SISCOM (Subtraction Ictal SPECT CO-registered to MRI)

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "SISCOM (Subtraction Ictal SPECT CO-registered to MRI)"

Transcription

1 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 99 Tc m -HMPAO and 99 Tc m -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 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

2 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 mm 3. 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. Figure 2 Interictal 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 mm 3. 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 256 x 256 x 124 and voxel resolution of x x 1.6 mm 3. 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 x x mm 3 (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 technique. In many cases, it is desirable to create 3-D visualizations of the fused subtraction SPECT images and the MRI volume image to identify the area of activation relative to the cortical anatomy. To do this, the Figure 3 MRI coronal-oblique slice 115. 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

3 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 illuminated). The images Figure 4 Ictal SPECT volume displayed in Multiplanar Sections. 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

4 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 while reviewing the change to the image. The purpose Figure 6 The Intensity window is used to here is to estimate a reasonable threshold that eliminates interactively threshold the volume. 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. If you do not have a middle mouse button, hold down the Control key while using the left mouse button. 4

5 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 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. Figure 8 The Cursor Link tool with a base image (ictal), transformed match image (interictal), and a fused image. 5

6 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 Figure 9 Transform window. SISCOM_Interictal_SPECT_TRANS volume image to a disk file of the same name. 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 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. 6 Figure 10 The Morphology Step Editor after the first Fill Holes operation.

7 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 Figure 11 Fill Holes window. the Step Editor window to review the processed images. 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_TRANS 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

8 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 8- bit, 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

9 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 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. Figure 16 ROI Sample Options window set to sample ictal activity. Figure 17 ROI samples from masked ictal SPECT. 7. 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

10 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 (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 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 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 Figure 18 Image Algebra normalization of ictal SPECT. an ictal SPECT volume normalized to a mean value of 100 (Figure 18). 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 , 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. Figure 19 Current loaded volumes new volumes are combined mask, masked ictal, masked interictal, normalized ictal, and normalized interictal. 10

11 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 Same, Datatype set to Float, and Max/Min Settings set to Calculate (Note: in this step, make absolutely Figure 20 Ictal - interictal SPECT subtraction in Image Algebra. 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

12 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 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 Figure 22 ROI on subtraction SPECT. load it or select Open after selecting the object map file). The Object window will appear when the object map has been loaded. 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 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

13 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 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 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 The standard deviation can now be used to threshold the subtraction SPECT image to subtraction SPECT. 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 x 2 = 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 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 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. Figure 25 Save As window for saving 2SD SISCOM image. 13

14 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

15 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 threshold range to provide a reasonable bounded Figure 28 Object Extractor Define Region window with region surrounding the brain, with the outer margins of target brain region defined prior to segmentation. 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

16 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. 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 29 Extract Options to segment MRI binary brain mask. 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 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.) Figure 31 Currently loaded volumes new volumes include subtraction SPECT, >2SD SPECT, MRI, corrected MRI, MRI brain 16

17 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). 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 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. Figure 32 Surface Match options window. Figure 33 Matrix Tool transformation for SPECT to MRI registration. Figure 34 Cursor Link tool showing registration and fusion of MRI and original ictal SPECT. 17

18 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). 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 35 A SISCOM image - registration and fusion of MRI and subtraction SPECT. 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

19 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

20 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). 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). Figure 38 SISCOM module with three input volumes (ictal SPECT, interictal SPECT, MRI). Figure 39 Register SPECT Tool before computation of activation map. 5. When the SPECT images are registered, the resampling of the 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

21 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) 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 Select the Extract Brain button to begin the morphologic-based segmentation of the whole brain from this 3D MRI volume. 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. Figure 40 Register SPECT Tool with Activation Map. Figure 41 Extract MRI Brain tool for brain segmentation. Figure 42 Extract MRI Brain tool for brain segmentation. 21

22 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 representation to thoroughly review the Figure 43 Main SISCOM module after completion of the SISCOM results of the SISCOM process. As an process depicting activation map fused with structural MRI. 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

Brain Images. Download the data. Comparing Images. 13 th February Version 1.4. For GIMIAS-1.4. Contact:

Brain Images. Download the data. Comparing Images. 13 th February Version 1.4. For GIMIAS-1.4. Contact: Brain Images Comparing Images 13 th February 2012 Version 1.4 For GIMIAS-1.4 Contact: cistib.developers@gmail.com The aim of this exercise is to show different visualization and fusion functionalities

More information

Essential Training Guide

Essential Training Guide Analyze 10.0 Essential Training Guide 1999-2010 Analyze Direct, Inc. 1986-2010 BIR, Mayo Clinic Analyze 10.0 Essential Training Guide 1 Table of Contents Getting Started and Loading Data into Analyze Analyze

