diffusion processing using minc - mcconnell brain …€¦ · · 2014-02-25• what can i do with...

Diffusion Processing using MINCDiffusion Processing using MINC

mincdiffusion package

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Outline

• What can I do with diffusion imaging?– scalar maps– scalar maps– tractography

• I have my data from the scanner, now what?– what’s the format of the file– artifacts (vibration, susceptibility)

• What can mincdiffusion do for you?What can mincdiffusion do for you?– Preprocessing: diff_preprocess.pl– Fiber orientation estimation

• Tensor minctensor pl• Tensor : minctensor.pl• Spherical deconvolution: mincDeconvolve

– Fibertracking: mincFiberTrackVi li ti i 3D i– Visualization: minc3Dvis

• How do I get help2

Examples of what can be done with d ffdiffusion imaging

• Scalar maps p– to compare between populations– left‐right differences

t– etc.• Tractography

– to segment localize pathwaysto segment, localize pathways– to see how regions are connected through anatomy– to investigate tract‐specific properties, e.g., tract l th t t t t t t t klength, tract count, tract asymmetry, network properties

– etc.

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How do I make these pretty pictures?How do I make these pretty pictures?

FA

principle fibre direction map

tractography

ADC||

LRAPIS

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Outline• What can I do with diffusion imaging?

– scalar maps– scalar maps– tractography


• What can mincdiffusion do for youWhat can mincdiffusion do for you– Preprocessing: diff_preprocess.pl– Fiber orientation estimation




BIC Diffusion data

• File format– DICOM:DICOM:

• multiple 2D dicom files (MOSAIC) directly from scanner – MINC:

• a single 4D MINC converted using dcm2mnc (on the BIC• a single 4D MINC converted using dcm2mnc (on the BIC servers)

• each element in the time dimension is a diffusion direction• diffusion information in the followingmincheader fields• diffusion information in the following mincheader fields

– acquisition: b_valuese.g. mincheader diff.mnc | grep bvalueacquisition: bvalues= 0, 1000, 1000, 1000;

– acquisition: direction_x, direction_y, direction_ze.g. mincheader diff.mnc | grep direction acquisition: direction_x= 0, 1, 0, 0;acquisition: direction_y= 0, 0, 1, 0;acquisition: direction_x= 0, 0, 0, 1;

no diffusion weighting b=0 6

What the data should and should not l k l klook like

• 4D viewer: xdisp4D viewer: xdisp

• Contrast difference b = 0 and b ≠ 0

ibili if d d d• susceptibility artifacts dependent on readout direction

• vibration artifacts

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Distortion in phase‐encode directionDistortion in phase encode directionAnterior Anterior

Posterior Posterior

LeftRightB0‐map

note: FSL’s new eddy does correction for motion eddy‐currents and distortion (PE‐swap)

Basser et al 2002

note: FSLs new eddy does correction for motion, eddy currents and distortion (PE swap)B0‐map helps you tell where the distortions are

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What the data should and should not l k l klook like

• 4D viewer: xdisp4D viewer: xdisp

• Contrast difference b = 0 and b ≠ 0

ibili if d d d• susceptibility artifacts dependent on readout direction

• vibration artifacts– some cases affected as of 2009

– repair on 3T in January 2014

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Vibration ArtifactVibration Artifact

RGB mapRGB map

diff i di ti t 1 0 0diffusion direction vector: 1, 0, 0

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Remove the corrupted framesRemove the corrupted frames

• need to remove those frames (strong x‐direction)need to remove those frames (strong x direction)

• need to remove from header as well

• not ideal but most acquisitions >64 directionsnot ideal but most acquisitions >64 directions, should not greatly affect results

remove‐direction.pl “#,#,#,#” dwi.mnc dwi‐rem‐frames.mnc

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After the frames have been removedAfter the frames have been removed

remove‐direction.pl “2,18,24,36,37” dwi.mnc dwi‐rem‐frames.mnc

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After the frames have been removedAfter the frames have been removed

corrupted directions keeping corrupted co upted d ect o sremoved

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data

OutlineOutline• What can I do with diffusion imaging?

– scalar maps– scalar maps– tractography


• What can mincdiffusion do for you?What can mincdiffusion do for you?– Preprocessing: diff_preprocess.pl– Fiber orientation estimation




mincdiffusion packagemincdiffusion package

• Developed by Dr Jennifer CampbellDeveloped by Dr. Jennifer Campbell

• Primarily used for methods development

O d• Open‐source code– methods in C++

– matlab for tensor computation

– perl scripts for file manipulation

• Soon to be distributed as part of MINC

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Why would I want to use this package?Why would I want to use this package?Pros

• very flexible

Cons

• not a GUI• very flexible

• can ask for modifications/ implementations

• not a GUI

• small support team

• in development, not as widelyp

• visualization in 3D is very informative/pretty

in development, not as widely accepted as FSL, does not provide as many fancy tools ( i t ti TBSS )• works in MINC (registration, TBSS, …)

• probabilistic tractographyimplemented but not quite p qready for prime‐time (suggest FSL)

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What can mincdiffusion do for you?

