SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
Introduction
A method for advanced imaging of epilepsy patients has been developed with Analyze at the Mayo
Foundation which uses a combination of SPECT and MRI imaging for improved diagnosis of areas of
regional activation in the brain during seizure. This technique, called SISCOM, for Subtraction Ictal SPECT
COregistered to MRI, takes advantage of the transient focal increase in cerebral blood flow in the region of
seizure focus to image and statistically identify the part of the brain involved in the seizure activity. SPECT
imaging techniques, using such radiotracers as 99Tcm-HMPAO and 99Tcm-EDC, have demonstrated ability to
map ictal and interictal blood flow patterns, providing the potential for using these in combination to localize
the seizure focus. Interictally, many epilepsy patients will exhibit a region of hypoperfusion in the region of
the seizure focus. Ictally, these radiotracers, with maximum uptake within 30-60 seconds, become trapped in
the brain, producing a ‘snap shot’ of the ictal cerebral perfusion pattern that can be imaged up to 4 hours
later. The ictal and interictal SPECT scans thus provide complimentary information which, when
appropriately processed with Analyze, can be used to determine the region of increased activation during
seizure, particularly important in non-lesional epilepsy cases.
Given the relatively poor spatial resolution and structural detail of the ictal and interictal SPECT images,
precise anatomic localization of the site of increased activation during seizure can be difficult. Coregistration with a volumetric MRI of the same patient provides fusion of the critical functional information
from the SPECT scans with the structural detail of the MRI, an important synergistic visualization which
allows direct functional to structural correlation and analysis. The fusion of the subtraction SPECT images
with the MRI also permits the visual validation of the imaged region of activation through correlation with
abnormal pathology and/or known functional areas of the brain and the symptoms exhibited by the patient
during seizure. The fused SISCOM images may further be used to help guide the resection of brain tissue
during a stereotaxic, image-guided neurosurgical procedure and to evaluate the results of treatment.
The SISCOM technique was initially developed using combinations of several Analyze modules in a
sequence which produced the desired results, demonstrating the potential for Analyze to be used very
effectively for prototyping solutions to specific applications. Given this prototypical solution, a new
SISCOM module has been developed which accomplishes the same tasks as in the manual SISCOM
procedure, but within the context of a single Analyze module optimized to provide the user with a facile
mechanism applying the SISCOM technique. This includes a specific user interface for control of the
parameters necessary to accomplish the SISCOM processing, with automatic processing and analysis of
many of the steps that required significant manual intervention in the original sequence of steps. The new
SISCOM module can be found in the Process->Fusion menu.
This tutorial provides assistance for both of these SISCOM processing methods: manual and automated. It
is perhaps in the user’s best interest to go through the entire manual SISCOM procedure before using the new
SISCOM module for two reasons. First, following the manual processing steps provides valuable insights
into the algorithm used to create the fused SISCOM images, providing a fundamental understanding of the
meaning of the activation regions and their association with structural anatomy. Second, the manual steps in
the process nicely demonstrates the mechanisms by which current Analyze modules can be used to explore
the implemenation and realization of a particular application processing task. For these reasons, the manual
method to accomplish the SISCOM task is presented first in this tutorial.
1
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
Image Data
The SISCOM technique requires acquisition of ictal (during the seizure) and
interictal (resting, or between seizures) SPECT images and a MRI volume spanning
the entire brain. Examples of such scans are provided in the tutorial demonstration
data in AnalyzeAVW image file format. These image files include:
SISCOM_Ictal_SPECT.avw:
Ictal SPECT volume image acquired following
radiotracer injection during seizure. The volume is 64 x 64 x 45, acquired
coronally, and has an isotropic voxel resolution of 4.1 x 4.1 x 4.1 mm3. A ‘hot
body’ colormap is associated with the image to depict levels of radiotracer activity
(increasing black→dark red→orange→white). See Figure 1.
Figure 1 Ictal SPECT
coronal slice 22.
SISCOM_Interictal_SPECT.avw: Interictal SPECT volume image
acquired at rest (between seizures). The volume is 64 x 64 x 45, acquired
coronally, and has an isotropic voxel resolution of 4.1 x 4.1 x 4.1 mm3. A
colormap is also associated with this volume image (Figure 2).
SISCOM_MRI.avw: An isotropic MRI
volume image acquired at rest (between
seizures). The original volume image was
acquired anisotropically in a coronaloblique orientation, with a volume size of
Figure 2 Interictal
256 x 256 x 124 and voxel resolution of
SPECT coronal slice 22.
0.859 x 0.859 x 1.6 mm3. This anisotropic
volume was then interpolated using the Resize option in the Load As
module to an isotropic size of 256 x 256 x 231 and a resulting voxel
resolution of 0.859 x 0.859 x 0.859 mm3 (Figure 3).
(Note: The interpolation of the MRI volume to create an isotropic volume
image with cubic voxels is not strictly necessary for the SISCOM
Figure 3 MRI coronal-oblique
technique. In many cases, it is desirable to create 3-D visualizations of the
slice 115.
fused subtraction SPECT images and the MRI volume image to identify the
area of activation relative to the cortical anatomy. To do this, the
anisotropic MRI volume image needs to be interpolated to be cubic as some point during the SISCOM
procedure. Doing this up front provides for the most convenient use of the MRI volume data during the
entire SISCOM process. For more information on interpolating anisotropic data to be cubic, please see the
Getting Started with Analyze tutorial.)
The SISCOM Algorithm
The SISCOM procedure consists of the following parts:
1) Determine thresholds for ictal and interictal SPECT volume images to segment cerebral
voxels for registration and analysis
2) Register the ictal and interictal SPECT volume images.
3) Segment the brain from the ictal and co-registered interictal volumes images and create a
combined binary mask for the whole brain.
4) Normalize and subtract the ictal and interictal SPECT images.
5) Determine the statistical regions of activation from the subtraction SPECT image.
6) Register the SPECT to the MRI volume image and create a fused representation of
regions of focal activation from the SPECT and the structural information from the MRI.
2
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
The Manual SISCOM Procedure
The following provides a step-by-step tutorial on how to accomplish the SISCOM processing technique with
the demonstration image data provided using the manual method via several Analyze modules.
Threshold Determination for Brain Voxel Selection
The SISCOM procedure begins by determining thresholds for the ictal and interictal SPECT volume images
that will segment the voxels corresponding to activity in the brain vs. extracerebral activity and background
noise. These thresholds will be used in both the segmentation process prior to analysis and during the
registration of the two SPECT volumes.
First, load the two SPECT volume images:
1.
Use the Load module to load the SISCOM_Ictal_SPECT.avw volume image from the
tutorial image data directory ($BIR/images/TutorialData).
2.
Repeat this step to load the SISCOM_Interictal_SPECT.avw volume image.
Next, interactively determine the threshold level for the ictal SPECT volume image which selects those
voxels that correspond to activity within the bounds of the brain (given limitations due to image acquisition,
including spatial resolution and partial volume effect):
3.
Select the
SISCOM_Ictal_SPECT
volume image in the main
Analyze workspace.
