Diffusion Weighted ImagingTensor Analysis

Vincent A. MagnottaAssociate Professor

March 21, 2011

Diffusion Tensor Analysis Flow Chart

DTIData

Collection(DICOM)

ImagesFormat

Conversion

GenerationOf Diffusion

Tensor

RigidCo-RegisterWith AC-PCAligned T1

Non-RigidCo-RegisterWith AC-PCAligned T1

CreateDiffusion

ScalarImages

ResampleImages

Into ACPCSpace

Concatenate Data

DTIPrep

1. Verify Acquisition2. Artifact Detection3. Motion Correction4. Update Gradient

Directions5. Remove Bad Data

ExtractB0

Image

Diffusion Tensor Image Analysis• Image format conversion

– Change from DICOM to Nifti or NRRD image formats– Rotate applied diffusion gradients

• 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

• Create Diffusion Tensor scalar maps– 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

• 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– 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 Pipeline2.4 Slice-wise intensity related artifacts checking

We propose to use Normalized Correlation (NC) between successive slices across all the diffusion gradients for screening the intensity related artifacts.

Analysis region

Slice number

Slic

e-to

-slic

e co

rrel

ation

val

ue

DTIPrep: Quality Control Pipeline2.5 Interlace-wise Venetian blind artifact checking

Venetian blind like artifacts can be detected via correlations and motion parameters between the interleaved parts for each gradient volume.

Tran

slatio

n (m

m),

Angl

e of

rota

tion

(deg

rees

)

Gradient number

DTIPrep: Quality Control Pipeline2.8 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 was lowered in a single scan processed by

DTIPrep

Without DTIPrep With DTIPrep

Stan

dard

dev

iatio

n in

FA

Stan

dard

dev

iatio

n in

FA9% 21% 26% 27%

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:

– 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.gz3dcalc -a Subject_DWI_B0_mask.nii.gz \ -datum short -expr "a*1" \ -prefix Subject_B0_maskShort.nii.gz3dcalc -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– Divide regions from A-D

and F-I in half– Retain sizes of E1, E2 and

E3 – BRAINS script exists

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

A A.5 B B.5 C C.5 D D.5 E1-2 E2-3 E3-3 F F.5 G G.5 H H.5 I I.5

Anterior to Posterior

Frac

tiona

l Ani

sotro

py

PatientsControlsP values0.05 line

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 - IntroductionBase on the directional information provided by DTI, fiber tracking can be used to explore the underlying white matter fiber structure non-invasively