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21. The Differential SPECT Tool

21.1 Introduction

The SPECT tool implements several features for localizing focal epilepsy based on ictal and interictal SPECT subtraction methods. These features include two comparative methods to a healthy normal population, a straight subtract method for ictal and interictal SPECTs, a utility for combining activation blobs, and a cluster level statistics calculator base on random field theory. The two main methods are a reimplementation of the ISAS (Ictal-Interictal Subtraction Analysis by SPM) algorithm and a new variant, the ISAB (Ictal-Interictal Subtraction Analysis by Bioimage Suite) algorithm.

21.2 Interfacing with the Data Tree Manager

The SPECT tool is design to be used with the Data Tree Manager and a datatree structure. The Image Tab of the SPECT tool, shown in Figure 21.1 provides the functions for interacting with the Data Tree Manager. The Make Tree button creates a template datatree with the necessary healthy normal images in place and blank nodes for the patient images. The Grab Selection button on the SPECT tool allows inputs to ISAS or ISAB to be set directly from the datatree. Any resulting outputs from the SPECT processing methods are automatically put into the current datatree at the appropriate node. This integration with the Data Tree Manager allows for easier organization of data and a better visualization of how the images relates to each other (see Chapter 15).

Figure 21.1: The Images Tab of the SPECT Tool. The Images Tab interacts with the Data Tree Manager, sets inputs for the processing methods, and changes various options.

21.3 Ictal-Interictal Subtraction Analysis by SPM

The ISAS algorithm was introduced by Chang DJ et al. and McNally et al. It consists of several preprocessing steps followed by a comparison between the patient and a healthy normal population. First, the ictal and interictal SPECTs are non-linearly registered to MNI space. The warped SPECTs are then masked, smoothed and proportionally scaled to have a mean intensity value of 50. The proportional scaling scale factor is determined first by finding the full mean intensity (the mean intensity of every voxel in the image), second by finding the mean intensity of voxels greater than the full mean divided by 8, and third by dividing 50 by the mean voxel intensity. A voxel by voxel T-test or tmap is calculated by comparing the difference between the patient's ictal and interictal SPECT to the differences of between two SPECT images for healthy normals using:

$$t=\frac{x-\mu}{\sigma}$$ (21.1)

where

$$x=Ict_{Patient}-Int_{Patient}$$ (21.2)

$$\mu=\frac{1}{14}\sum_{k=1}^{14}{Ict_{HN}}_k - {Int_{HN}}_k$$ (21.3)

$$\sigma=\frac{1}{13}\sum_{k=1}^{14}({Ict_{HN}}_k - {Int_{HN}}_k-\mu)^2$$ (21.4)

The tmap can then be thresholded to find activation blobs and sent to the Results Tab for analysis. The ISAS_Calc button is located on the SPECT Processing Tab in the SPECT Tool, shown in Figure 21.2. For step by step instructions for performing ISAS in BioImage Suite, please see example 21.8. For step by step instructions for performing ISAS in SPM, please see http://spect.yale.edu/the ISAS website.

Figure 21.2: The SPECT Processing Tab of the SPECT Tool. The SPECT Processing Tab houses the ISAS and ISAB buttons and displays relevant information during calculations.

21.4 Ictal-Interictal Subtraction Analysis by Bioimage Suite

The ISAB algorithm uses the same preprocessing steps and healthy normal population as the ISAS algorithm. However ISAB assumes the mean noise between the healthy normal SPECT images is zero and estimates an unbiased standard deviation by using a half normal distribution. The tmap is calculated from the following:

$$t=\frac{x-\mu}{\sigma}$$ (21.5)

where

$$x=Ict_{Patient}-Int_{Patient}$$ (21.6)

$$\mu=0$$ (21.7)

$$\sigma=\sqrt{\frac{\pi}{2}}\frac{\sum_{k=1}^{14}\vert{Ict_{HN}}_k - {Int_{HN}}_k\vert}{14}$$ (21.8)

The ISAB_Calc button is located on the SPECT Processing Tab in the SPECT Tool. For step by step instructions for performing ISAB, please see the example 21.8.