4.
Invoke the Multiplanar
Sections module under the
Display submenu (or use the
powerbar icon for Multiplanar
Sections – second from left by
default).
5.
Review the ictal SPECT scan
data by displaying all of the 45
images using the Display
Section(s) option under the
Generate submenu (or the
powerbar icon for Display
Sections on the very left of the
powerbar – a traffic light
symbol with the lower Go light
Figure 4 Ictal SPECT volume displayed in Multiplanar Sections.
illuminated). The images
demonstrate the ‘hot body’
colormap to indicate levels of activity in the SPECT scans (Figure 4).
6.
In order to see the effect of the threshold operation, increase the size of the displayed images
using the Size option in the View submenu. Select the Double size display for the
images.
7.
Choose a particular image out of the set of 45 upon which to interactively specify a
threshold to select the voxels corresponding to the brain. In this case, image 22
provides a good representation of the brain structure and different activity levels
from which these voxels can be thresholded. Select the Slice option under the
Generate submenu and, using the slider next to the Slice control, set the current
slice to image 22 (Figure 5).
Figure 5 Slice window.
3
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
8.
To interactively threshold this image, select the Intensities option under the View submenu.
Change the Type from the current Colormap setting to
Threshold (Figure 6). A completely white image should
appear where image 22 was last displayed. The default
min and max settings for the threshold range are the full
range of the data, 0 and 255 in this case, causing all
voxels in the image to be considered as part of the
thresholded set (and thus a white image).
9.
Interactively change the Minimum value by moving the
left end of the threshold slider. As this is moved, the
thresholded image will change to reflect the new set of
voxels that are within the current threshold range. The
threshold selection is a matter of judgment based on the
effect of changing the threshold minimum interactively
Figure 6 The Intensity window is used to
while reviewing the change to the image. The purpose
interactively threshold the volume.
here is to estimate a reasonable threshold that eliminates
the extracerebral activity and background noise and yet
maintains voxels that appear to be part of activity from within the brain tissue itself. For this
particular SISCOM_Ictal_SPECT volume image, a minimum threshold value around 60 is
appropriate.
10. Record this minimum threshold value for the SISCOM_Ictal_SPECT volume image, as it
will be used later for registration and analysis.
11. Repeat steps #3 through #10 for the SISCOM_Interictal_SPECT volume image. This
can be done by either selecting the SISCOM_Interictal_SPECT volume image and
invoking a new Multiplanar Sections module, or by simply dragging the
SISCOM_Interictal_SPECT volume image from the Analyze workspace to the currently
running Multiplanar Sections module (currently with the SISCOM_Ictal_SPECT images).
If the latter is done, the display will update to show image 22 from the interictal data, which is
a good slice again for threshold determination. The Intensity Type will switch back to
Colormap, but simply set this to Threshold and determine the minimum for this interictal
volume. In this case, a minimum threshold value around 75 is appropriate. Again, record
this minimum for the SISCOM_Interictal_SPECT volume image for later use.
12. Exit all copies of the Multiplanar Sections module prior to moving on to the next registration
steps (File→ Exit).
Ictal-Interictal SPECT Registration
In order to compare and subtract the ictal and interictal SPECT volume images, the two volumes must be
spatially co-registered. The registration of the ictal and interictal SPECT volumes will be accomplished
using a normalized mutual information-based voxel matching algorithm in Analyze.
To register the ictal and interictal SPECT volumes, complete the following steps:
1.
In the main Analyze workspace, select the SISCOM_Ictal_SPECT volume
image with the left mouse button. Then select the
SISCOM_Interictal_SPECT volume image using the middle mouse
button, resulting in both volumes being selected (border highlight on each).
The order selected will be important, as when the registration module is
invoked, the first selection will be the Base volume and the second selection
will be the Match volume.
2.
Invoke the 3-D Registration module in the Fusion section of the Process submenu.
4
If you do not have a middle
mouse button, hold down
the Control key while using
the left mouse button.
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
3.
To check to make sure the proper volumes are assigned to the Base and Match registration
volumes, turn on the Input/Output Ports option under the File submenu in the 3-D
Registration module. If for some reason the Base Volume is not the
SISCOM_Ictal_SPECT volume image and/or the Match Volume is not
SISCOM_Interictal_SPECT, drag the SISCOM_Ictal_SPECT volume to the Base
Volume port, and drag SISCOM_Interictal_SPECT to the Match Volume port. (Note:
this is a good example of how to use drag-and-drop in Analyze to establish the image volumes
with which a module is actively running – in this case a
module which requires two inputs.)
4.
Under Generate select the Voxel Match (NMI) option
for the normalized mutual information registration
algorithm.
5.
In many mutual information registration cases, it is not
necessary to set a threshold to achieve an accurate
registration. However, in the case of these SPECT
images, significant background noise and reconstruction
artifact exist in the lower value range, which may have
Figure 7 The Voxel Match (NMI) window
been evident during the thresholding done in the
while setting the thresholds.
previous section. In order to optimize the registration,
the thresholds determined in the previous section should
be set as the Minimum Threshold values for the Base and Match volume images in this
registration process. In the window, select the Thresholds check box.
6.
Set the Minimum Base Threshold to 60 (as previously determined for
SISCOM_Ictal_SPECT) and the Minimum Match Threshold to 75 (for
SISCOM_Interictal_SPECT). See Figure 7.
7.
Select the Register button to start the registration process. When the registration is complete,
the transformation matrix that will transform the Match volume (interictal) to the Base volume
(ictal) will appear in a Matrix Tool window.
8.
To visually inspect the accuracy of the registration, the
Cursor Link tool can be used to view the original ictal
images, the transformed interictal images, and a fused
representation of both. Invoke the Cursor Link tool under
the Tools submenu (Figure 8).
9.
To view the images in the Cursor Link tool at a larger
size, select the Size option under the View submenu and
set a larger image display size, i.e., Double, Triple, or
Quadruple.
10. The Section slider in the Cursor Link tool can be used to
page through the co-registered SPECT volume images to
verify that the interictal SPECT is now accurately
registered to the ictal SPECT. The linked cursor can also
be used in the image display windows to compare relevant
structures, like specific anatomic locations or surfaces,
between the ictal base volume and the transformed
interictal match volume.
11. Even though not strictly necessary in this case, it is often
useful to save the transformation matrix for later
application or reference. To do this, use the Save Matrix
option in the Matrix Tool window that appeared at the end
of the registration process. Save the matrix to a file called
Interictal_to_Ictal.mat.
5
Figure 8 The Cursor Link tool with a
base image (ictal), transformed match
image (interictal), and a fused image.
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
12. The interictal volume image should now be transformed into a new output volume image that
is in co-registration with the ictal volume. To do this, select the Transform option under the
Generate submenu. The default option is to Transform Match to Base, which is what is desired
here to transform the registered interictal volume to the base ictal volume.