More information

Diffusion Mapping with FireVoxel Quick Start Guide

Diffusion Mapping with FireVoxel Quick Start Guide Diffusion Mapping with FireVoxel Quick Start Guide Medical image analysis tool developed by Artem Mikheev and Henry Rusinek Radiology Department, NYU School of Medicine Original version prepared by Jinyu

More information

Ch. 4 Physical Principles of CT

Ch. 4 Physical Principles of CT Ch. 4 Physical Principles of CT CLRS 408: Intro to CT Department of Radiation Sciences Review: Why CT? Solution for radiography/tomography limitations Superimposition of structures Distinguishing between

More information

Introduction... 3 Image Basics... 3 Image Data... 4

Introduction... 3 Image Basics... 3 Image Data... 4 TABLE OF CONTENTS Introduction... 3 Image Basics... 3 Image Data... 4 Display... 5 Layouts... 6 Options... 7 Zooming... 7 Ruler/Angle Tools... 7 Crosshair Settings... 8 Color Tables... 8 Overlays... 9

More information

INTRODUCTION TO MEDICAL IMAGING- 3D LOCALIZATION LAB MANUAL 1. Modifications for P551 Fall 2013 Medical Physics Laboratory

INTRODUCTION TO MEDICAL IMAGING- 3D LOCALIZATION LAB MANUAL 1. Modifications for P551 Fall 2013 Medical Physics Laboratory INTRODUCTION TO MEDICAL IMAGING- 3D LOCALIZATION LAB MANUAL 1 Modifications for P551 Fall 2013 Medical Physics Laboratory Introduction Following the introductory lab 0, this lab exercise the student through

More information

icatvision Quick Reference

icatvision Quick Reference icatvision Quick Reference Navigating the i-cat Interface This guide shows how to: View reconstructed images Use main features and tools to optimize an image. REMINDER Images are displayed as if you are

More information

Image Acquisition Systems

Image Acquisition Systems Image Acquisition Systems Goals and Terminology Conventional Radiography Axial Tomography Computer Axial Tomography (CAT) Magnetic Resonance Imaging (MRI) PET, SPECT Ultrasound Microscopy Imaging ITCS

More information

A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms

A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms Omar Hamo, Georg Nelles, Gudrun Wagenknecht Central Institute for Electronics, Research Center Juelich,

More information

Learning to use the drawing tools

Learning to use the drawing tools Create a blank slide This module was developed for Office 2000 and 2001, but although there are cosmetic changes in the appearance of some of the tools, the basic functionality is the same in Powerpoint

More information

MSI 2D Viewer Software Guide

MSI 2D Viewer Software Guide MSI 2D Viewer Software Guide Page:1 DISCLAIMER We have used reasonable effort to include accurate and up-to-date information in this manual; it does not, however, make any warranties, conditions or representations

More information

FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING. Francesca Pizzorni Ferrarese

FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING. Francesca Pizzorni Ferrarese FROM IMAGE RECONSTRUCTION TO CONNECTIVITY ANALYSIS: A JOURNEY THROUGH THE BRAIN'S WIRING Francesca Pizzorni Ferrarese Pipeline overview WM and GM Segmentation Registration Data reconstruction Tractography

More information

Chapter 3 Set Redundancy in Magnetic Resonance Brain Images

Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 16 Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 3.1 MRI (magnetic resonance imaging) MRI is a technique of measuring physical structure within the human anatomy. Our proposed research focuses

More information

Biomedical Image Analysis based on Computational Registration Methods. João Manuel R. S. Tavares

Biomedical Image Analysis based on Computational Registration Methods. João Manuel R. S. Tavares Biomedical Image Analysis based on Computational Registration Methods João Manuel R. S. Tavares tavares@fe.up.pt, www.fe.up.pt/~tavares Outline 1. Introduction 2. Methods a) Spatial Registration of (2D

More information

TexRAD Research Version Client User Guide Version 3.9

TexRAD Research Version Client User Guide Version 3.9 Imaging tools for medical decision makers Cambridge Computed Imaging Ltd Grange Park Broadway Bourn Cambridge CB23 2TA UK TexRAD Research Version Client User Guide Version 3.9 Release date 23/05/2016 Number

More information

/5 Stacks. Displays the slice that follows the currently displayed slice. As a shortcut, press the > key.

