PET/CT Analysis using 3D Slicer Jeffrey Yap PhD Ron Kikinis MD Wendy Plesniak PhD Slicer3 Training Compendium

Materials This tutorial requires the following installation: 3D Slicer version 3.5 Software, which can be installed from Tutorial data can be downloaded from: Disclaimer It is the responsibility of the user of 3DSlicer to comply with both the terms of the license and with the applicable laws, regulations and rules. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Tutorial Data Unzip the tutorial dataset to find the following files & folders: PETCTFusion-TutorialScene.mrml (Scene file) CT1 (folder containing Pre-Treatment CT DICOM data) PET1 (folder containing Pre-Treatment PET DICOM data) PET1-label.nrrd (pre-generated label map file) CT2 (folder containing Post-Treatment CT DICOM data) PET2 (folder containing Post-Treatment PET DICOM data) PET2-label.nrrd (pre-generated label map file) If all of these files are not present, please try downloading the tutorial dataset again. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Tutorial Data CT acquisition: (jeff, please fill in anything relevant about the dataset) PET acquisition: (jeff, please fill in anything relevant about the dataset, Isotope used, etc.) Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Clinical Context Clinical relevance : (jeff) -underlying diagnosis -why were scans done -what was radiologist’s interpretation -treatment? -followup plan. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Clinical Context Tutorial context : (jeff) -background: standard ways to evaluate response to treatment -how this tutorial addresses clinical needs This tutorial demonstrates how a pre-treatment study is analyzed, and provides a follow-up study for independent analysis and evaluation. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Learning objective Following this simple tutorial, you’ll be able to use 3D Slicer to: Load a MRML scene description file, Achieve combined display of PET and CT data, Make quantitative measurements of Standardized Uptake Value (SUV) within one or more volumes of interest Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Overview of workflow 1.Brief Slicer GUI overview 2.Load CT, PET, and tumor segmentation in a MRML scene description file 3.Select Volumes in PETCTFusion Module 4.Adjust Display for PET and CT Fusion 5.Compute Max SUVbw and Mean SUVbw 6.Browse SUV attributes 7.Repeat for post-treatment CT, PET and tumor segmentation as independent exercise Tutorial Workflow Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Slicer GUI overview * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary The Graphical User Interface (GUI) of Slicer3 integrates five components: the Menu Toolbar the Module GUI Panel the 3D Viewer the Slice Viewer the Slice and 3D View Controller Slice Viewer 3DViewer Module GUI Panel Slice and 3D View Controller Menu Toolbar Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA

Load the MRML Scene Using File->Load Scene: Raises the Load Scene Interface select the PETCTFusion- Tutorial-Scene.mrml file double click the file, or click “Open” Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Background: What is “MRML”? MRML (pronounced mur-muhl) stands for the "Medical Reality Markup Language"; and is an XML-based representation of a MRML Scene. MRML Scene description is the XML-based file (with extension.mrml) that contains the complete MRML Scene specification, and which references all datasets present in the MRML Scene. MRML Scene is the collection of datasets and their current state, viewing parameters, semantic descriptions, and algorithm parameterization. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Navigate to the PETCTFusion Module Note: This menu lists all of the software modules available in this build of 3D Slicer. Using Modules Menubutton: Expose the menu and select the module called “PETCTFusion”. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Navigate to the PETCTFusion Module Clicking the hyperlink here will take you to the module’s documentation web page. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary Click on the Help & Acknowledgements Panel to see more information about this module. Clicking the grey title bar again will collapse the panel.

Select the Volumes to Display and Analyze In each of the volume selectors in the “Data Fusion” panel, select the: CT1 volume, PET1 volume, and PET1-label volume. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary When the PET volume is selected, a “wait message” will be displayed while parameters are read from DICOM header.

Adjust the combined display of PET and CT data In the “Display” panel, adjust the window and level for CT1 and PET1 volumes. Choose a colorization option from among Grey, Heat, or Spectrum. (Heat is the default.) Adjust the Slices Fade Scale to jointly display the datasets. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Explore the dataset Scroll through the slices to locate the green Tumor label and the yellow Liver label (used only to demonstrate multiple-VOI functionality). Type –740 in the Axial Viewer’s Slice Number Entry widget. To view the Tumor label. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

View Tumor Label in all Slice Viewers To return all Slice Viewers to the Tumor Label: Select the Scene Snapshots menubutton: and choose “PreTreatmentPETCT” and the “Restore” option. (Touch the Slices Fade Scale to update all viewers.) Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Brief Description of Quantitative Measurement: Standardized Uptake Value Under certain circumstances, 18 FDG SUV correlates with metabolic rate of glucose and/or the number of viable tumor cells Simplified semi-quantitative measure that can be routinely performed in clinical PET studies Adjusts for differences in patient size and injected activity SUV (time) = Radioactive Concentration x Weight Injected Activity Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Making assessments from measurements: Response criteria Complete response (CR): Complete resolution of all lesions Partial Response (PR): >= 25% decrease in SUVmax Stable Disease (SD): < 25% change Progressive Disease (PD): >= 25% increase in SUVmax Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Compute SUV for all VOIs in the pre-treatment study In the “Quantitative Measures” panel, click the “Compute/Refresh” button. SUVmax and SUVmean for each VOI (represented by a different color in the label map) will be displayed in the table. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Browse SUV attributes. In the “Study Parameters” panel, click the arrow icon at the right to expand the “Refresh SUV attributes” panel to find the attributes used in performing the SUVbw computation. Clicking the “x” icon will collapse the panel again. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Compute SUVbw for follow-up study. Now select “CT2”, “PET2” and “PET2- label” in the Data Fusion Panel. This dataset contains a green VOI that corresponds to the one in the pre- treatment study. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Compute SUVbw for follow-up study. Adjust the display for the follow-up study. In the “Quantitative Measures” Panel, select “Compute/Refresh” to compute SUVbw for the corresponding VOI. Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Assess response with respect to this VOI Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary Pre-Treatment Max SUVbw = Post-Treatment Max SUVbw = g/ml g/ml % => (SD) (jeff – please confirm/fix.)

This tutorial has demonstrated: use of an interactive interface to load a scene the combined display of PET and CT volumes in a single visualization a workflow to make quantitative measurements of Standardized Uptake Value (SUV) within a volume of interest Summary Yap J, Kikinis R, Plesniak W: Harvard CTSC at RSNA * Brief Slicer GUI overview * Load CT, PET, and tumor segmentation * Select Volumes in PETCTFusion Module * Adjust Display for PET and CT Fusion * Compute SUVmax and SUVmean * Assessment * Summary

Acknowledgments Harvard Clinical and Translational Science Center National Alliance for Medical Image Computing NIH U54EB Neuroimage Analysis Center (NAC) National Center for Image-Guided Therapy (NCIGT) Surgical Planning Laboratory, Brigham and Women’s Hospital