The Destination should also default to the Analyze Workspace, which will
create the transformed volume in the main Analyze workspace. By
default, the name of the output transformed volume image file is the same
as the file being transformed with an X prepended to the file name, in this
case it should be set to XSISCOM_Interictal_SPECT. Clear this
existing name in the Name field and enter
SISCOM_Interictal_SPECT_TRANS for the new transformed
interictal file name (Figure 9).
13. Select the GO action in the Transform window to apply the
transformation matrix to the match (interictal) volume image and create
the new, co-registered version of the interictal volume image in the main
Analyze workspace (SISCOM_Interictal_SPECT_TRANS).
14. Use the Save As module to save the
SISCOM_Interictal_SPECT_TRANS volume image to a disk file of
the same name.
Figure 9 Transform window.
15. Exit the 3-D Registration module.
Brain Segmentation and Binary Mask Creation for Ictal and Interictal SPECT Images
Preliminary segmentation of the brain in both volumes was accomplished in the first section via thresholding.
The segmentation is further refined here using morphologic processing to generate binary masks for further
analysis of the voxel contents.
To segment out the entire brain from the ictal and interictal
SPECT scans using thresholding and morphologic processing:
1.
Select the SISCOM_Ictal_SPECT volume image in
the Analyze workspace and invoke the Morphology
module in the Segment sections of the Process
submenu.
2.
In the Morphology module, invoke the Step Editor
under the Generate submenu (or select the Step Editor
powerbar icon on the left of the powerbar). See Figure
10.
3.
Select the Threshold operation and set the threshold
minimum level to the previously determined minimum
value of 60. Select the Threshold Volume button on
the bottom of the Threshold window to apply the current
threshold. When asked about which volume to process,
select the Change a Copy of the Loaded Volume option.
This will create a new binary volume image called
SISCOM_Ictal_SPECT0 in the main Analyze
workspace.
4.
In the Morphology Step Editor, select the Fill Holes
option. This option is used to fill the interior holes in the
brain, producing a solid binary for the whole brain.
Figure 10 The Morphology Step Editor
after the first Fill Holes operation.
6
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
5.
In the Fill Holes window, select a Dimension of 2D (with default Orientation of Transverse
and Connectivity of 4) and fill the holes through the 2-D transverse sections by selecting the
Fill Volume option.
6.
Once again, select the Fill Holes option to add another step, set the 2D Dimension option, but
now select the Coronal Orientation for this step. Select Fill Volume to complete this step.
7.
Repeat step #6, but select the Sagittal Orientation for this step (Figure 11).
8.
Finally, repeat step #6 one more time, but select the Transverse
Orientation again. This will result in 5 completed steps in the
Step Editor – a Threshold step followed by 4 Fill steps. Doing
2D fill hole operations in each of the orthogonal directions
followed by one more pass through the first orthogonal
direction (transverse in this case) will often fill all interior holes
and create a solid binary object, even if the holes are open to
the outside in 3D (causing a 3D fill holes to not work).
9.
Select the Display Current Section(s) option at the bottom of
the Step Editor window to review the processed images.
Figure 11 Fill Holes window.
10. Exit the Morphology module.
11. Select the SISCOM_Interictal_SPECT_TRANS volume image in the Analyze workspace
and repeat steps #1 - #10 to similarly process and segment the brain from the transformed
interictal SPECT volume image. Use the previously determined threshold minimum of 75 for
the interictal images. This will create a new binary volume image called
SISCOM_Interictal_SPECT_TRANS0.
12. Before saving these new binary volume images to disk, change the names of the loaded
volume images using the Rename option. To do this, select the SISCOM_Ictal_SPECT0
volume image and use the right-mouse-button menu
in the main Analyze canvas to invoke the Rename
option and change the name to
SISCOM_Ictal_SPECT_BIN. Similarly, select
the SISCOM_Interictal_SPECT_TRANS0
volume image and change its name to
SISCOM_Interictal_SPECT_TRANS_BIN
(Figure 12).
13. Use the Save module to save both of these processed
binary volume images to new files in the
AnalyzeAVW image file format. (See the Getting
Started with Analyze tutorial for information on
saving loaded image files.) If the volume images
have been renamed as given in step #12 prior to
saving to disk, the new name will carry into the Save
dialog, allowing these new names to be used directly
during the save.
Figure 12 Currently loaded volumes - original ictal and
interictal, co-registered interictal, and binary ictal and
co-registered interictal mask volumes.
To further refine the mask of voxels considered to be part of the brain, a combination of the two
segmented binary masks from the ictal and transformed interictal SPECT scans can be created.
Give that these volumes are now in spatial registration, a further refinement of this set of voxels
can be made by selecting only those binary voxels that are both one’s (1’s) in the combination of
the two (an AND function between the two binary volumes). To create this combined, optimized
binary mask, do the following:
14. With no volumes selected in the main Analyze workspace, invoke the Image Algebra module
in the Manipulate section of the Process submenu.
7
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
15. In the Output= field, delete the existing formula and type the following formula to multiply
two volumes together: a*b (hit Enter at the end
of the formula). This will provide iconic
representations for each of the variables in the
formula: Output, a, and b.
16. Drag the SISCOM_Ictal_SPECT_BIN volume
image to the icon for the variable a. Drag the
SISCOM_Interictal_SPECT_TRANS_BIN
volume image to the icon for the variable b
(Figure 13).
17. Select the Output button above the Output icon
and set the following output parameters:
Workspace output (default), Name field set to
SISCOM_SPECT_MASK, Volume and Slice
Figure 13 Image Algebra window with input volumes.
parameters all set to 1 with Slices per Volume set
to Same, Datatype set to Unsigned 8-bit, and
Max/Min Settings set to Calculate. Select Done when these parameters are set (Figure 14).
18. Select the Go button in the main Image Algebra window to start the process. This will result in
a new volume image called SISCOM_SPECT_MASK in the Analyze
workspace, which will be the result of the multiplication of the two
input binary images.
19. Use the Save module to save SISCOM_SPECT_MASK to disk.
Prior to further analysis (normalization and subtraction), this combined
binary mask is used against the two current SPECT volume images, ictal
(SISCOM_Ictal_SPECT) and transformed interictal
(SISCOM_Interictal_SPECT_TRANS), to segment only those
voxels in these volumes that correspond to the combined estimate of
voxels in the brain:
20. If the Image Algebra module is not still running, invoke it and enter
the a*b formula again into the Output= formula field. If it still
running from the previous multiplication of the binary volumes,
simply reuse it.
21. Drag the combined binary volume SISCOM_SPECT_MASK to the
icon for the variable a. Drag the SISCOM_Ictal_SPECT volume
image to the icon for the variable b.
Figure 14 Image Algebra Output
parameters window.
22. Select the Output button above the Output icon
and set the following parameters: Workspace
output (default), Name field set to
SISCOM_Ictal_SPECT_THRESH, Volume
and Slice parameters all set to 1 with Slices per
Volume set to Same, Datatype set to Unsigned 8bit, and Max/Min Settings set to Calculate.
Select Done when these parameters are set.