/5 Stacks. Displays the slice that follows the currently displayed slice. As a shortcut, press the > key. 20-02-2018 1/5 Stacks Stacks This submenu contains commands that work with stacks. Add Slice Inserts a blank slice after the currently displayed slice. Hold down the Alt key to add the slice before the

More information

Prostate Detection Using Principal Component Analysis

Prostate Detection Using Principal Component Analysis Prostate Detection Using Principal Component Analysis Aamir Virani (avirani@stanford.edu) CS 229 Machine Learning Stanford University 16 December 2005 Introduction During the past two decades, computed

More information

ND Processing Tools in NIS-Elements

ND Processing Tools in NIS-Elements ND Processing Tools in NIS-Elements Overview This technical note describes basic uses of the ND processing tools available in NIS-Elements. These tools are specifically designed for arithmetic functions

More information

Automated segmentation methods for liver analysis in oncology applications

Automated segmentation methods for liver analysis in oncology applications University of Szeged Department of Image Processing and Computer Graphics Automated segmentation methods for liver analysis in oncology applications Ph. D. Thesis László Ruskó Thesis Advisor Dr. Antal

More information

OCT Segmentation Manual Jens Kowal, Pascal Dufour This manual gives a quick introduction into the OCT Segmentation application in particular it

OCT Segmentation Manual Jens Kowal, Pascal Dufour This manual gives a quick introduction into the OCT Segmentation application in particular it OCT Segmentation Manual Jens Kowal, Pascal Dufour This manual gives a quick introduction into the OCT Segmentation application in particular it explains how to prepare the OCT data in order to be loaded

More information

Application of Skills: Microsoft Excel 2013 Tutorial

Application of Skills: Microsoft Excel 2013 Tutorial Application of Skills: Microsoft Excel 2013 Tutorial Throughout this module, you will progress through a series of steps to create a spreadsheet for sales of a club or organization. You will continue to

More information

Table of Contents THE ANALYZEAVW IMAGE FILE FORMAT. 1. Organizing a List of DICOM Files page 2

Table of Contents THE ANALYZEAVW IMAGE FILE FORMAT. 1. Organizing a List of DICOM Files page 2 1 Table of Contents 1. Organizing a List of DICOM Files page 2 2. AVW_VolumeFile as a Header to a Single Binary File page 4 3. Organizing a List of Unknown Files page 5 4. AnalyzeAVW Image File Format

More information

Functional MRI data preprocessing. Cyril Pernet, PhD

Functional MRI data preprocessing. Cyril Pernet, PhD Functional MRI data preprocessing Cyril Pernet, PhD Data have been acquired, what s s next? time No matter the design, multiple volumes (made from multiple slices) have been acquired in time. Before getting

More information

SPM Introduction SPM! Scott Peltier. FMRI Laboratory University of Michigan. Software to perform computation, manipulation and display of imaging data

SPM Introduction SPM! Scott Peltier. FMRI Laboratory University of Michigan. Software to perform computation, manipulation and display of imaging data SPM Introduction Scott Peltier FMRI Laboratory University of Michigan Slides adapted from T. Nichols SPM! Software to perform computation, manipulation and display of imaging data 1 1 SPM : Overview Library

More information

Masking Lidar Cliff-Edge Artifacts

Masking Lidar Cliff-Edge Artifacts Masking Lidar Cliff-Edge Artifacts Methods 6/12/2014 Authors: Abigail Schaaf is a Remote Sensing Specialist at RedCastle Resources, Inc., working on site at the Remote Sensing Applications Center in Salt

More information

Computer Aided Surgery 8:49 69 (2003)

Computer Aided Surgery 8:49 69 (2003) Computer Aided Surgery 8:49 69 (2003) Biomedical Paper Multimodal Image Fusion in Ultrasound-Based Neuronavigation: Improving Overview and Interpretation by Integrating Preoperative MRI with Intraoperative

More information

Appendix B: Creating and Analyzing a Simple Model in Abaqus/CAE

Appendix B: Creating and Analyzing a Simple Model in Abaqus/CAE Getting Started with Abaqus: Interactive Edition Appendix B: Creating and Analyzing a Simple Model in Abaqus/CAE The following section is a basic tutorial for the experienced Abaqus user. It leads you

More information

LAB DEMONSTRATION COMPUTED TOMOGRAPHY USING DESKCAT Lab Manual: 0

LAB DEMONSTRATION COMPUTED TOMOGRAPHY USING DESKCAT Lab Manual: 0 LAB DEMONSTRATION COMPUTED TOMOGRAPHY USING DESKCAT Lab Manual: 0 Introduction This lab demonstration explores the physics and technology of Computed Tomography (CT) and guides the student and instructor

More information

Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos

Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos Jue Wu and Brian Avants Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, USA Abstract.

