Transcript National Alliance for Medical Image Computing Diffusion Tensor Imaging Sonia Pujol, Ph.D. Randy Gollub, M.D., Ph.D. Surgical Planning Laboratory -1- http://www.slicer.org Brigham and Women’s Hospital.

National Alliance for Medical Image Computing
Diffusion Tensor Imaging
Sonia Pujol, Ph.D.
Randy Gollub, M.D., Ph.D.
Surgical Planning Laboratory
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http://www.slicer.org
Brigham and Women’s Hospital
Acknowledgments
National Alliance for Medical Image Computing
NIH U54EB005149
Neuroimage Analysis Center
NIH P41RR013218
Laboratory of Mathematics in Imaging,
Brigham and Women’s Hospital
Thanks to Carl-Fredrik Westin, Lauren O’Donnell, Raul San
Jose Estepar, Carlos Isorna, Maxime Boucher, Matthan Caan
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Material
• Slicer 2.6
http://www.na-mic.org/Wiki/index.php/Slicer:Slicer2.6_Getting_Started
• DTI Sample Data Set
Dwi-dicom.zip
SlicerSampleDTI.zip
http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101
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Goal of this tutorial
Guiding you step-by-step
through the DWI data
analysis capabilities of
Slicer, including generation
of tensors, calculation of
scalar metrics and
tractography tools.
Final result of the tutorial
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Slicer DT-MRI Module
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Overview
•
•
•
•
•
Part 1: Loading and Converting DTI Data
Part 2: Computing Fractional Anisotropy
Part 3: Generating Fiber Tracts
Part 4: Selective Seeding
Part 5: Clustering
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Diffusion Weighted Imaging (DWI)
Diffusion
Sensitizing
Gradients
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Diffusion
Weighted
Images
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Diffusion Weighted Imaging (DWI)
Example: Correlation between the orientation of
the 11th gradient and the signal intensity in the
Splenium of the Corpus Callosum
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Loading DTI Data
Slicer can load
• DWI Volumes …………………….
• Tensors……………………………
• Tracts………………………………
• DTI Scenes ……………………….
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DWI Training Dataset 1
• 2 Baselines and 12 Gradients
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DWI Training Dataset 2
• 1 Baseline and 6 Gradients
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Loading the DWI Training Dataset 1
Click on Add Volume to load
the Dicom-DWI training dataset
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Loading DWI data
The Props Panel of the module
Volumes appears.
Select Nrrd Reader in
the Properties field
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Loading DWI data
Click on Browse and load the
file namic01-dwi.nhdr in
the directory Dwi-dicom
Check that the path to the
file is correct. If needed,
manually enter it
Click on
Apply
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Loading DWI data
Slicer loads the Nrrd
DWI dataset
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Loading DWI data
Left-click on the button
Or, and select the
orientation Slices
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Loading DWI data
The anatomical
slices are now
aligned with the
sampling grid
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Loading DWI data
Change the
FOV to 2000
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Loading DWI data
The sagittal and
coronal viewers
display the 14 MR
volumes: 2 baselines
and 12 diffusion
weighted volumes
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Loading DWI data
Left-Click on the V
button to display the
axial and sagittal slices
inside the viewer.
Use the axial slider
to slice trough the
baselines and
diffusion weighted
MR volumes.
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DT-MRI Module
Select Modules in the
Main Menu
Select
VisualisationDTMRI
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DT-MRI Module
The panel Input of the
DTMRI module appears
Click on the tab Conv
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DT-MRI Module
The panel Conv of the
DTMRI module appears
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Converting DWI data to tensors
Select the Input Volume
namic01-dwi.nhdr and
click on ConvertVolume
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Converting DWI data to tensors
bgˆi T Dgˆ i
Si  S0 e
(Stejskal and Tanner 1965, Basser 1994 )
Dxx
Dyx
Dzx
Dxy
Dyy
Dzy
Dxz
Dyz
Dzz
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{Si} represent the signal
intensities in presence of
the gradients gi
Slicer computes the
symmetric 3x3 tensor
matrix D for each voxel
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Converting DWI data to tensors
• At the end of the calculation, Slicer displays
the average of all diffusion weighted images
(_AvGradient) and the baseline volume
(_Baseline).
• The tensors volume is available from the
DTMRI module for additional calculations.
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Converting DWI data to tensors
Slicer displays the
anatomical views of
the average of all 12
diffusion weighted
images (average
over all gradient
directions)
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Adjusting Window Level
Click on the Module Volumes
and select the tab Display
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Adjusting Window Level
Select the Active Volume
namic01-dwi-nhdr_AvGradient
Use the sliders Win and Lev to
adjust the Window level
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Adjusting Window Level
Slicer displays the
anatomical views of
the average of all 12
diffusion weighted
images (average over
all gradient directions)
Inspect the anatomy
using the axial,
sagittal and coronal
sliders.
