \
--inputVolume Subject_DTIPREP.nhdr \
--outputVolume SUBJECT_Tensor.nhdr \
--medianFilterSize 1,1,1
--backgroundSuppressingThreshold 50
--b0Index 0
Rotationally Invariant Scalar
Generation
• Eigen analysis of tensor data
• Creates a variety of scalars:
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FA – Fractional Anisotropy
MD – Mean Diffusivity
RA – Relative Anisotropy
LI – Lattice Index
AD – Axial Diffusivity
RD – Radial Diffusivity
gtractAnisotropyMap \
--inputTensorVolume Subject_Tensor.nhdr \
--outputVolume SUBJECT_FA.nii.gz \
--anisotropyType FA
Image Extraction and Clipping
• Extract B0 image
• Clip B0 image to remove skull using AFNI
extractNrrdVectorIndex --index 0\
--inputVolume Subject_DTIPREP.nhdr \
--outputVolume Subject_B0.nii.gz
3dAutomask -prefix Subject_DWI_B0_mask.nii.gz \
Subject_B0.nii.gz
3dcalc -a Subject_DWI_B0_mask.nii.gz \
-datum short -expr "a*1" \
-prefix Subject_B0_maskShort.nii.gz
3dcalc -a Subject_B0_maskShort.nii.gz \
-b Subject_DWI_B0.nii.gz -expr "a*b" \
-prefix Subject_DWI_B0_Brain.nii.gz
DWI to Anatomical Registration
• Utilize BRAINSFit image registration
– Supports Mutual Information registration metric
• Non-linear image registration
– B-splines can be used to correct for susceptibility
artifacts
– Eliminates the need for field maps
BRAINSFit –movingVolume Subject_DWI_B0_Brain.nii.gz \
--fixedVolume Subject_clippedT1.nii.gz\
--transformType Rigid,BSpline \
--numberOfSamples 500000 \
--splineGridSize 12,12,12 \
--outputTransform SUBJECT_ACPC.mat \
--initializeTransformMode useMomentsAlign
Invert Transform
• Provide a mapping from AC-PC apace back to the
DTI space
• Approximate inverse is computed using Thin Plate
Spline (TPS) transforms
• Used to map ROIs into DTI space for fiber tracking
gtractInvertBSplineTransform \
--inputTransform SUBJECT_ACPC.mat \
--outputTransform SUBJECT_ACPC_Inverse.mat \
--inputReferenceVolume Subject_clippedT1.nii.gz
Resample DTI Scalars
• Place rotationally invariant scalars into the space
of anatomical images
• Resample B0 image to check quality of registration
BRAINSResample \
--referenceVolume SUBJECT_T1.nii.gz \
--inputVolume SUBJECT_FA.nii.gz \
--warpTransform SUBJECT_ACPC.mat \
--outputVolume SUBJECT_FA_ACPC.nii.gz
--interpolationMode Linear
Diffusion Tensor Scalar Measurements
• Lobar Talairach Analysis
– Frontal, Temporal, Parietal, Occipital, and Cerebellar white
matter measurements
– White matter region defined using both FA and tissue
classified images
– BRAINS measurement script exists
Diffusion Tensor Scalar
Measurements A-P
• Analysis of Anisotropy
from Anterior-Posterior
based on Talairach Atlas
1
0.9
Fractional Anisotropy
– Divide regions from A-D
and F-I in half
– Retain sizes of E1, E2 and
E3
– BRAINS script exists
Patients
0.8
Controls
0.7
P values
0.6
0.05 line
0.5
0.4
0.3
0.2
0.1
0
A
A.5
B
B.5
C
C.5
D
D.5 E1-2 E2-3 E3-3
F
-0.1
Anterior to Posterior
F.5
G
G.5
H
H.5
I
I.5
SPM Analysis
• Co-register to Atlas
image
– Apply transform to DTI
scalar image
– Smooth Scalar images
– Threshold to White
matter regions
– Possible issues with
anatomic variability
Fiber Tracking - Introduction
Base on the directional information
provided by DTI, fiber tracking can
be used to explore the underlying
white matter fiber structure noninvasively