More information

ThE ultimate, INTuITIVE Mr INTErFAcE

ThE ultimate, INTuITIVE Mr INTErFAcE ThE ultimate, INTuITIVE Mr INTErFAcE Empowering you to do more The revolutionary Toshiba M-power user interface takes Mr performance and flexibility to levels higher than ever before. M-power is able to

More information

Lesson 5 Solid Modeling - Constructive Solid Geometry

Lesson 5 Solid Modeling - Constructive Solid Geometry AutoCAD 2000i Tutorial 5-1 Lesson 5 Solid Modeling - Constructive Solid Geometry Understand the Constructive Solid Geometry Concept. Create a Binary Tree. Understand the basic Boolean Operations. Create

More information

Lucy Phantom MR Grid Evaluation

Lucy Phantom MR Grid Evaluation Lucy Phantom MR Grid Evaluation Anil Sethi, PhD Loyola University Medical Center, Maywood, IL 60153 November 2015 I. Introduction: The MR distortion grid, used as an insert with Lucy 3D QA phantom, is

More information

Expression Design Lab Exercises

Expression Design Lab Exercises Expression Design Lab Exercises Creating Images with Expression Design 2 Beaches Around the World (Part 1: Beaches Around the World Series) Information in this document, including URL and other Internet

More information

Creating and Analyzing a Simple Model in Abaqus/CAE

Creating and Analyzing a Simple Model in Abaqus/CAE Appendix B: Creating and Analyzing a Simple Model in Abaqus/CAE The following section is a basic tutorial for the experienced Abaqus user. It leads you through the Abaqus/CAE modeling process by visiting

More information

TI-83 Graphing Functions & Relations

TI-83 Graphing Functions & Relations To graph a function: 1. The equation must be written in functional notation in explicit (or y equals ) form. (Y is the dependent variable and X is the independent variable.) For instance, we may need to

More information

Implementation of Advanced Image Guided Radiation Therapy

Implementation of Advanced Image Guided Radiation Therapy Image Acquisition Course Outline Principles, characteristics& applications of the available modalities Image Processing in the T x room Image guided treatment delivery What can / can t we do in the room

More information

Brain Extraction, Registration & EPI Distortion Correction

Brain Extraction, Registration & EPI Distortion Correction Brain Extraction, Registration & EPI Distortion Correction What use is Registration? Some common uses of registration: Combining across individuals in group studies: including fmri & diffusion Quantifying

More information

Section 9. Human Anatomy and Physiology

Section 9. Human Anatomy and Physiology Section 9. Human Anatomy and Physiology 9.1 MR Neuroimaging 9.2 Electroencephalography Overview As stated throughout, electrophysiology is the key tool in current systems neuroscience. However, single-

More information

EXCEL 2007 GETTING STARTED

EXCEL 2007 GETTING STARTED EXCEL 2007 GETTING STARTED TODAY S DESTINATION Quick Access Toolbar Customize it! Office Button Click Excel Options BREAK DOWN OF TABS & RIBBON Tab Name Contains Information relating to Contains the following

More information

BrainSpace V2.1: Geometric mapping and diffusion-based software for cross-subject multimodality brain imaging informatics

BrainSpace V2.1: Geometric mapping and diffusion-based software for cross-subject multimodality brain imaging informatics Brief Introduction to BrainSpace V2.1 BrainSpace V2.1: Geometric mapping and diffusion-based software for cross-subject multimodality brain imaging informatics Graphics and Imaging Laboratory Wayne State

More information

SQL Server. Management Studio. Chapter 3. In This Chapter. Management Studio. c Introduction to SQL Server

SQL Server. Management Studio. Chapter 3. In This Chapter. Management Studio. c Introduction to SQL Server Chapter 3 SQL Server Management Studio In This Chapter c Introduction to SQL Server Management Studio c Using SQL Server Management Studio with the Database Engine c Authoring Activities Using SQL Server

More information

Building a Basic Application with DT Vision Foundry

Building a Basic Application with DT Vision Foundry Goal Building a Basic Application with DT Vision Foundry This tutorial demonstrates how to develop an inspection application with DT Vision Foundry machine vision software from Data Translation. You will

More information

Experiment 1: Introduction to Labview 8.0 (tbc 1/7/2007, 1/13/2009,1/13/2011)

Experiment 1: Introduction to Labview 8.0 (tbc 1/7/2007, 1/13/2009,1/13/2011) Experiment 1: Introduction to Labview 8.0 (tbc 1/7/2007, 1/13/2009,1/13/2011) Objective: To learn how to build virtual instrument panels in Labview 8.0 Tasks: 1. Build the Labview VI shown in the tutorial