23. Select the Go button in the main Image Algebra
window to start the process. This will result in a
new volume in the Analyze workspace called
SISCOM_Ictal_SPECT_THRESH, which will
be a result of applying the combined binary mask
Figure 15 Image Algebra module after applying mask.
to the original ictal SPECT volume image,
leaving only those voxels within the masked region with their original SPECT values and
everything else being set to 0 (Figure 15).
8
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
24. Repeat steps #20 - #23 with the transformed interictal
SISCOM_Interictal_SPECT_TRANS volume image. Use the same
SISCOM_SPECT_MASK for the variable a, with the volume image
SISCOM_Interictal_SPECT_TRANS for the variable b. Set the name of the Output file
to SISCOM_Interictal_SPECT_TRANS_THRESH. This will result in a masked version
of the transformed interictal volume image.
25. Exit the Image Algebra module.
Normalization and Subtraction of Ictal-Interictal SPECT Images
The masked ictal and co-registered interictal volume images can now be
sampled to determine the mean activity levels to be used in normalization of
these two scans prior to subtraction. Once normalized, the two volume
images are subtracted and further analysis is done on the subtraction image
to select the statistically significant region of activation.
The analysis of the masked ictal SPECT volume image is done using the
following steps:
1.
Select the SISCOM_Ictal_SPECT_THRESH volume image in
the main Analyze workspace and invoke the Region Of Interest
module under the Measure submenu (or use the ROI power bar
icon).
2.
Select the Size option under the View submenu and change the
display size for the images to Quadruple. Select Done when the
size is set.
3.
Select the Orientation option under the Generate submenu and set
the current orthogonal orientation to Coronal (if this is not already
established as the default). Select Done when this is set.
4.
Select the Slice option under the Generate submenu and set the
Number slider to the total number of images, in this case to the number
45 (note that 45 is the max number of images in the coronal direction).
Make sure the Slice and Increment sliders are set to 1 (default). Select
Done when these are set.
5.
Select the Sample Options item under the Generate submenu. In the
Sample Options window, set the following parameters: Sample Type set
to Selected Region (default), set the Minimum of the Sample Max/Min
slider to 1 (either by moving the left end of the slider or simple
selecting and entering 1 in the Minimum field to the left of the slider),
Range set to Volume, change Summing to On, Auto Reset to On
(default), Sequence Display to On (default), Stat Type to Intensity
(default), Decimal Places to 2 (default), and Log Stats set to Off
(default – although this latter parameter could be used to then record and
save the mean values measured for ictal and interictal volume images as
an option to simply writing them down) See Figure 16.
6.
Once all of the Sample Options have been established, select the Sample
Images button in the main Region Of Interest window and then click on
the image currently displayed in the Region Of Interest image display
canvas (most likely a blank image - #1 in the sequence). The Region Of
Interest module will go through all of the images in the volume to
sample the voxels on each image.
7.
Figure 16 ROI Sample Options
window set to sample ictal activity.
Figure 17 ROI samples from
masked ictal SPECT.
When complete, the ROI Stats window that appears when the Sample Images process begins
will reflect the summed measurements for the sampled parameters. The Mean in Range
9
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
parameter will reflect the mean value of all non-zero voxels throughout the entire masked ictal
volume image (note this is Mean in Range and not simply the Mean value, which also includes
zero-valued voxels). In this case, the Mean in Range should have a value of about 85.24 (if
all thresholds to this point in the tutorial have been set to the specified values). See Figure 17.
Note this Mean in Range value of 85.24 down as the normalization mean to be used with the
ictal SPECT volume image.
To use the sampled Mean in Range value for the ictal SPECT to normalize the volume image to a chosen
normalized mean of 100, do the following:
8.
Invoke the Image Algebra module under the Manipulate section of the Process submenu.
9.
In the Output= field, delete the existing formula and type the following normalization formula
(note that the 85.24 is the Mean in Range value sampled from above): a*(100/85.24)
10. Drag the SISCOM_Ictal_SPECT_THRESH volume image to the icon for the variable a.
11. Select the Output button above the Output icon and
set the following output parameters: Workspace
output (default), Name field set to
SISCOM_Ictal_SPECT_NORM, Volume and
Slice parameters all set to 1 with Slices per Volume
set to Same, Datatype set to Float, and Max/Min
Settings set to Calculate. Select Done when these
parameters are set.
12. Select the Go button in the main Image Algebra
window to start the process. This will result in a new
volume image called
SISCOM_Ictal_SPECT_NORM in the Analyze
workspace, which will be the result of the
multiplication by the normalization factor to create
an ictal SPECT volume normalized to a mean value
of 100 (Figure 18).
Figure 18 Image Algebra normalization of ictal SPECT.
13. Use the Save (or Save As) module to save SISCOM_Ictal_SPECT_NORM to disk.
The analysis and normalization of the masked co-registered interictal SPECT volume image is done in the
same manner as the ictal SPECT:
14. Repeat steps #1 – 7 to sample and determine the Mean in
Range value for the co-registered interictal SPECT
volume image
(SISCOM_Interictal_SPECT_TRANS_THRESH). The
Mean in Range value for this co-registered interictal
SPECT volume image should be around 104.44, which
should be noted for the normalization factor.
15. Repeat steps #8 – 13 to create the normalized coregistered interictal SPECT volume image. Use the
formula: a*(100/104.44) for the normalization
formula in Image Algebra. Set the Output file name to be
SISCOM_Interictal_SPECT_TRANS_NORM and
make sure to save it to disk when complete
(Figure 19).
16. Exit all copies of the Region Of Interest and Image
Algebra modules.
10
Figure 19 Current loaded volumes – new volumes
are combined mask, masked ictal, masked interictal,
normalized ictal, and normalized interictal.
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
Given the normalized ictal SPECT volume image and the normalized co-registered interictal SPECT volume
image, these two can now be subtracted to produce a volume image that depicts ictal changes in regional
activity:
17. With no volumes selected in the main Analyze workspace, invoke the Image Algebra module
(Process→
→Manipulate→
→Image Algebra).
18. In the Output= field, delete the existing formula and
type the following formula to subtract two volume
images: a-b.
19. Drag the SISCOM_Ictal_SPECT_NORM volume
image to the icon for the variable a. Drag the
SISCOM_Interictal_SPECT_TRANS_NORM
volume image to the icon for the variable b.
20. Select the Output button above the Output icon and
set the following output parameters: Workspace
output (default), Name field set to
SISCOM_SPECT_SUBTR, Volume and Slice
parameters all set to 1 with Slices per Volume set to
Figure 20 Ictal - interictal SPECT subtraction in
Same, Datatype set to Float, and Max/Min Settings
Image Algebra.
set to Calculate (Note: in this step, make absolutely
sure the Datatype is set to Float, as the subtraction image will contain both positive and
negative values). Select Done when these parameters are set.
21. Select the Go button in the main Image Algebra window to start the process. This will result in
a new volume image called SISCOM_SPECT_SUBTR in the Analyze workspace, which will
be the subtraction SPECT volume image demonstrating regions of ictal change in activation.