Anatomical Tensor RGB

FA

MD

minctensordiff_preprocess

…mincDeconvolve

mincFibreTracktensors

…

DWIs

tractography

DWIs

ODFs17

Preprocessing• diff_preprocess.pl

– registers multiple runs, but not across them (FSL g p , (does)

– registers to the anatomical and takes care of diffusion direction rotations

– creates a brain maskdiff l d i1 d i2 d i i h 1

…

diff_preprocess.pl ‐anat anat.mnc dwi1.mnc dwi2.mnc dwi‐reg‐with‐t1.mnc

…

DWI1

T1w‐Anatomical Brain mask

DWI2

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mritoself minctracc18

Diffusion Tensor

diffusion signalbvalue

diffusion tensor

signal with no diffusion weightingsignal with no diffusion weighting

• Fit the diffusion signal to a tensor, can be described by an ellipsoid

e1λ1

principal diffusion direction• Need to acquire at least 6 non‐collinear directions

e2λ2e3λ3

d l3 3

diagonalize to get eigenvectors and eigenvalues

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Diffusion acquisitionDiffusion acquisition

iti t th diff i l th di ti f th di tsensitive to the diffusion along the direction of the gradient

Courtesy J. Campbell

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Diffusion Tensoriffusion Tensor‐ Single fiber orientation

‐ Outputs– Eigenvectors and eigenvalues

e1λ1

e2λ2e λ

– MD: mean diffusivity• WM ~ 0.8 mm2/s

e3λ3

• GM ~ 0.9 mm2/s

– FA : fractional anisotropy• 0 ‐>

• 1 ‐>

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Diffusion Tensor in MINCi l• minctensor.pl

minctensor.pl ‐mask anat‐n3‐bet_mask.mnc dwi‐reg‐with‐t1.mnc ‐outputdir /tmp/

RGB: color‐coded b dire tion and

MDFAby direction and weighted by FA

e1x e1y e1z ; e2x e2y e2z ; e3x e3y e3z

…

λ1, λ2, λ3 22

How to check everything is ok

minc3Dvis• use Display to draw an ROIuse Display to draw an ROI• minc3Dvis ‐vector dwi‐reg‐with‐t1_e1x.mnc dwi‐reg‐with‐t1_e1y.mnc dwi‐reg‐with‐t1_e1z.mnc ‐vectormask roi.mnc• Make sure the principal eigenvector of the diffusion t i i ti i th t d di titensor is pointing in the expected direction

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Spherical DeconvolutionSpherical Deconvolution• mincDeconvolve

Model the diffusion signal as the convolution of a single fiber‐Model the diffusion signal as the convolution of a single fiber response function with the fiber orientations in that voxel.

‐ Can reconstruct multiple fiber orientations‐ Have to a pick a region of the brain with a single fiber

from Tournier et al 2004

response function (e.g. CC)

mincDeconvolve –DWIs dwi‐reg‐with‐t1.mnc ‐mask anat‐n3‐bet_mask.mnc ‐o deconvolve.mnc

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How to look at deconvolution results

• can’t display whole brain at once, too intensive for the visualizationintensive for the visualization

• draw an ROI in Display, then use minc3Dvis

minc3Dvis ‐ODF deconvolve.mnc ‐ODFmask us_roi.mnc –vector deconvolve.mnc ‐vectormaskus_roi.mnc

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Tractography• PDD tracking (FACT): Follow the principal diffusion direction computed with the tensordiffusion direction computed with the tensor

• Maxima: Follow the maxima of the ODF computed with spherical deconvolutioncomputed with spherical deconvolution

• mincFibretrack Input Options• rois: multiple, exclusion, textrois: multiple, exclusion, text• termination: curvature, threshold (FA or other), maximum lengthb f d f i

Jiang et al. 2006

• brute force, seed frequency, step size

• mincFibretrack Output optionsbi ti it• binary connectivity map

• tracts (as streamlines) carefully26

T t d li tiTract delineation

tractographyconstraints:

Calamante et al. NeuroImage 2010.