More information

EPI Data Are Acquired Serially. EPI Data Are Acquired Serially 10/23/2011. Functional Connectivity Preprocessing. fmri Preprocessing

EPI Data Are Acquired Serially. EPI Data Are Acquired Serially 10/23/2011. Functional Connectivity Preprocessing. fmri Preprocessing Functional Connectivity Preprocessing Geometric distortion Head motion Geometric distortion Head motion EPI Data Are Acquired Serially EPI Data Are Acquired Serially descending 1 EPI Data Are Acquired

More information

The Ball-Pivoting Algorithm for Surface Reconstruction

The Ball-Pivoting Algorithm for Surface Reconstruction The Ball-Pivoting Algorithm for Surface Reconstruction 1. Briefly summarize the paper s contributions. Does it address a new problem? Does it present a new approach? Does it show new types of results?

More information

Exercise 1: Introduction to MapInfo

Exercise 1: Introduction to MapInfo Geog 578 Exercise 1: Introduction to MapInfo Page: 1/22 Geog 578: GIS Applications Exercise 1: Introduction to MapInfo Assigned on January 25 th, 2006 Due on February 1 st, 2006 Total Points: 10 0. Convention

More information

course notes quick reference guide

course notes quick reference guide course notes quick reference guide Microsoft Excel 2010 Welcome to Excel 2010 Excel 2010 is the premier spreadsheet application from Microsoft. Excel 2010 makes it easier to analyze data quickly with new

More information

In this lecture. Background. Background. Background. PAM3012 Digital Image Processing for Radiographers

In this lecture. Background. Background. Background. PAM3012 Digital Image Processing for Radiographers PAM3012 Digital Image Processing for Radiographers Image Enhancement in the Spatial Domain (Part I) In this lecture Image Enhancement Introduction to spatial domain Information Greyscale transformations

More information

Intermediate Microsoft Excel 2008

Intermediate Microsoft Excel 2008 Intermediate Microsoft Excel 2008 Table of Contents ADVANCED FORMATTING... 2 FORMATTING NUMBERS... 2 WRAPPING TEXT... 3 THE MERGE AND CENTER FUNCTIONS... 4 INSERTING COMMENTS... 5 FREEZE PANES... 6 INSERTING

More information

Animated Modifiers (Morphing Teapot) Richard J Lapidus

Animated Modifiers (Morphing Teapot) Richard J Lapidus Animated Modifiers (Morphing Teapot) Richard J Lapidus Learning Objectives After completing this chapter, you will be able to: Add and adjust a wide range of modifiers. Work in both object and world space

More information

MEG Laboratory Reference Manual for research use

MEG Laboratory Reference Manual for research use MEG Laboratory Reference Manual for research use for Ver. 1R007B Manual version 20040224 Index 1. File... 11 1.1 New... 11 1.2 Open... 11 1.3 Transfer...... 11 1.4 Suspended File List... 12 1.5 Save...

More information

Interactive 3D editing tools for image segmentation q

Interactive 3D editing tools for image segmentation q Medical Image Analysis 8 (2004) 35 46 www.elsevier.com/locate/media Interactive 3D editing tools for image segmentation q Yan Kang, Klaus Engelke *, Willi A. Kalender Institute of Medical Physics, University

More information

Hands on practices on products and applications.

Hands on practices on products and applications. Hands on practices on products and applications. Karol Paradowski Senior Specialist Institute of Geodesy and Cartography Modzelewskiego 27 Street 02-679 Warsaw Poland karol.paradowski@igik.edu.pl www.igik.edu.pl

More information

Importing and processing a DGGE gel image

Importing and processing a DGGE gel image BioNumerics Tutorial: Importing and processing a DGGE gel image 1 Aim Comprehensive tools for the processing of electrophoresis fingerprints, both from slab gels and capillary sequencers are incorporated

More information

FLIR Tools+ and Report Studio

FLIR Tools+ and Report Studio Creating and Processing Word Templates http://www.infraredtraining.com 09-20-2017 2017, Infrared Training Center. 1 FLIR Report Studio Overview Report Studio is a Microsoft Word Reporting module that is

More information

Ossa 3D User Manual. ios App v1.0.0

Ossa 3D User Manual. ios App v1.0.0 Ossa 3D User Manual ios App v1.0.0 CONTENTS Description 3 Getting Started User Interface 4 Control Gestures 4 View Modes 5 Save Project 5 Settings Menu In-App Purchases 5 Enable Passcode 5 Page Tools View

More information

EZware Quick Start Guide

EZware Quick Start Guide EZware Quick Start Guide Your Industrial Control Solutions Source www.maplesystems.com For use as the following: Evaluation Tool for Prospective Users Introductory Guide for New Customers Maple Systems,

More information

MOVING FROM CELL TO CELL

MOVING FROM CELL TO CELL VCAE: EXCEL Lesson 1 Please send comments to Author: Zahra Siddiqui at zed_ess@hotmail.com Concepts Covered: Cell Address; Cell Pointer; Moving across Cells Constants: Entering, Editing, Formatting Using

More information

Definiens. Professional 5. Reference Book. Definiens AG.