22. Use the Save (or Save As) module to save SISCOM_SPECT_SUBTR to disk.
Determination of Statistical Region of Activation from Subtraction SPECT
The subtraction SPECT image can now be further analyzed to determine the statistically significant region of
increased focal activation. This is accomplished by again determining a mean and standard deviation for the
subtracted levels of activity and then computing and selecting (via thresholding) those levels which are one
or two standard deviations about the mean. The resulting thresholded subtraction image will depict only
those regions of statistically significant increased activity in the ictal vs.
interictal SPECT scans.
The analysis of the subtraction SPECT volume image to check the mean and
determine a standard deviation for the subtracted values is done following
these steps:
1.
Select the SISCOM_SPECT_MASK binary volume image in the
main Analyze workspace and invoke the Save As module (Figure
21).
2.
Select the Orient check box at the top of the Save As window and
change the Output orientation to Transverse. (Note: The purpose
here is to save this binary volume as an Analyze Object Map. Since
all Analyze Object Maps are stored in the transverse orientation, this
binary volume will need to be reformatted as it is saved in the object
map from its original coronal orientation to the transverse
orientation).
Figure 21 Save As window to save
binary mask as an object map file.
11
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
3.
Add a .obj extension to the SISCOM_SPECT_MASK file name in the File field at the bottom
of the Save As window (i.e., set the output file name to SISCOM_SPECT_MASK.obj).
4.
Change the Format field to the OBJMAP selection.
5.
Select the Save option once all of these parameters have been set.
This will save the SISCOM_SPECT_MASK binary volume image as
an Analyze Object Map, setting all of the binary 0’s to correspond to
the first object in the object map (Object_1) and all of the binary 1’s
to correspond to the second object in the object map (Object_2). This
SISCOM_SPECT_MASK.obj object map file will be used to
provide a defined region-of-interest in the Region Of Interest module
to sample only those voxels that were in the combined binary mask
from the ictal and interictal SPECT volume images.
6.
Select the subtraction SPECT volume image
SISCOM_SPECT_SUBTR in the main Analyze workspace and invoke
the Region Of Interest module under the Measure submenu (or use
the ROI power bar icon). See Figure 22.
7.
Select the Load Object Map option in the File submenu and select the
SISCOM_SPECT_MASK.obj object map that was just saved from
the file selection box that appears (either double click on this file to
load it or select Open after selecting the object map file). The Object
window will appear when the object map has been loaded.
Figure 22 ROI on subtraction SPECT.
8.
Select the Size option under the View submenu and change the display size for the images to
Quadruple. Select Done when the size is set.
9.
Select the Orientation option under the Generate submenu and set the current orthogonal
orientation to Coronal (if not already established as the default). Select Done when this is set.
10. Select the Slice option under the Generate submenu and set the Number slider to the total
number of images, in this case to the number 45. Make sure the Slice and Increment sliders
are set to 1 (default). Select Done when these are set.
11. Select the Sample Options item under the Generate submenu. In the
Sample Options window, set the Sample Type to Object(s). This will
cause a list of all objects to appear to the right of the Sample Type area.
Select the check box for Object_2 in this list of objects (ignore all
other objects – Object_1 is the background outside of the brain region
of interest and all other objects currently have no region definition).
See Figure 23.
12. For the rest of the Sample Options window, set the following
parameters: Combine Objects to No (default), the Minimum and
Maximum of the Sample Max/Min slider as defaulted (true max/min
in subtraction image), Range set to DataType (default), change
Summing to On, Auto Reset to On (default), Sequence Display to On
(default), Stat Type to Intensity (default), Decimal Places to 4, and
Log Stats set to Off (default – although this latter parameter could be
used to then record and save the mean value measured for the
subtraction SPECT volume image as an option to simply writing it
down).
13. Once all of the Sample Options have been established, select the
Sample Images button in the main Region Of Interest window. This
will cause the Region Of Interest module to go through all of the
Figure 23 ROI Sample Options for
images in the volume and sample the region specified in the object
subtraction SPECT sampling.
map as Object_2, which corresponds to those voxels segmented as
being part of the combined brain mask from the ictal and co-registered interictal volume
images.
12
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
14. When complete, the ROI Stats window will reflect the summed
measurements for the sampled parameters (Figure 24). In this case, since
the full range of the data was used, the Mean field will reflect the mean
value of all voxels throughout the entire masked subtraction SPECT
volume. In this case, the Mean should have a value of around 0.0086.
Note that given the normalization of the ictal and co-registered interictal
SPECT volume images to a mean value of 100, the subtraction SPECT
volume image should have a mean value of near 0. This provides a good
check for the validity of the current subtraction SPECT volume image.
15. Also from the ROI Stats window, the standard deviation field St. Dev. will
reflect the standard deviation of the voxel values in this masked region, and
in this case should have a value close to 12.3154. Note this St. Dev. value
down to be used to select only those voxels that are either one or two
standard deviations above the mean (0) in the subtraction SPECT volume
image.
16. Exit the Region Of Interest module.
Figure 24 ROI Stats
following sampling of
subtraction SPECT.
The standard deviation can now be used to threshold the subtraction SPECT image to
select only those voxels that are either one or two standard deviations above the
mean of 0. To do this for selection of values two standard deviations above the mean, do the following:
17. Compute a value that is two standard deviations above the mean. In this case, two standard
deviations is 12.3154 x 2 = 24.6308.
18. Select the SISCOM_SPECT_SUBTR volume image in the main Analyze workspace and
invoke the Save As module under the Files submenu.
19. Select the Intensities check box in the Save As window. With the default
Intensity Scale operation selected, set the Minimum intensity in the Input
column to be the value that is twice the standard deviation, in this case set it
to 24.6308. Setting this Minimum in the Input column causes only those
values between the Input Maximum and Minimum to be used during the
Intensity Scaling process – values lower that the Minimum will be set to
the Output Minimum and values higher than the Maximum will be set to the
Output Maximum, with linear scaling of the value in-between. (Note that
the Input Datatype will be Float, reflecting the data type used during the
subtraction to capture the full dynamic range of the subtracted values). See
Figure 25.
20. Change the Output DataType to Unsigned 8-bit. The Maximum and
Minimum intensities will default to 255 and 0. This will establish the
scaling of the range of values in the Input which are two standard deviations
about the mean to an Output range that will be a full unsigned 8-bit range
from 0 to 255, stretching the subtraction SPECT range over the full 8-bits of
value representation.
21. Set the file name in the File field to SISCOM_SPECT and select Save to
save the thresholded subtraction SPECT volume image
to disk.
22. Invoke the Load module under the File submenu, select the
SISCOM_SPECT volume image that was just saved and select Open to load
this volume image into the Analyze workspace.
13
Figure 25 Save As window for
saving 2SD SISCOM image.
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
23. Use the Multiplanar Sections module to review the SISCOM_SPECT volume images to see
the statistically significant regions of focal activation during the ictal SPECT scan (Figure 26).
Figure 26 Subtraction SPECT images depicting areas of focal activation in Multiplanar Sections module.
