Pujol S, Gollub R -1- National Alliance for Medical Image Computing Slicer3 Training Tutorial Python Stochastic Tractography Module Doug Terry, Julien von Siebenthal, Sylvain Bouix, Marek Kubicki Psychiatry Neuroimaging Lab Slicer3 Training Compendium

Pujol S, Gollub R -2- National Alliance for Medical Image Computing Introduction Terry, von Siebenthal, Bouix, Kubicki The python stochastic tractography module seeds nerve fiber bundles from regions of interest (ROIs) based on DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography. By incorporating uncertainty due to fiber crossings, imaging noise and resolution, stochastic tractography can robustly extract fiber bundles when streamline tractography cannot. The tracts generated by the stochastic tractography can be used to generate a connectivity probability image, which can be used to study connectivity between different regions of the brain.

Pujol S, Gollub R -3- National Alliance for Medical Image Computing Materials and Req.’s This course requires the installation of the Slicer3 software and training dataset accessible at the following locations: Slicer 3 Software Training Dataset (packaged and compressed) Prerequisite Skills –Loading images into Slicer 3 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. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -4- National Alliance for Medical Image Computing Other Requirements In order to complete Python Stochastic Tractography, you are going to need: –Python –Python NumPy –Slicer 3 Build (for windows) –Slicer 3 Lib (for windows) Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -5- National Alliance for Medical Image Computing Data This course is built upon three datasets of a single healthy subject brain: DWI (Nrrd)Optional: Whitematter Image (Nrrd) ROI Image (Nrrd) shown overlaid on baseline DWI, tan pixel is seed ROI Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -6- National Alliance for Medical Image Computing ROI Image Regions of Interest (ROI) denoted by an integer label. Optionally, other regions can be specified to filter the tracts An ROI image overlaid on FA image, regions with zero label are transparent. The ROI image is an integer image. Each value defines a unique region. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -7- National Alliance for Medical Image Computing Learning objective Following this tutorial, you’ll be able to seed nerve fiber bundles and compute a connectivity map using the stochastic tractography module. Python Stochastic Tractography Module Generate Connectivity Map DWI, ROI Images Connectivity Map Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -8- National Alliance for Medical Image Computing Launching Python For most versions of Slicer 3 at this type, Python has not been integrated and needs to be launched manually. You can do that by: –In command line, navigate to the Slicer3-Build Folder –Type: “ cd python” – Type: “ python PyPipelineServer.py” –Then, launch Slicer 3. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -9- National Alliance for Medical Image Computing Enabling Daemons Also note, if this is your first time using a python module, you must change one slicer setting. –On the top menu, click “View” –Go down to “Application Settings” and select it. –Click the “Slicer Settings” Tab –Click the box to “Slicer Daemon” to enable Slicer Daemons. –Then, close Slicer and your Python connection for changes to take effect. Once they are closed, you may start over by re- launching Python & Slicer 3. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -10- National Alliance for Medical Image Computing Loading Volumes Click the pull-down menu next to Modules, and select Volumes tab. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -11- National Alliance for Medical Image Computing Loading DWI Click Select Volume File Search file directory for any dwi.nhdr. In this tutorial, we will be using either: Namic01- dwi_compressed.nhdr or cayet.nhdr (external dwi provided by Anna Rotarska-Jagiela at Goethe-Universität) Click Open Click Apply Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -12- National Alliance for Medical Image Computing Loading ROI Click Select Volume File Search file directory for the ROI.nhdr you’d like to use. We’re using namic01-ROI- leftcingulum.nhdr or Cayet_ROI_01.nhdr Click Open Click Apply Optional: You can also load a second ROI or white matter mask the same way. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -13- National Alliance for Medical Image Computing Different Data Sets This module was created on the framework that it could be used by anyone with a diffusion weighted image without external preprocessing steps, therefore making it available to anyone interested in probabilistic tractography. As long as the data can be loaded into Slicer, it can undergo Python Stochastic Tractography. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -14- National Alliance for Medical Image Computing Verifying the ROI Click the Rings to link views. Set B(ackground) as DWI.nhdr image Set F(oreground) as ROI.nhdr image Use toggles to make sure ROI is over correct area of DWI. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -15- National Alliance for Medical Image Computing Extract DWI Click the Modules drop down tab. Select Python Modules Select Python Extract DWI Volume Click next to “Input DWI Volume” and select the DWI.nhdr Click Apply Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -16- National Alliance for Medical Image Computing Selecting the Baseline Set B(ackground) as baseline image, which has lowest number of all the images created. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -17- National Alliance for Medical Image Computing Thresholding Baseline Terry, von Siebenthal, Bouix, Kubicki Click the Modules drop down tab and select Volumes. Set the Active Volume to the new Baseline Image. You are going to want to play with the Window/Level and Threshold tabs by moving the toggles to determine what intensities you want to include in the Otsu- like mask and the White Matter Mask. This step is for your own knowledge… you DO NOT need to actually threshold right now, but know the threshold values to input later.

Pujol S, Gollub R -18- National Alliance for Medical Image Computing Baseline Intensities Terry, von Siebenthal, Bouix, Kubicki Looking at the “Bg” values can also help you determine thresholds. Rememeber these numbers for later. For Otsu-like mask, you may want to exclude values outside of brain. For White matter mask, you may want to exclude ventricle/CSF values.