A: inclusion or stopping mask based on thresholding a scalar map, e.g., fractionalconstraints: thresholding a scalar map, e.g., fractional anisotropy (FA)




B: tract-delineating regions of interest (ROIs): constraints:




B: tract-delineating regions of interest (ROIs):- may be intermediate points or expected end pointsconstraints: may be intermediate points or expected end points-“way points” or “obligatory passages”




C: exclusion masksconstraints:




D: curvature constraints constraints:




D: curvature constraints E: other inclusion or exclusion criteria, e.g.,constraints: E: other inclusion or exclusion criteria, e.g., tract length

Tractography optionsTractography options• Seed region (ROI) selection

input exclusion maskinput 2 separate ROI files input 1 ROI file

Jiang et al. 2006

‐ can be drawn in Display or a list of points in a text file 37

Tractography options• Brute force (careful with –tracts option)

Single tracking

B t f

• Seed frequency: 1 27 per voxel (1 ith b t f )

Brute‐force

Jiang et al. 2006

• Seed frequency: 1‐27 per voxel (1 with brute force)• Step size : usually same as voxel sampling ( d d f b t f )(recommended for brute force)

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Tractography optionsTractography options

• Termination criteria– threshold (e.g. only track where FA > 0.2)– curvature (e.g. don’t allow curvature of more than 70deg)– maximum tract length (in mm)maximum tract length (in mm)

• Tracking options– brute force– seed frequency– step size

• OutputsOutputs– connectivity map: 1 where tracking went, 0 otherwise– tracts: streamlines

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Tractography (single vs brute force)Tractography (single vs brute force)

mincFibreTrack ‐algorithm PDD ‐namebase dwi‐reg‐with‐t1 ‐mask dwi‐reg‐with‐t1_FA.mnc ‐gt 0.2 ‐roi us_roi.mnc ‐curvature_constraint_angle 70 ‐conn conn_FACT.mnc ‐tracts tracts_FACT.vtk –brute_force

Let’s look at the tractography result:

minc3Dvis ‐3DImage anat.mnc ‐rois us_another_roi.mnc 0.5 1.5 –roicolour 1 1 1 –tracts tracts FACT.vtk ‐tubes_

Let’s look at the difference in terms of the connectivity map:

minc3Dvis 3DImage anat mnc rois us another roi mnc 0 5 1 5minc3Dvis ‐3DImage anat.mnc ‐rois us_another_roi.mnc 0.5 1.5 conn_FACT_another_bf.mnc 0.5 1.5 conn_FACT_another.mnc 0.5 1.5

‐roiopacity 1 0.3 0.3

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Tractography (tensor vs maxima of ODF)Tractography (tensor vs maxima of ODF)

• mincFibreTrack ‐algorithm PDD ‐namebase dwi‐reg‐with‐t1 ‐mask dwi‐reg‐with‐t1_FA.mnc ‐gt 0.2 ‐roi us_roi.mnc ‐curvature_constraint_angle70 ‐conn conn_FACT.mnc ‐tracts tracts_FACT.vtk –brute_force

• mincFibreTrack ‐algorithm Maxima ‐ODF deconvolve.mnc –mask dwi‐reg‐with‐t1_FA.mnc ‐gt 0.2 ‐roi us_roi.mnc ‐curvature_constraint_angle 70 ‐conn conn_max.mnc ‐tracts tracts_max.vtk –brute_force

Let’s look at the difference:

minc3Dvis ‐3DImage anat.mnc ‐tracts tracts_deconv.vtk tracts_FACT.vtk ‐tractcolour 0.2 0.3 0.5 0.2 0.6 0 ‐rois us another roi.mnc 0.5 1.5 ‐_ _roicolour 1 0 0

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VisualizationVisualization

• minc3Dvisminc3Dvis– 3D Image

vectors– vectors

– fiber ODFs and maxima

f ( i th i f )– surfaces (rois or any other iso surface)

– tracts colored by direction (or single color), lines or tubesor tubes

– map scalars onto tracts

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Mapping Scalars onto TractsMapping Scalars onto Tracts

• minc3Dvis ‐tracts tracts_deconv.vtk ‐rois us_roi.mnc 0.5 1.5 ‐roicolour 0 0 1 ‐scalars dti‐reg‐to‐t1‐mask_FA.mnc ‐scalar_range 0 1

J. Campbell

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InstallationInstallation

• Can be installedCan be installed– BIC machines (easiest)– Linux– MAC (WIP)

• Installation and usage instructions here:Installation and usage instructions here:http://www.bic.mni.mcgill.ca/~ilana/diffusion/diffusion_tools.html

• Soon to be part of standard MINC package• Questions?

– [email protected] g

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diffusion processing using minc - mcconnell brain …€¦ · · 2014-02-25• what can i do with...

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