SPECT to MRI Registration
The SISCOM subtracted SPECT image is useful for determining where the focal activation of significance is
between the ictal and interictal SPECT scans. However, precise anatomic localization of these regions of
focal activation is difficult. Co-registration of the SPECT volume image to a volumetric MRI from the
patient provides the mechanism for fusion of the SPECT regions of focal activation with the anatomical
detail of the MRI.
Registration of the MRI and SPECT volumes images is accomplished through the use of the Analyze Surface
Matching registration algorithm. This algorithm requires the specific determination of the common surface
to register prior to applying the registration process, so preliminary segmentation is necessary. The following
steps demonstrate how to segment and register the MRI and SPECT volume images.
First, inhomogeneity correction is applied to the MRI volume image. This is often useful prior to the
application of segmentation tools, particularly if there are threshold components to those tools. To do this:
1.
Using the Load (or Load As) module, load the SISCOM_MRI.avw volume image from the
demonstration data directory.
2.
Select the SISCOM_MRI volume image in the main Analyze workspace and invoke the Spatial
Filter module in the Filters section of the Process submenu.
3.
Select the Filters option under the Generate submenu (or use the left-most powerbar icon) to
bring up the selection of spatial filters that are currently available (Figure 27).
14
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
4.
Select the Inhom. Correct option (Inhomogeneity Correction) at the
bottom of the Filter Type window.
5.
For the inhomogeneity correction parameters on the right side of the
Filters window, enter a Window Size of 75 and a Threshold minimum
of 10 to threshold out the background noise in the MRI volume image.
6.
Select the Filter button at the bottom center of the Filter window to
process the MRI volume image. When asked, choose the Change a
Copy of the Loaded Volume option to create a new volume image,
named SISCOM_MRI0, holding the corrected MRI volume data.
7.
Exit the Spatial Filter module.
8.
Select the SISCOM_MRI0 volume image in the main Analyze
workspace and use the right-mouse-button menu to invoke the Rename
option. Change the name of this volume image to
SISCOM_MRI_INHCOR.
9.
Use the Save (or Save As) module to save the SISCOM_MRI_INHCOR
volume image to disk.
This inhomogeneity-corrected MRI volume image can now be used with an
advanced morphological segmentation tool within Analyze, called Object
Figure 27 Filters window for MRI
Extractor, to segment out a representation of the whole brain from the MRI
inhomogeneity correction.
volume image. The desired result is a filled binary representation of the whole
brain where the outer cortical surface will be used to surface match against the
previously segmented estimate of the cortical surface of the brain from the combined ictal and interictal
SPECT volume images. To complete this segmentation:
10. Select the SISCOM_MRI_INHCOR inhomogeneity-corrected MRI volume image and invoke
the Object Extractor module from the Segment section of the Process submenu. (See the
Automated Object Extraction tutorial for information on segmentation with Object Extractor).
11. Under View, select the Intensities option and interactively select a Window Maximum that
provides a brightened display of the MRI volume image – around 100 works well.
12. Select the Define Region option under the Generate
submenu (or the left-most powerbar icon). This will
bring up an interactive window for definition of a
‘target’ region of interested on a given slice.
13. In the Define Region window, select the Coronal
Orientation and use the Slice slider to select a slice
through the head that provides a good representation of
the full extent of the brain. In this case, use coronal
slice 115. Once the slice has been set, select the
Define Region button at the bottom left of the Define
Region window.
14. Within the displayed image, select a position to place a
seed point somewhere within the brain, i.e. pick a
position in the cerebral white matter. This will invoke
a Threshold Max/Min slider that can be used to grow a
bounded region from this seed point to connect all
voxels within this defined threshold range. Adjust the
Figure 28 Object Extractor Define Region window with
threshold range to provide a reasonable bounded
target brain region defined prior to segmentation.
region surrounding the brain, with the outer margins of
the region following the outline of the cerebral cortex.
A good range in this example uses a Minimum of 22 and a Maximum of 50 (Figure 28).
15
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
15. Once the threshold and associated region has been specified, select
the Extract Object button at the bottom of the window. This will
invoke an Extract Options window with several options to control
the morphologic-based segmentation of the brain in this MRI
volume image.
16. In the Extract Options menu, set the following options: Number of
Dialtions to Auto(default), Fill Holes to On, Final Connect to Off
(default), Result to Binary, and Reuse Input Volume to No
(default). Set the Output File Name field to the name
SISCOM_MRI_Brain_BIN (Figure 29). Once these parameters
have been set, select the Done button to dismiss this window. Then
select the Extract Object button in the Define Regions window to
start the extraction process.
Figure 29 Extract Options to
segment MRI binary brain mask.
17. The object extraction process will morphologically segment the brain
from the MRI volume image through a series of erode, connectivity
analysis, and dilate operations. Once the process is complete, a Fill
Holes step will fill all interior holes in the binary volume image of the
brain (similar to what was done in creating the SPECT brain masks in
the previous sections), and the segmented whole binary brain volume
will be output back to the Analyze workspace with the specified name
of SISCOM_MRI_Brain_BIN. This solid binary representation of
the brain provides the exterior cortical surface as the explicit surface
against which the segmented binary brain from the combined
ictal/interictal SPECT binary volume image is matched in the
registration process (Figure 30).
18. Exit the Object Extractor module.
19. Use the Save (or Save As) module to save the
SISCOM_MRI_Brain_BIN volume image to disk.
Figure 30 Segmented MRI binary.
.brain mask (coronal slice 115).
The Surface Matching algorithm is now used to co-register these two segmented surfaces – the MRI brain
and the combined ictal/interictal brain:
20. In the main Analyze workspace, select the SISCOM_MRI_Brain_BIN volume image with
the left mouse button. Then select the SISCOM_SPECT_MASK volume image using the
middle mouse button, resulting in both volumes being selected
(border highlight on each). (Note that order will be important
here, as when the registration module is invoked, the first
selection will be the Base volume and the second selection will
be the Match volume.) See Figure 31.
21. Invoke the 3-D Registration module in the Fusion section of
the Process submenu.
22. To check to make sure the proper volumes are assigned to the
Base and Match registration volumes, enable the Input/Output
Ports option under the File submenu in the 3-D Registration
module. If for some reason the Base Volume is not the
SISCOM_MRI_Brain_BIN volume image and/or the Match
Volume is not SISCOM_SPECT_MASK, drag the appropriate
volumes from the Analyze workspace to the Base or Match
Volume ports. (Note: the base volume here is the higherresolution MRI volume image. In Surface Matching, it is
important that the higher resolution volume image is the base,
as the match volume surface will be sampled by only a limited
set of points which are then matched against the base surface.)
16
Figure 31 Currently loaded volumes – new
volumes include subtraction SPECT, >2SD
SPECT, MRI, corrected MRI, MRI brain
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
23. Under Generate select the Surface Match option for the surface matching
registration algorithm.
24. Select the Sampling check box and set the Number of Points to use from the
match surface to 1000 (Figure 32).