Definiens. Professional 5. Reference Book. Definiens AG. Definiens Professional 5 Reference Book Definiens AG www.definiens.com 1 Algorithms Reference Imprint and Version Document Version 5.0.6.1 Copyright 2006 Definiens AG. All rights reserved. This document

More information

Quick Tips to Using I-DEAS. Learn about:

Quick Tips to Using I-DEAS. Learn about: Learn about: Quick Tips to Using I-DEAS I-DEAS Tutorials: Fundamental Skills windows mouse buttons applications and tasks menus icons part modeling viewing selecting data management using the online tutorials

More information

Creating a T-Spline using a Reference Image

Creating a T-Spline using a Reference Image 1 / 17 Goals Learn how to create a T-Spline using a Reference Image. 1. Insert an image into the workspace using Attach Canvas. 2. Use Calibrate to set the proper scale for the reference image. 3. Invoke

More information

Let s start by examining an Excel worksheet for the linear programming. Maximize P 70x 120y. subject to

Let s start by examining an Excel worksheet for the linear programming. Maximize P 70x 120y. subject to Excel is a useful tool for solving linear programming problems. In this question we ll solve and analyze our manufacturing problem with Excel. Although this problem can easily be solved graphically or

More information

Advanced Targeting Using Image Deformation. Justin Keister, MS DABR Aurora Health Care Kenosha, WI

Advanced Targeting Using Image Deformation. Justin Keister, MS DABR Aurora Health Care Kenosha, WI Advanced Targeting Using Image Deformation Justin Keister, MS DABR Aurora Health Care Kenosha, WI History of Targeting The advance of IMRT and CT simulation has changed how targets are identified in radiation

More information

A Data Flow Approach to Color Gamut Visualization

A Data Flow Approach to Color Gamut Visualization A Data Flow Approach to Color Gamut Visualization Gary W. Meyer and Chad A. Robertson Department of Computer and Information Science University of Oregon, Eugene, Oregon 97403 Abstract Software has been

More information

Abbie M. Diak, PhD Loyola University Medical Center Dept. of Radiation Oncology

Abbie M. Diak, PhD Loyola University Medical Center Dept. of Radiation Oncology Abbie M. Diak, PhD Loyola University Medical Center Dept. of Radiation Oncology Outline High Spectral and Spatial Resolution MR Imaging (HiSS) What it is How to do it Ways to use it HiSS for Radiation

More information

Excel Level 1

Excel Level 1 Excel 2016 - Level 1 Tell Me Assistant The Tell Me Assistant, which is new to all Office 2016 applications, allows users to search words, or phrases, about what they want to do in Excel. The Tell Me Assistant

More information

- Graphical editing of user montages for convenient data review - Import of user-defined file formats using generic reader

- Graphical editing of user montages for convenient data review - Import of user-defined file formats using generic reader Data review and processing Source montages and 3D whole-head mapping Onset of epileptic seizure with 3D whole-head maps and hemispheric comparison of density spectral arrays (DSA) Graphical display of

More information

Joint CI-JAI advanced accelerator lecture series Imaging and detectors for medical physics Lecture 1: Medical imaging

Joint CI-JAI advanced accelerator lecture series Imaging and detectors for medical physics Lecture 1: Medical imaging Joint CI-JAI advanced accelerator lecture series Imaging and detectors for medical physics Lecture 1: Medical imaging Dr Barbara Camanzi barbara.camanzi@stfc.ac.uk Course layout Day AM 09.30 11.00 PM 15.30

More information

XD 3.6 Release Notes

XD 3.6 Release Notes XD 3.6 Release Notes 1 Introduction 1.1 Purpose The purpose of these Release Notes is to: 1.2 Scope Communicate any known limitations for Mirada s XD release. Provide information on certain product features.