21.5 Subtraction Processing

The Subtraction_Processing button located in the Utilities Tab, shown in Figure 21.2, provides a double check against registration, masking, and smoothing errors that may occur with ISAS and ISAB. The ictal SPECT is first rigidly aligned and intensity normalized to the interictal SPECT. The interictal SPECT is then subtracted from aligned and normalized ictal SPECT. The resulting subtraction SPECT can be thresholded at an appropriate intensity to use as a check against ISAS or ISAB. This image is automatically calculated when either ISAS or ISAB is performed but can be calculated by itself. See Example 21.9 on using the Utilities Tab below for step by step instructions.

Figure 21.3: The Utilities Tab of the SPECT Tool. The Utilities Tab houses two buttons to assist in the SPECT process and displays relevant information during calculations.

Figure 21.4: The Results Tab of the SPECT Tool. The Results Tab computes and displays cluster level statistics based on the tmap computed either from ISAS or ISAB.

21.6 Rejoining Blobs

The Rejoin_Blobs button located in the Utilities Tab provides an interface for combining the activation blobs created in SPM. When ISAS is preformed with SPM, two of the outputs are a hyperperfusion blob and a hypoperfusion blob. Combining these two blobs allows the blobs to be viewed together for better visualization of the changes between the ictal and the interictal SPECT. See Example 21.9 on using the Utilities Tab below for step by step instructing.

21.7 Cluster Level Statistics

The Results Tab provides an interface to compute cluster level statistics based on the tmap computed either from ISAS or ISAB. The Hyperperfusion Statistics button calculates the cluster level p-values based on increases between the ictal and interictal SPECTs while the Hypoperfusion Statistic button calculates p-values based on decreases between the ictal and interictal SPECTs. The uncorrected p-value is based on the smoothness of the healthy normal population SPECTS, the extend threshold, the significance level, and the size of the cluster. The corrected p-value also takes into account the shape of the tmap. The significance level and extend threshold can be set in the Images Tab of the SPECT Tool. The cluster level statistics are based off the work of Friston et al. and Worsley et al.

21.8 EXAMPLE: Running ISAS and ISAB

This section provides a step by step guide for running either ISAS or ISAB using the SPECT tool. It is assumed that the user is comfortable with the Data Tree Manager. Please see Chapter 15 for a description of the Data Tree Manager.

21.8.1 Setting Images

1. Open the Data Tree Manager in BioImage Suite. .
2. Click ``Tools'' in the menu of the Data Tree Manger, then ``SPECT Processing Tool''. The SPECT Processing Tool will pop up in a new dialog box.
3. Click ``File'' in the menu of the Data Tree Manger, then ``Switch Directories''. Select the appropriate working directory.
4. In the Images Tab of the SPECT Processing Tool, click the Make Tree button. See Figure 21.5.

   Figure 21.5: Making the template datatree using the Make Tree button. Left: The location of the Make Tree button, labeled A. Middle: The output of Make Tree button. The nodes in the green box are set while the nodes in the red box are not set. Right: The template datatree with all images loaded.

   1.1
   1. This creates a new datatree in the Data Tree Manager with all of the images preset for processing. The images and transformations between the MNI Template MRI, Mean SPECT, ISAS STD SPECT, and ISAB STD SPECT images are already specified.
   2. The required nodes that are not yet specified, i.e. Interictal SPECT, Ictal SPECT, Patient MRI, are gray and inserted into the tree as well.
5. In the Data Tree Manager, right click on the Patient MRI node and select ``Set Filename''. Browse to find the appropriate filename that corresponds to the patient's MRI. Here, the path of the image is being set; the image is not loaded to memory at this time.
6. Similarly, repeat for the Interictal SPECT node and Ictal SPECT node.

21.8.2 Registering Images

1. First register the Ictal SPECT to the Interictal SPECT using a rigid registration.

   Figure 21.6: Registering Images for SPECT processing. Top: Setting the ``Space/Anatomical'' Image and the ``Functional'' Image. Bottom: A completed tree with all transformations loaded. Notice the lines between the nodes are green.