25. Select the Register button to start the registration process.
26. When registration is complete, the Matrix Tool window will appear with the
registration transform for registering the SPECT volume images to the MRI
volume image (Figure 33). Note that since all of the SPECT volume images are
in co-registration, this transformation provides a mechanism for transforming any
of the SPECT volumes into spatial co-registration with the MRI volume image.
This includes the original ictal scan (SISCOM_Ictal_SPECT),
the co-registered interictal volume image
(SISCOM_Interictal_SPECT_TRANS), and the focal
activation subtraction SPECT volume image (SISCOM_SPECT).
Figure 32 Surface Match
options window.
27. Select the Save Matrix button in the Matrix Tool and save the
co-registration transformation matrix to a file called
SPECT_to_MRI.mat. Select Done on the Matrix Tool to
exit the tool.
28. The currently running 3-D Registration module with the current
transformation matrix can be used to further visualize and
investigate the functional to structural co-registration and fusion
of the SPECT and MRI volume images. Select the
inhomogeneity-corrected MRI volume image
(SISCOM_MRI_INHCOR) in the main Analyze workspace and
drag it to the Base Volume Input/Output port in the 3-D
Registration module. Select the original ictal SPECT volume
Figure 33 Matrix Tool transformation
for SPECT to MRI registration.
image (SISCOM_Ictal_SPECT) and drag it to the
Match Volume Input/Output port.
29. Select the Cursor Link tool from the Tools submenu once
these new input volumes have been assigned. The Cursor
Link tool will now allow a direct, interactive visualization
of the fused MRI and ictal SPECT scans to visually inspect
the accuracy of the SPECT to MRI registration (Figure 34).
Similarly, the co-registered interictal SPECT volume image
(SISCOM_Interictal_SPECT_TRANS) can be
dragged
to the Match Volume Input/Output port to view it fused
to the MRI.
30. Of most importance is the visualization of the focal regions
of activation from the subtraction SPECT with the
structural anatomy depicted in the MRI. Select and drag
the SISCOM_SPECT volume image to the Match Volume
Input/Output port. The Cursor Link tool now displays the
statistically significant regions of focal activation during
seizure directly fused with the structural details from the
MRI volume image (Figure 35). This tool can be used to
evaluate these regions, using the linked cursor to interrogate
specific locations and the Section and Orientation, in the
View submenu, controls to change the slice displayed. In
this particular example, the SISCOM activation site
corresponds with a right parietal lesion in the MRI.
17
Figure 34 Cursor Link tool showing registration
and fusion of MRI and original ictal SPECT.
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
31. Controlling the contribution of each individual image
and the window for each image can be used to
optimize the fused image display. The % Base Image
slider at the bottom of the tool can control the
contribution of Base vs. Match images in the fused
display. It may also be necessary to change the
Intensities parameters in the View submenu to create a
better-fused display, as with decreasing the Maximum
for the MRI base volume in this case. Note that there
are separate window intensity controls for both the
Base and Match Volume images.
32. The fused SPECT/MRI images depicted in the upper
right of the Cursor Link tool can be saved as a
complete 24-bit AnalyzeAVW volume image and used
with other modules in the Analyze software system.
To do this, first optimize the display of the fused
images as in the previous step. Then select the
Transform option in the Generate menu. Select the
Fuse Match to Base option, and set the Destination to
Analyze Workspace (default) with a new file name of
SISCOM_Fused in the Name field. Select the GO
button to create this fused volume image. This
SISCOM_Fused 24-bit volume image can then be
used directly in modules like
Multiplanar Sections or Oblique
Sections to further review the
integrated visualization of
SPECT function and MRI
structure (Figures 36 and 37).
Figure 35 A SISCOM image - registration
and fusion of MRI and subtraction SPECT.
33. Use the Save (or Save As)
module to save the
SISCOM_Fused volume image
to disk.
34. The SISCOM_SPECT volume
image should also be
transformed as an independent
volume image co-registered to
the MRI. To do this, select the
Transform Match to Base
option, change the output file
name to
SISCOM_SPECT_CoReg in the
Name field, and select GO. This
will output the
SISCOM_SPECT image in
spatial registration with the MRI
for use in creating advanced,
synergistic 3-D visualizations of
these two volume images.
Figure 36 Coronal SISCOM images through region of focal activation
during seizure.
35. Use the Save (or Save As) module to save the SISCOM_SPECT_CoReg volume image to disk.
36. Exit the 3-D Registration module.
18
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
The resulting SISCOM_SPECT_CoReg and SISCOM_Fused volume images are the final results for the
SISCOM process. The SISCOM_Fused volume provides valuable information on the functional to
structural correlation of the regions of focal activation during seizure. These images can be used in the
evaluation of the clinical epilepsy case, and may further be integrated into other interactive display/guidance
systems for direct use of the integrated information in the potential treatment approach. The
SISCOM_SPECT_CoReg volume image can be used as a source of focal activation regions for other kinds
of processing, like deriving objects in an object map for direct visualization of these regions with the
structural details of the MRI brain using volume rendering.
Figure 37 3-D visualization of SISCOM
focal activation region in epilepsy.
19
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
The Automated SISCOM Module
As mentioned in the Introduction, the Analyze process used to accomplish the SISCOM procedure provides a
method and a template to develop a specific application module to accomplish this same task in a more
automated way. The SISCOM module in Analyze (Process->Fusion->SISCOM) provides such a tool to
easily accomplish the SISCOM processing of appropriate input data. The following provides a tutorial on
using this SISCOM module with exactly the same data as in the manual method above, demonstrating this
ability to process SISCOM data in a simple and efficient manner.
Starting the SISCOM Module
The input to the SISCOM module consists of the original three volume images as used in the manual
method: SISCOM_Ictal_SPECT, SISCOM_InterIctal_SPECT, and SISCOM_MRI (Note: If the
manual SISCOM tutorial has just been completed, the Analyze Workspace could be cleaned up by removal of
all but these three original files, if needed). To start the SISCOM module:
1.
In the main Analyze workspace, select the
SISCOM_Ictal_SPECT volume image with the left
mouse button. Then select the
SISCOM_InterIctal_SPECT volume image using the
middle mouse button (or Shift->left mouse button if no
middle button is available), resulting in both volumes being
selected (border highlight on each). Then select the
SISCOM_MRI volume image using the middle mouse
button again, which will result in all three of the input
volume images being selected. (Note that order will be
important here, as when the SISCOM module is invoked,
the first selection is expected to be the ictal SPECT volume,
the second selection the interictal SPECT volume, and the
third the MRI volume image.)
2.
Invoke the SISCOM module from the Process->Fusion>SISCOM menu item. (Note: if the original three input
volumes were not all selected or were selected in the wrong
order, the Input/Output Ports under File can be enabled to
allow any of the volumes to be dragged from the Analyze
Workspace to the respective port – see Figure 38).
Figure 38 SISCOM module with three input
volumes (ictal SPECT, interictal SPECT, MRI).
3.
Select the Process->Register SPECT tool (or use the left-most
icon on the powerbar) to invoke the Register SPECT tool to
automatically compute an activation map from the two SPECT
volume images (see Figure 39).
4.