More information

better images mean better results

better images mean better results better images mean better results A better way for YOU and YOUR patient brought to you by Advanced Neuro analysis with access to studies wherever you need it Advanced Neuro from Invivo Advancements in

More information

An Automated Image-based Method for Multi-Leaf Collimator Positioning Verification in Intensity Modulated Radiation Therapy

An Automated Image-based Method for Multi-Leaf Collimator Positioning Verification in Intensity Modulated Radiation Therapy An Automated Image-based Method for Multi-Leaf Collimator Positioning Verification in Intensity Modulated Radiation Therapy Chenyang Xu 1, Siemens Corporate Research, Inc., Princeton, NJ, USA Xiaolei Huang,

More information

Basic Microsoft Excel 2007

Basic Microsoft Excel 2007 Basic Microsoft Excel 2007 Contents Starting Excel... 2 Excel Window Properties... 2 The Ribbon... 3 Tabs... 3 Contextual Tabs... 3 Dialog Box Launchers... 4 Galleries... 5 Minimizing the Ribbon... 5 The

More information

Quantitative Analysis and Visualization with 3D Slicer

Quantitative Analysis and Visualization with 3D Slicer Surgical Planning Laboratory Brigham and Women s Hospital Boston, Massachusetts USA a teaching affiliate of Harvard Medical School Quantitative Analysis and Visualization with 3D Slicer Jeffrey Yapp, PhD

More information

Status Bar: Right click on the Status Bar to add or remove features.

Status Bar: Right click on the Status Bar to add or remove features. Excel 2013 Quick Start Guide The Excel Window File Tab: Click to access actions like Print, Save As, etc. Also to set Excel options. Ribbon: Logically organizes actions onto Tabs, Groups, and Buttons to

More information

COBRE Scan Information

COBRE Scan Information COBRE Scan Information Below is more information on the directory structure for the COBRE imaging data. Also below are the imaging parameters for each series. Directory structure: var/www/html/dropbox/1139_anonymized/human:

More information

MR Advance Techniques. Vascular Imaging. Class III

MR Advance Techniques. Vascular Imaging. Class III MR Advance Techniques Vascular Imaging Class III 1 Vascular Imaging There are several methods that can be used to evaluate the cardiovascular systems with the use of MRI. MRI will aloud to evaluate morphology

More information

Using the Customize Dialog Box

Using the Customize Dialog Box Toolbar Tools > Customize Using the Customize Dialog Box The Customize tool is used to define custom work environment, toolbar, and tool settings. The Customize dialog box appears when you access the Customize

More information

Excel Tables & PivotTables

Excel Tables & PivotTables Excel Tables & PivotTables A PivotTable is a tool that is used to summarize and reorganize data from an Excel spreadsheet. PivotTables are very useful where there is a lot of data that to analyze. PivotTables

More information

Comp 145 UNC-Chapel Hill. Contract II MIDAS. Submitted to. Dr. Stephen Aylward and Prof. Greg Welch. February 13, Dr. Stephen Aylward, Client

Comp 145 UNC-Chapel Hill. Contract II MIDAS. Submitted to. Dr. Stephen Aylward and Prof. Greg Welch. February 13, Dr. Stephen Aylward, Client Comp 145 UNC-Chapel Hill Contract II MIDAS Submitted to Dr. Stephen Aylward and Prof. Greg Welch February 13, 2001 Dr. Stephen Aylward, Client Mike Beranek Anthony Chow Sharon Gravely Andy Mackelfresh

More information

Autodesk Fusion 360: Render. Overview

Autodesk Fusion 360: Render. Overview Overview Rendering is the process of generating an image by combining geometry, camera, texture, lighting and shading (also called materials) information using a computer program. Before an image can be

More information

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 MIT OpenCourseWare http://ocw.mit.edu HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Layout Tutorial. Getting Started. Creating a Layout Template

Layout Tutorial. Getting Started. Creating a Layout Template Layout Tutorial This tutorial will explain how create a layout template, send views to a layout page, then save the document in PDF format. In this tutorial you will learn about: Creating a Layout Template

More information

Excel 2010: Basics Learning Guide

Excel 2010: Basics Learning Guide Excel 2010: Basics Learning Guide Exploring Excel 2010 At first glance, Excel 2010 is largely the same as before. This guide will help clarify the new changes put into Excel 2010. The File Button The purple

More information

Analysis of CMR images within an integrated healthcare framework for remote monitoring

Analysis of CMR images within an integrated healthcare framework for remote monitoring Analysis of CMR images within an integrated healthcare framework for remote monitoring Abstract. We present a software for analyzing Cardiac Magnetic Resonance (CMR) images. This tool has been developed

More information

Image Registration I

Image Registration I Image Registration I Comp 254 Spring 2002 Guido Gerig Image Registration: Motivation Motivation for Image Registration Combine images from different modalities (multi-modality registration), e.g. CT&MRI,

More information

-Table of Contents- 1. Overview Installation and removal Operation Main menu Trend graph... 13