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Converting DWI data to tensors
Left-click on Bg and
select the volume
namic01-dwi
nhdr_Baseline
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Converting DWI data to tensors
Slicer displays the
baseline (T2) images.
Browse the baseline
images to check if the
anatomy is correct
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Converting DWI data to tensors
Click on the module Data
Slicer displays the list
of DTI volumes
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Converting DWI data to tensors
Select File Close
in the Main Menu to
clear the scene
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Loading the DWI Training Dataset 2
Click on Add Volume
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Loading Slicer Sample DWI data
Select the Props Panel
Use the Basic Reader
Click on Browse
Navigate to the folder containing
the tutorial data
Select the first file D.001
Click Open
Select ImageHeaders: Auto
Click Apply
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Loading Slicer Sample DWI data
The DWI images
appear in the Viewer
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Loading Slicer Sample DWI data
Observe the axial
slices using the slider
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Loading Slicer Sample DWI data
A sequence of white stripes appears in the diffusion
weighted images. They correspond to intersections with
the baseline images in Slicer Axial/Sagittal/Coronal
(AxiSagCor) slice mode.
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Loading Slicer Sample DWI data
Superior
In Axial/Sagittal/Coronal mode the
slices planes, which are aligned
with the RAS coordinates, are
cutting through the DWI volume
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Loading Slicer Sample DWI data
Left click on Or and
select the orientation
Slices in the Menu
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Loading Slicer Sample DWI data
The original slices
appear in the Viewer
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Loading Slicer Sample DWI data
Superior
In AxiSlice/SagiSlice/CorSlice
mode the slices are aligned
with the DWI volume
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Inferior
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Loading Slicer Sample DWI data
Notice that the viewer displays the stack of S0
and diffusion weighted images {Si}
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Loading Slicer Sample DWI data
Browse the original axial
slices corresponding to
the baseline (S0) image.
Example: display the slice 209
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Loading Slicer Sample DWI data
Adjust the window level and observe the
baseline image (S0)
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Adjusting Image Window Level
Select the Volumes module
Select the Display panel
Select the volume D
Adjust Window and Level
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Loading Slicer Sample DWI data
Observe the baseline image (S0)
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Loading Slicer Sample DWI data
Notice that the image intensity for
each of the six gradient orientations
is much lower than the S0 image.
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DT-MRI Module
Select Modules in the
Main Menu
Select
VisualizationDTMRI
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DT-MRI Module
Select the module DTMRI
Select the Conversion
Panel: Conv
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Convert DWI data to tensors
Select InputVolume D
Select Protocol
BWH_6g.1bSlice
Click on Prop to display the
parameters of the acquisition
protocol
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Acquisition protocol
Slicer displays the parameters of the acquisition protocol used
to acquire the DTI Sample Data BWH_6g.1bSlice at
Brigham and Women’s Hospital, corresponding to:
• n=6 gradients
• Gradient directions = {0.707107 0.707107 0} ,
{0 0.707107 0.707107 }, {0.707107 0 0.707107 },
{0 0.707107 -0.707107 }, {0.707107 -0.707107 0},
{-0.707107 0 0.707107}
• Gradient order: Slice interleaved
• b=1 baseline
• B-value = 1000
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Convert DWI data to tensors
Click on Convert Volume
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Converting DWI data to tensors
Slicer displays the anatomical views of the
average of all 6 diffusion weighted images
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Converting DWI data to tensors
Left Click on the button Bg and select the
volume D_Baseline
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Converting DWI data to tensors
Observe the volume D_Baseline
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Overview
•
•
•
•
•
Part 1: Loading and Converting DTI Data
Part 2: Computing Fractional Anisotropy
Part 3: Generating Fiber Tracts
Part 4: Selective Seeding
Part 5: Clustering
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Computing Fractional Anisotropy
In the DT-MRI module,
click on More to navigate
in the different panels
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Computing Fractional Anisotropy
Select the panel Scalars
Browse the menu Create Volume to
see the list of calculations that Slicer
can perform on the D_Tensor dataset.
Select Fractional Anisotropy
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Computing Fractional Anisotropy
• The Fractional Anisotropy (FA) is a
measure of the diffusion anisotropy that can be
calculated without explicitly computing any
eigenvalue:
1 trace( D) I
D

3
3
FA 
D
2
where |D| and trace(D) are the norm and trace of
the Diffusion Tensor.
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Computing Fractional Anisotropy
The Fractional Anisotropy
Panel appears
Select the Region of Interest
ROI:Mask
The Scale Factor is set by default
to 1000, because the standard
range of FA values (0.0 to 1.0) is not
compatible with Slicer
Click on Apply
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Computing Fractional Anisotropy
The Viewer displays
the FA volume.