   

   
   1. On the Data Tree Manager, highlight the Interictal SPECT and click the Set Space/Anatomical Image button. See Figure 21.6.
   2. Highlight the Ictal SPECT on the Data Tree Manager and click the Set Functional Image button.
   3. Send each of these images to the viewers by clicking the RF Viewer button for Space/Anatomical image and the TR Viewer button for the functional image. This sends the Interictal to the ``Reference'' viewer and the Ictal to the ``Transform'' viewer.
   4. In the Data Tree Manager menu, click ``Registration'', and then ``Linear Registration.'' The Registration/Overlay Tool should pop up in a new dialog box.
   5. To perform the registration of the Ictal SPECT to the Interictal SPECT, click the Rigid button.
   6. Once the registration is complete, you can check it in the viewers. Also, a message box should pop up displaying where the transformation was saved.
   7. In the Data Tree Manager, right-click on the Ictal SPECT node, select ``Load Transformation From Parent,'' and load the automatically saved *.matr file corresponding to the transformation of the Ictal SPECT to the Interictal SPECT.
2. Perform the same registration steps with the Interictal SPECT as the ``Functional Image'' and the Patient MRI as the ``Space/Anatomical Image'' to register the Interictal SPECT to the Patient MRI.
3. Perform the same registration steps with the Patient MRI as the ``Functional Image'' and the MNI Template MRI as the ``Space/Anatomical Image'' to register the Patient MRI to the MNI Template MRI. However, this time use the Compute Linear+Non-Linear Registration button found under the Non-Linear Registration Tab of the Registration/Overlay Tool instead of the Rigid button.
4. In the Data Tree Manger, right-click on the Patient MRI node, select ``Load Transformation From Parent,'' and load the automatically saved *.grd file corresponding to the transformation of the Patient MRI to the MNI Template MRI image.
5. For non-linear registrations, the inverse transformation must be explicitly calculated and loaded to the tree.
   1. First click on the Transformation Tab of the Registration/Overlay Tool.
   2. In the list of transformations on the left had side of the tab, select the non-linear *.grd file specifying the transformation between the Patient MRI and the MNI Template MRI.
   3. Click the Invert button located to the right. This will save the transformation as ``Invert_*.grd''.
   4. Once the transformation is inverted, click the Save button to save the new *.grd file.
   5. In the tree, right click on the Patient MRI node, select ''Load Transformation To Parent,'' and load the saved *.grd file corresponding to the inverted registration of the Patient MRI to the Template MRI image.

Note: All node lines between image should be green at this point as in Figure 21.6. The green arrows indicates that the transformations between the images are set. More information on performing registrations in BioImage Suite can be found in Chapter 10.

21.8.3 Performing ISAS and ISAB

Figure 21.7: The data tree and the SPECT Processing Tool are color coded. The color coding shows which Grab Selection button corresponds to which node in the data tree.

1. First, select the Icterictal SPECT node on the Data Tree Manager. Then click the corresponding Grab Selection button on the Images Tab of the SPECT Tool. See Figure 21.7. The Interictal text should turn green once the image is set.
2. Repeat for the Ictal SPECT.
3. Set the population mean and standard deviation.
   1. For ISAS, set the Mean SPECT with the Mean SPECT image in the Data Tree Manager and set the STD SPECT with the ISAS STD image in the Data Tree Manager.
   2. For ISAB, set the STD SPECT with the ISAB STD image. Note: The mean image does not need to be set for the ISAB algorithm.
4. (Optional) Set the SPECT mask.
5. At this point, all the necessary images should be set and the text should turn green. See Figure 21.8

   Figure 21.8: The Images tab with all images and options set.

   

6. Set the options located at the bottom of the Images Tab to the correct values.
   1. The Smoothing Kernel, Extent Threshold, and Significance level are set to default ISAS and ISAB values.
   2. To include masking in ISAS or ISAB, check the ``Use SPECT Mask'' checkbox. Note: The SPECT mask must be set to use this option.
   3. To save all intermediate images, check the "Save Intermediate Data".
7. Click on the SPECT Processing Tab of the SPECT Processing Tool. Then click on either the ISAS_Processing or ISAB_Processing buttons to begin.
8. The outputs will be stored in the datatree under either the ISAS STD node or the ISAB STD node. The default outputs are the Preprocessed_Ictal image, Preprocessed_Interictal image, the TMAP image, and the Prefusion Blobs image.