-Table of Contents- 1. Overview Installation and removal Operation Main menu Trend graph... 13 Thank you for buying Data Analysis Software. In order to use this software correctly and safely and to prevent trouble, please read this manual carefully. Notice 1. No part of this manual can be reproduced

More information

Excel FDLRS Sunrise

Excel FDLRS Sunrise Excel 2010 FDLRS Sunrise Within 2 weeks participants will submit a project: Create an Excel spreadsheet with a chart include the completed and signed ARROW form Send to: Lourdes Day FDLRS (Bartow Airport)

More information

Figure 1. Blank startup window

Figure 1. Blank startup window QCM Analyzer Version 0.3 User s Guide August 2015, Lubos Brieda, https://www.particleincell.com 1. About QCM Analyzer is an HTML/Javascript program for analyzing and postprocessing QCM log files. This

More information

ELEC451 Integrated Circuit Engineering Using Cadence's Virtuoso Layout Editing Tool

ELEC451 Integrated Circuit Engineering Using Cadence's Virtuoso Layout Editing Tool ELEC451 Integrated Circuit Engineering Using Cadence's Virtuoso Layout Editing Tool Contents Contents 1. General 2. Creating and Working On a Layout o 2.1 Undoing/Re-doing an Action o 2.2 Display Options

More information

Basic Data Acquisition with LabVIEW

Basic Data Acquisition with LabVIEW Basic Data Acquisition with LabVIEW INTRODUCTION This tutorial introduces the creation of LabView Virtual Instruments (VI s), in several individual lessons. These lessons create a simple sine wave signal,

More information

Limitations of Projection Radiography. Stereoscopic Breast Imaging. Limitations of Projection Radiography. 3-D Breast Imaging Methods

Limitations of Projection Radiography. Stereoscopic Breast Imaging. Limitations of Projection Radiography. 3-D Breast Imaging Methods Stereoscopic Breast Imaging Andrew D. A. Maidment, Ph.D. Chief, Physics Section Department of Radiology University of Pennsylvania Limitations of Projection Radiography Mammography is a projection imaging

More information

You are to turn in the following three graphs at the beginning of class on Wednesday, January 21.

You are to turn in the following three graphs at the beginning of class on Wednesday, January 21. Computer Tools for Data Analysis & Presentation Graphs All public machines on campus are now equipped with Word 2010 and Excel 2010. Although fancier graphical and statistical analysis programs exist,

More information

Microsoft Office Excel

Microsoft Office Excel Microsoft Office 2007 - Excel Help Click on the Microsoft Office Excel Help button in the top right corner. Type the desired word in the search box and then press the Enter key. Choose the desired topic

More information

Preprocessing of fmri data

Preprocessing of fmri data Preprocessing of fmri data Pierre Bellec CRIUGM, DIRO, UdM Flowchart of the NIAK fmri preprocessing pipeline fmri run 1 fmri run N individual datasets CIVET NUC, segmentation, spatial normalization slice

More information

EXCEL SPREADSHEET TUTORIAL

EXCEL SPREADSHEET TUTORIAL EXCEL SPREADSHEET TUTORIAL Note to all 200 level physics students: You will be expected to properly format data tables and graphs in all lab reports, as described in this tutorial. Therefore, you are responsible

More information

Seg3D Lab Walkthrough

Seg3D Lab Walkthrough Seg3D Lab Walkthrough Seg3D 2.1 Documentation Center for Integrative Biomedical Computing Scientific Computing & Imaging Institute University of Utah Seg3D software download: http://software.sci.utah.edu

More information

User Guide for Sylvius 4 Online An Interactive Atlas and Visual Glossary of Human Neuroanatomy

User Guide for Sylvius 4 Online An Interactive Atlas and Visual Glossary of Human Neuroanatomy User Guide for Sylvius 4 Online An Interactive Atlas and Visual Glossary of Human Neuroanatomy S. Mark Williams & Leonard E. White Overview About Sylvius 4 Sylvius 4 provides a unique computer-based learning

More information

Office 2016 Excel Basics 01 Video/Class Project #13 Excel Basics 1: Excel Grid, Formatting, Formulas, Cell References, Page Setup (O16-13)

Office 2016 Excel Basics 01 Video/Class Project #13 Excel Basics 1: Excel Grid, Formatting, Formulas, Cell References, Page Setup (O16-13) Office 2016 Excel Basics 01 Video/Class Project #13 Excel Basics 1: Excel Grid, Formatting, Formulas, Cell References, Page Setup (O16-13) Topics Covered in Video: 1) Excel file = Workbook, not Document

More information