Move the mouse in the
slices to see FA values
for each voxel.
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Computing Fractional Anisotropy
•Note
high
FA
values over large
tracts such as the
corpus callosum
•Note
low
FA
values over gray
matter
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Fractional Anisotropy Statistics
Goal
Measure Fractional
Anisotropy Statistics in a
Region of Interest (ROI)
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ROI Drawing
Select the Editor module
in the main Menu.
Select the Volumes panel.
Select the Original Grayscale
FractionalAnisotropy_D
Select the Working Labelmap
NEW and keep the Default
Descriptive Name Working.
Click on Start Editing
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ROI Drawing
Select the Effects panel
Left click on Draw in the
Effects Menu
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ROI Drawing
The Draw Panel of the
Editor Module appears
Left-click on Output,
and select the color label
#2 (pink)
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ROI Drawing
Draw the contour of the
Corpus Callosum with the
mouse in the sagittal slice
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ROI Drawing
Click on
Apply in
the Editor
Module
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Measure FA Statistics in ROI
Select Modules Measurement
VolumeMath in the Main Menu
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MaskStat
Select MaskStat
The MaskStat functionality
uses the labelmap as a
mask over the FA volume,
and calculates “stats” on
the region contained under
the labelmap.
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MaskStat
Set Volume to Mask to
FractionalAnisotropy_D_Tensor
Set LabelMap to Working
Set Masked Output to Create New
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MaskStat
Click on Mask, select the same color as
your labelmap
Click on Browse to select a directory to
place the output text file and enter the file
name
FractionalAnisotropy_D_Tensor_hist.txt
Click on Run
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MaskStat Result
A window shows the statistics (multiplied by the
Scale Factor): minimal, maximal, mean and standard
deviation of the FA values.
The results have been saved in the file
FractionalAnisotropy_D_Tensor_hist.txt
written on the disk.
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Save FA volume and ROI
Select the module Editor
Select the panel Volumes
and click on Save
Enter a FilenamePrefix and
select the format NRRD(.nhdr)
Click on Save
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Save FA volume and ROI
Slicer generates a Nrrd
header file (FA.nhdr), and a
raw compressed file
(FA.raw.gz).
Save the Working volume
containing the label map using
the same process.
(Slicer Training #7: Saving data.)
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Overview
•
•
•
•
•
Part 1: Loading and Converting DTI Data
Part 2: Computing Fractional Anisotropy
Part 3: Generating Fiber Tracts
Part 4: Selective Seeding
Part 5: Clustering
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Tractography Panel
Select the DTMRI
module and click on
the Panel More
Select the Panel
Tracts inside the
DTMRI module
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Tractography Panel
Select the Tab Settings
Left-click on Color
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Tractography Panel
A Color selection
panel appears
Select a new color
for the tracts
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Create a single tract
Position the mouse
on a point inside the
Corpus Callosum,
and hit the s key.
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Create a single tract
A tract appears in
the 3D Viewer.
Drag right mouse
button down in the 3D
Viewer to zoom in.
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Create a single tract
The 3D window
shows a closer
view of the tract.
Click on the V
buttons
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Create a single tract
Slicer displays the
slices in the 3D
window.
Drag left mouse
button in the 3D
Viewer to rotate
the volume,
Drag right mouse
button to zoom in,
until you get to a
convenient view.
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Generate Multiple Tracts
Position the mouse
on different points in
the corpus callosum
and hit the s key.
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Generate Multiple Tracts
The tracts that
correspond to the
visited points
appear in the 3D
Viewer.
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Generate Multiple Tracts
Hold down the s key
and move the mouse
in the corpus
callosum
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Generate Multiple Tracts
Multiple tracts are
generated for each
point visited by the
mouse.
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ROI Seeding
Slicer has functionalities to generate tracts
from a pre-defined Region Of Interest (ROI).
Training dataset ROI:
• ROI1: Temporal stem, between frontal and temporal
lobes
• ROI2: Posterior temporal lobe
• ROI3: Splenium of the corpus callosum
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ROI Drawing
Select the Editor module
in the main Menu.
Select the Volumes panel
and click Setup
Select the Original Grayscale
FractionalAnisotropy_D_Tensor
Select the Labelmap Working.
Click on Start Editing
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ROI Drawing
Select the Effects panel
Left click on Draw in the
Effects Menu
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ROI Drawing
Select the color label #7
in the module Editor
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ROI1: Temporal stem
Select View1x512
COR in the Main Menu
Draw a region of interest
in the Temporal stem
(slice #156)
Click on Apply in the
module Editor
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ROI Drawing
Select the color label #10 in
the module Editor
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ROI2: Posterior temporal lobe
Select View1x512
COR in the Main Menu.