The Register SPECT tool provides an interface to threshold
determination for the two input SPECT images in order to define
voxels corresponding to true cerebral activity. The initial
thresholds are set such that the minimum is 25% of the
maximum. In this case, manipulate the Cerebral Activity
Threshold slice for the Interictal volume to change it from the
default value of 63 to value of 75 used in the manual method.
(Note the change in the binary image and its relationship to the
binary image for the Ictal volume. The current slice number and
the orthogonal orientation can also be changed).
5.
Figure 39 Register SPECT Tool before
When the SPECT images are registered, the resampling of the
computation of activation map.
Interictal image can be accomplished using several different
interpolation algorithms. The Interictal Transformation Type selection provides the
mechanism to choose between Nearest Neighbor, Linear, Cubic Spline and Sinc interpolation.
20
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
6.
Once the SPECT images are registered, normalized and subtracted, the
level of significance in the activation signal determines which voxels
are considered as possible regions of activation. The Activation Level
selection allows specification of this significance, either One Standard
Deviation or Two Standard Deviations from the mean in the
subtraction image.
7.
Select the Register Interictal to Ictal and Calculate Activation Map
button to do the entire process: registration, normalization, subtraction,
selection of statistically significant voxels of activation, and output of
the activation map. The Activation Map will become part of the
Register SPECT interface (Figure 40) and will also be placed into the
Activation Map Input/Output Port in the main SISCOM module.
(Note: the Activation Map Input/Output Port allows the Activation
Map volume to be saved back to the Analyze Workspace by using the
right-mouse-button menu to select the Export Activation Map option –
see Figure 43).
8.
Select the Next button to continue along the SISCOM process.
9.
The Extract MRI Brain tool (which is invoked by the Next button) is
provided to allow facile segmentation of the brain from the full head
SISCOM_MRI volume image. This segmentation is done to provide a
basis for registration of the SPECT to the MRI, and to provide another
structural anatomy mask within which to keep the regions of activation.
Controls for the specific target slice and orientation are provided, along
with a Fill Holes after Extraction option to produce a solid segmentation
result (turned on by default). (Figure 41)
Figure 40 Register SPECT Tool with
Activation Map.
10. To achieve an optimal brain segmentation, the Minimum and Maximum
values for the defined target region should be manipulated to achieve the
best definition of the cortical boundary as possible. The Max should
further be manipulated to be as low as possible to remove tissues outside
of the brain, but maintain all of the voxels interior to the brain. In this
case, change the slider for the Max parameter to a value of 55.
11. Select the Extract Brain button to begin the morphologic-based
segmentation of the whole brain from this 3D MRI volume.
Figure 41 Extract MRI Brain tool for
brain segmentation.
12. When complete, a third panel is added to the Extract MRI Brain tool depicting the results of
this segmentation process. The extracted brain volume image is also placed
into the Extracted Brain Input/Output Port in the main SISCOM module
(which can again be exported to the Analyze Workspace).
13. If the results of the segmentation are satisfactory, select the Next button to
continue the SISCOM procedure.
14. The Fuse SPECT & MRI tool will automatically start the process of
registering the SPECT volumes to the SISCOM_MRI volume at this point in
the process (invoked by the Next button automatically). Once the registration
is complete, the Fuse SPECT & MRI tool will display the result of this
fusion – the Activation Map overlayed with the structural SISCOM_MRI
volume (see Figure 42).
15. The fused Activation Map and structural SISCOM_MRI volume can be
reviewed using the orthogonal orientation selection buttons and the slice
slider. The blending of the MRI and Activation Map can also be controlled
by the Blend % slider to achieve optimal visualization of both the structure
and the function depicted in this image.
16. The registration matrix can be reviewed using the Matrix button, and the
registration process can be executed again using the Match Surfaces button.
21
Figure 42 Extract MRI Brain
tool for brain segmentation.
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
17. If the spatial registration appears correct
and the fused image optimal, the
Activation Map from the SPECT image
can be fused with the SISCOM_MRI and
output to a new 24-bit RGB volume image
by selecting the Fuse Volumes button.
Choose this Fuse Volumes button to
create this output volume, called
SISCOM. The fused SISCOM volume
image is placed into the Fused
Input/Output Port of the main SISCOM
module, and is also directly exported back
to the Analyze Workspace (Figure 43).
This allows the fused SISCOM volume to
be used with the other Analyze modules
directly.
18. Select the Process->Compare Tool
option in the SISCOM module. This
Compare Tool allows any combination of
the input and/or derived volumes to be
visualized together in a fused
Figure 43 Main SISCOM module after completion of the SISCOM
representation to thoroughly review the
process depicting activation map fused with structural MRI.
results of the SISCOM process. As an
example, the registered Ictal SPECT volume
could be viewed with the SISCOM_MRI
volume as shown in Figure 44. Use this tool to
review different combinations of the input and
derived volume images.
19. An object map can also be directly generated
from the SISCOM module in which the objects
correspond to the regions of activation in the
Activation Map. Select the File->Create
ObjectMap option to invoke this menu.
20. The Activation Threshold (%) option allows
further control over the identification of true
activation regions prior to object map generation.
The total number of objects to include is
controlled by the Maximum Activation Objects
parameter, and the minimum size of the
activation regions can also be set using the
Maximum Activation Objects. This allows full
control over the inclusion of activation regions as
objects in the output Object Map.
Figure 44 SISCOM Compare Tool depicting fusion of
Ictal SPECT and structural MRI.
21. The Output Object Map option will generate an
object map from the current Activation Map, with the controlling parameters already described,
allowing the object map to be used in other Analyze modules in association with the original
SISCOM_MRI volume image. For example, this may be used in the Volume Render program
to directly create visualizations of 3D brain structure and region function (activation) as shown
in Figure 37.
22
SISCOM (Subtraction Ictal SPECT CO-registered to MRI)
References
TJ O'Brien, MK. O'Connor, BP Mullan, BH Brinkmann, DP Hanson, EL So. "Subtraction Ictal SPECT
Using Surface Matching Co-registration and Normalization by Mean Cerebral Pixel Intensity: Validation
of the Method with Phantom and Patient Studies." Nuclear Medicine Communications 1998. 19:31-45.
TJ O'Brien, EL So, BP Mullan, MF Hauser, BH Brinkmann, NI Bohnen, GD Cascino, DP Hanson, CR Jack,
FW Sharbrough. "Subtraction ictal SPECT coregistered to MRI improves clinical usefulness of SPECT in
localizing the seizure focus." Neurology 1998. 50:445-454.
TJ O'Brien, ML Zupanc, BP Mullan, MK O'Connor, BH Brinkmann, KM Cicora, and EL So. "The practical
utility of performing peri-ictal SPECT in the evaluation of children with partial epilepsy" Pediatric
Neurology 1998. 19(1): 15-22.
BH Brinkmann, A Manduca, and RA Robb. "Optimized homomorphic unsharp masking for MRI grayscale
inhomogeneity correction." IEEE Transactions on Medical Imaging 1998. 17(2): 161-171.
23