21.8.4 Viewing Results

1. Create an overlay of the Perfusion Blobs onto the MNI Template MRI. Figure 21.9 shows the Perfusion Blobs overlayed on a template MRI.

   Figure 21.9: The output of the creating an overlay of the Perfusion Blobs onto a template MRI.

   
   1. Highlight the MNI Template MRI node on the Data Tree Manager. Then, click the Set Space/Anatomical Image button located to the right.
   2. Highlight the Perfusion Blobs image on the datatree and click the Set Functional Image button.
   3. Click the Reslice Images button locate under the Set Space/Anatomical Image button.
   4. In the Overlays Tab of the Data Tree Manager (locate below theReslice Images button), set the low threshold to zero and click the Create Overlay button.
   5. The transform viewer should now show the Prefusion Blobs overlayed on to the Template MRI.
2. On the SPECT Tool, click on the Results Tab and set the TMAP image with the output TMAP from the SPECT processing.
3. Click either the Hyperperfusion Statistics button or the Hypoperfusion Statistics button to calculate the cluster level statistics. The cluster size, cluster p-value, corrected p-value, the maximum tscore, and XYZ coordinates of the maximum tscore will be displayed on the Results Tab.
4. The Set Crosshairs buttons can be used to navigate the Transform Viewer to the XYZ coordinates of the maximum tscore.
5. To view the Subtraction_Processing results, create an overlay using the instructions above with the Subtraction image as the ``Functional Image'' and the Patient MRI as the ``Space/Anatomical Image''. Note: The Subtraction image is locate under the Interictal node on the datatree.
6. Create coronal slices of the Perfusion Blobs on the Patient MRI
   1. Create an overlay using the steps above, but use the Perfusion Blobs as the ``Functional Image'' and the Patient MRI ``Space/Anatomical Image.''
   2. On the Data Tree Manager menu, select ``Viewers'' and then ``Simple Viewer.'' The Simple Viewer will now pop up.
   3. In the Simple Viewer main menu, select ``Display'' and then ``Grab From Transform Viewer.''
   4. Adjust the number of rows and columns in the Simple Viewer to display desired number of coronal slices.

21.9 EXAMPLE: Using the Utilities Tab

This example illustrates how to use the Utilities Tab of the SPECT tool.

21.9.1 Running the Subtraction Processing

1. Start with a datatree with the Ictal and Interictal SPECT and the rigid transformation set. For detailed instructions on setting up the tree, refer to steps 21.8.1-21.8.2 in Example 21.8
2. First, select the Icterictal SPECT node on the Data Tree Manager. Then click the corresponding Grab Selection button on the Images Tab of the SPECT Tool. The Interictal text should turn green once the image is set.
3. Repeat for the Ictal SPECT.
4. In the Utilities Tab of the SPECT Processing Tool, click on the Subtraction_Processing button.
5. Once finished, the SPECT tool will place the result as a child of the Interictal SPECT node.
6. To view the results, please refer to Viewing Results on Example 21.8.

21.9.2 Running the Rejoin Blobs Utility

1. In a the Data Tree Manager under the MNI Template MRI, add a node, title it ``Hyperperfusion Blob'', and set the hyperperfusion blob filename to the node. For more information on the Data Tree Manager see Chapter 15
2. Add a second node under the MNI brain. Title this node ``Hypoperfusion Blob'', and set the hypoperfusion blob filename to the node
3. First, select the Hypoperfusion Blob on the datatree and then click the Grab Selection button on the Images Tab of the SPECT Tool. The Hyperperfusion text should turn green once the image is set.
4. Repeat for the Hypoperfusion Blob.
5. On the Utilities Tab of the SPECT tool, click the Rejoin_Blobs.
6. The result, a single blob showing both hyperperfusion and hypoperfusion, will be placed in the datatree as a child of the Hyperperfusion Blob node. Note: Images created in SPM2 have a different initial orientation and will show up looking flipped in BioImage Suite. To correct for this problem, the image must be rotated $180\,^{\circ}$ in the z-direction. For more information on rotating images, see Chapter 7.

Webpage design by Marcello DiStasio.


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