Draw a region of interest in
the Posterior Temporal
Lobe (slice #128)
Click on Apply in the
module Editor
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ROI Drawing
Select the color label #5 in
the module Editor
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ROI3: Splenium
Select View1x512
SAG in the Main Menu.
Draw a region of interest
in the Splenium of the
Corpus Callosum
(slice #131)
Click on Apply in the
module Editor.
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ROI Seeding
Come back to the DTMRI module
and select the panel Tracts.
Click on the tab Seed and select
the SeedROI Working
Select the color label #7
corresponding to the ROI1
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ROI Seeding
Click on Seed Tracts
A warning message appears,
Click Yes if you are ready to
process the data.
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ROI1 Seeding
Slicer displays
the tracts
from ROI1
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ROI Seeding
Select the color label #10
corresponding to the ROI2
Click on Seed Tracts
A warning message appears,
Click Yes if you are ready to
process the data.
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ROI2 Seeding
Slicer displays
the tracts
from ROI2
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ROI Seeding
Select the color label #5
corresponding to the ROI3
Click on Seed Tracts
A warning message appears,
Click Yes if you are ready to
process the data.
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ROI3 Seeding
Slicer displays
the tracts
from ROI3
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Selective Seeding
Slicer displays the
tracts from the 3
segmented ROIs
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Overview
•
•
•
•
•
Part 1: Loading and Converting DTI Data
Part 2: Computing Fractional Anisotropy
Part 3: Generating Fiber Tracts
Part 4: Selective Seeding
Part 5: Clustering
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Selective Seeding
AND and NOT operators can be used to
select tracts generated from different
Regions Of Interest.
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Selective Seeding
Slicer displays the
tracts from the 3
segmented ROIs
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AND/NOT operators: example
• ROI1: Temporal stem, between frontal and
temporal lobes
• ROI2: Posterior temporal lobe
• ROI3: Splenium of the corpus callosum
• Target: Inferior Occipito-frontal Fasciculus from
the Frontal lobe to Occipital Lobe, through
Temporal lobe
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Selective Seeding
Click on the tab Select in the
Panel Tract
Select the ROI Labelmap
Working
Enter the labels of the ROI1
(label #7) and ROI2 (label #10) in
the list of labels called AND.
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Selective Seeding
Enter the label of the ROI3
(label #5) in the list of labels
called NOT.
Click on Find Tracts through ‘ROI’
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Selective Seeding
Slicer displays the
resulting tracts of the
Inferior Occipitofrontal Fasciculus
in red.
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Selective Seeding
The tracts that were
not selected appear
transparent.
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Deleting Tracts
Select the tab Display
and click on Delete to
delete all the tracts and
clear the scene.
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Deleting Tracts
Slicer removes all the
tracts generated from
the ROIs.
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Overview
•
•
•
•
•
Part 1: Loading and Converting DTI Data
Part 2: Computing Fractional Anisotropy
Part 3: Generating Fiber Tracts
Part 4: Selective Seeding
Part 5: Clustering
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ROI Seeding
Select Seed in the
Tracts Panel
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ROI Seeding
Select the ROI Working
Select the color label of the ROI (#2)
Click on Seed Tracts
A warning message appears,
Click Yes if you are ready to
process the data.
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ROI Seeding
The resulting
tracts appear
in the 3D Viewer.
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Corpus Callosum Tracts
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Clustering Method
Estimation of a
similarity measurement
for all pairs of tracts
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Similarity Measurement
The similarity measurement is
based on the mean closest
point distance between the
tracts.
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Clustering Method*
High dimensional
clustering space
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Clustering Method*
Cluster-colored
tracts
(*) White Matter Tract Clustering and Correspondence in Populations O’Donnel L, Westin C-F
Medical Image Computing and Computer-Assisted Interventions (MICCAI2005)
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Tract Clustering Algorithm
Click on More and
select the panel TC
In this tutorial example, we
cluster the fiber tracts
generated from the ROI in
the Corpus Callosum
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Tract Clustering Algorithm
The Number of Clusters is the
number of bundles expected.
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Tract Clustering Algorithm
N is the Sampling resolution
along the fibers
In this example, N=8
The default settings of
the algorithm is N=15
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Tract Clustering Algorithm
The ShapeFeature corresponds
to the Similarity Measurement
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Tract Clustering Algorithm
The ShapeFeature corresponds
to the Similarity Measurement
The default setting of
the algorithm is
MeanClosestPoint
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Tract Clustering Algorithm
Click on Cluster to
start the algorithm
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Tract Clustering Result
Slicer displays the result
of the tract clustering in
the Corpus Callosum.
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Tract Clustering Result
Slicer displays the result
of the tract clustering in
the Corpus Callosum.
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Conclusion
• Intuitive interface for manual and automatic
tracts generation
• Enhanced visualization of the anatomy by
fusion of tracts and DWI images
• Open-source environment
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