Pujol S, Gollub R -19- National Alliance for Medical Image Computing Launch Stochastic Tractography Click the Modules drop down tab. Select Python Modules. Select Python Stochastic Tractography. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -20- National Alliance for Medical Image Computing Input/Output Tab Click the arrow to the left of IO to drop down the Input/Output menu. Click Input DWI Volume drop-down menu and select the dwi.nhdr volume you’d like to input. Click Input ROI Volume drop-down menu and select the ROI.nhdr volume you’d like to input for seeding. Optional: Enter in a second ROI (for filtering) or White Matter Volume Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -21- National Alliance for Medical Image Computing Smoothing Tab Click the arrow next to Smoothing Parameters to drop down the Smoothing Tab To enable Smoothing, click the box next to Enabled so the check mark is displayed Enter in FWHM number (the larger the number, the more smoothing). Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -22- National Alliance for Medical Image Computing Otsu-like Mask Tab Click the arrow next to Otsu-like Mask Parameters to drop down the Tab To enable the mask, click the box next to Enabled so the check mark is displayed Enter in Inferior and Superior Otsu-like threshold numbers (based on T2 intensity values). Again, look at baseline intensity values to determine this. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -23- National Alliance for Medical Image Computing White Matter Mask Tab Click the arrow next to White Matter Mask Parameters to drop down the Tab. To enable the mask, click the box next to Enabled so the check mark is displayed. Enter Inferior and Superior WM threshold numbers (again, based on baseline intensity values). However, if you have inputted a white matter mask, leave this unchecked. To enable Artifacts Removal, click the box next to Enabled. Enter Inferior and Superior artifacts threshold numbers to patch voxels into WM Mask where FA values are high (also based on FA intensity values). Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -24- National Alliance for Medical Image Computing Tensor Parameters Tab Click the arrow to the left of Tensor Parameters to drop down the menu. Leave Baseline at 0, seeing how image number 0 is probably the baseline image. Check FA, TRACE, and/or MODE to output anisotropy indicies. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -25- National Alliance for Medical Image Computing Stochastic Tractography Tab Click the arrow to the left of Stochastic Tractography to drop down the menu. Set Total Tracts to the number on tracts you want to be seeded from each ROI voxel. Set Maximum Tract Length to the length (in mm). Set Step Size as the distance between each re-estimation of tensors (usually between 0.5 and 1 mm). Check Stopping criteria and enter FA value to terminate tracts (e.g. to stop fibers from traveling through the CSF). Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -26- National Alliance for Medical Image Computing Connectivity Map Tab Click the arrow to the left of Connectivity Map Parameters to drop down the menu. Check to Enable Connectivity Map. Choose rough (each voxel is counted only once if at least one fiber pass through it), cumulative (tracts are summed by voxel independently), or discriminative (tracts are summed by voxel depending on their length). Check to Enable Length Based seeding, where only certain tracts are displayed. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -27- National Alliance for Medical Image Computing Running Tractography Click Apply to Start Stochastic Tractography. Click Yes on the pop-up to allow external connections. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -28- National Alliance for Medical Image Computing …wait. Wait until computation is completed. Completed! Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -29- National Alliance for Medical Image Computing Turn on 3D View Turn on 3D View by clicking on closed eye on each viewing axis. Terry, von Siebenthal, Bouix, Kubicki Set “Bg” to the newly created “cm_#####”

Pujol S, Gollub R -30- National Alliance for Medical Image Computing Threshold Indication Scroll over the connectivity map to see its intensity values under “Bg”. You can threshold the connectivity map in Grayscale Model Maker. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -31- National Alliance for Medical Image Computing Grayscale Model Maker Drop down the Modules tab and select Grayscale Model Maker. Keep Parameter set as Greyscale Model maker. Set Input Volume to cm_### from tractography. Set Threshold to number you decided on from previous step. Select Smooth and Decimate numbers, or leave them as the default settings. Check Split Normals or Point Normals if you want. Hit Apply. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -32- National Alliance for Medical Image Computing Models Tab Drop down the Modules tab and select Models. Use module to customize the way you’d like your connectivity output to look. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -33- National Alliance for Medical Image Computing Visualize Results Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -34- National Alliance for Medical Image Computing Acknowledgements Terry, von Siebenthal, Bouix, Kubicki National Alliance for Medical Image Computing NIH U54EB Morphometry Biomedical Informatics Research Network NIH U24RRO21382 Surgical Planning Laboratory (BWH) Psychiatry Neuroimaging Laboratory Marek Kubicki’s and Sylvain Bouix’s NIH Grants

Pujol S, Gollub R -35- National Alliance for Medical Image Computing Appendix The following images have been generated from either Model Maker or Volume Rendering and modified under Models and Volumes just to show you the many things you can do with your Python Stochastic Tractography Output. The models you are about to see are seeded from a corpus callosum ROI from the data of Anna Rotarska-Jagiela at Goethe-Universität. Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -36- National Alliance for Medical Image Computing Visualize Model Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -37- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -38- National Alliance for Medical Image Computing Glyphs Turned on under Volumes Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -39- National Alliance for Medical Image Computing Visualize glyphs with models Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -40- National Alliance for Medical Image Computing Thresholded Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -41- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -42- National Alliance for Medical Image Computing Visualize Tensor (FA) Connectivity Terry, von Siebenthal, Bouix, Kubicki

Pujol S, Gollub R -43- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki