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Pujol S, Gollub R -1- National Alliance for Medical Image Computing Slicer3 Training Tutorial Python Stochastic Tractography Module Julien von Siebenthal,

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Presentation on theme: "Pujol S, Gollub R -1- National Alliance for Medical Image Computing Slicer3 Training Tutorial Python Stochastic Tractography Module Julien von Siebenthal,"— Presentation transcript:

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

2 Pujol S, Gollub R -2- National Alliance for Medical Image Computing Introduction Rausch, von Siebenthal, Bouix, Kubicki The python stochastic tractography module contains the tools necessary to extract, visualize and quantify white matter connections from DTI images. It 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.

3 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 (Slicer 3.5 nightly build as of January 3 rd, 2010) –http://slicer.org/pages/Special:SlicerDownloads Training Dataset (packaged and compressed) –http://www.na-mic.org/Wiki/images/0/01/IJdata.tar.gzhttp://www.na-mic.org/Wiki/images/0/01/IJdata.tar.gz Training Wiki –http://www.na-mic.org/Wiki/index.php/Python_Stochastic_Tractography_Tutorialhttp://www.na-mic.org/Wiki/index.php/Python_Stochastic_Tractography_Tutorial Prerequisite Skills –Loading images into Slicer 3 Terry, von Siebenthal, Bouix, Kubicki Disclaimer It is the responsibility of the user of 3D Slicer to comply with both the terms of the license and with the applicable laws, regulations and rules. Rausch, von Siebenthal, Bouix, Kubicki

4 Pujol S, Gollub R -4- National Alliance for Medical Image Computing Data This course is built upon three datasets from 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, KubickiRausch, von Siebenthal, Bouix, Kubicki

5 Pujol S, Gollub R -5- National Alliance for Medical Image Computing ROI Image Regions of Interest (ROI) are denoted by an integer label. 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, KubickiRausch, von Siebenthal, Bouix, Kubicki

6 Pujol S, Gollub R -6- National Alliance for Medical Image Computing Learning objective Following this tutorial, youll be able to seed nerve fiber bundles and compute a connectivity map using the stochastic tractography module. This map can be used for further analysis. Python Stochastic Tractography Module INPUT: DWI, ROI Images OUTPUT: Connectivity Map Terry, von Siebenthal, Bouix, Kubicki Further Analysis Rausch, von Siebenthal, Bouix, Kubicki

7 Pujol S, Gollub R -7- 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. –You must now close and restart Slicer 3 for these changes to take effect. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

8 Pujol S, Gollub R -8- National Alliance for Medical Image Computing Loading Volumes Click the Load Data folder button in the upper left part of the program. On the dropdown menu that appears, choose Add data Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

9 Pujol S, Gollub R -9- National Alliance for Medical Image Computing Loading images In the Add Data box that appears, Click Add File(s) Search file directory for your dwi nrrd file. In this tutorial, we will be using namic01- dwi_compressed.nhdr Click Open Terry, von Siebenthal, Bouix, Kubicki Click Add File(s) again Search your file directory for ROI file(s). We will use namic01-ROI- corpuscallosum.nhdr Click Open Rausch, von Siebenthal, Bouix, Kubicki

10 Pujol S, Gollub R -10- National Alliance for Medical Image Computing Loading ROI For each image youre loading, select the Centered box For your ROI, select the LabelMap box Terry, von Siebenthal, Bouix, Kubicki If youre using multiple ROIs, you should load those now the same way, by clicking Add File(s), opening the appropriate nrrd, and checking the Centered and LabelMap options – this is optional, and we wont do so at this point in the tutorial Rausch, von Siebenthal, Bouix, Kubicki

11 Pujol S, Gollub R -11- 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, KubickiRausch, von Siebenthal, Bouix, Kubicki

12 Pujol S, Gollub R -12- National Alliance for Medical Image Computing Verifying the ROI 1 st Click the Rings to link views. Set B(ackground) as DWI.nhdr image Set F(oreground) as ROI.nhdr image Use slider to examine ROI and make sure it is over correct area of DWI. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

13 Pujol S, Gollub R -13- National Alliance for Medical Image Computing Verifying the ROI Slide left and right to examine the position of your ROI in the 3D space of the brain. In our case, the tan dot is placed centrally in the corpus callosum, just as we expect. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

14 Pujol S, Gollub R -14- National Alliance for Medical Image Computing White Matter Mask Terry, von Siebenthal, Bouix, Kubicki A white matter mask is used to include only white matter portions of the brain in tractography to save computation time. See an example WM Mask by loading namic01-dwi- compressed.nhdr from the tutorial dataset. If you dont have a mask for your DWI, the next steps will give us the upper and lower thresholds needed by the Stochastic Tractography module to automatically create one. White matter mask overlaid on DWI baseline image Rausch, von Siebenthal, Bouix, Kubicki

15 Pujol S, Gollub R -15- National Alliance for Medical Image Computing Extract DWI Click the Modules drop down tab. Select Diffusion Select Diffusion Tensor Estimation Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

16 Pujol S, Gollub R -16- National Alliance for Medical Image Computing Extract DWI Terry, von Siebenthal, Bouix, Kubicki Click the box next to Input DWI Volume and select namic01-dwi- compressed.nhdr Click the boxes next to Output DTI Volume, Output Baseline Volume, and Otsu Threshold Mask, and for each option select Create New Volume Rausch, von Siebenthal, Bouix, Kubicki

17 Pujol S, Gollub R -17- National Alliance for Medical Image Computing Selecting the Baseline Once this has completed, we will load the baseline for finding our white matter mask thresholds Set B(ackground) as baseline image, which has lowest number of all the images created. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

18 Pujol S, Gollub R -18- 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. Adjust the Window/Level values by moving the sliders to make the grey scale image easily viewable Rausch, von Siebenthal, Bouix, Kubicki

19 Pujol S, Gollub R -19- National Alliance for Medical Image Computing Baseline Intensities Terry, von Siebenthal, Bouix, Kubicki Bright white areas of the ventricles should be excluded Dark areas outside the brain should also be excluded. Moving your mouse over the image, find the range of numbers you want to include inside the brain by looking at the Bg value shown in the corner of your active axis Record the upper and lower thresholds for later use. Rausch, von Siebenthal, Bouix, Kubicki

20 Pujol S, Gollub R -20- National Alliance for Medical Image Computing Launch Stochastic Tractography Click the Modules drop down tab. Select Diffusion. Select Python Stochastic Tractography. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

21 Pujol S, Gollub R -21- National Alliance for Medical Image Computing Input/Output Tab Click Input DWI Volume drop-down menu and select the namic01-dwi- compressed.nhdr Click Input ROI Volume drop-down menu and select the namic01-ROI- corpuscallosum.nhdr Optional: Enter in a second ROI (for filtering) or White Matter Volume Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

22 Pujol S, Gollub R -22- National Alliance for Medical Image Computing Input/Output Tab If you would like not only all the tracts coming from one seed ROI, but rather all the tracts coming from one seed ROI that also go though a second ROI, you should select that ROI as your Input ROI Volume (Region B) Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

23 Pujol S, Gollub R -23- National Alliance for Medical Image Computing Input/Output Tab If you would prefer to use your own previously generated white matter mask, select Input WM Volume and select your mask. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

24 Pujol S, Gollub R -24- National Alliance for Medical Image Computing Smoothing Tab To enable Gaussian Smoothing, click the box next to Enabled so the check mark is displayed Enter in FWHM (Full width at half max in millimeters) numbers for each direction. This is the spread of the gaussian in the x, y, and z directions. The larger the value the more blurring will occur. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

25 Pujol S, Gollub R -25- National Alliance for Medical Image Computing Brain Mask Tab To enable the mask, click the box next to Enabled so the check mark is displayed Enter in Lower and Higher brain threshold numbers (based on baseline intensity values). We will use These are the values you determined by looking at the Bg values on the extracted baseline. A wider threshold will include more of the brain, but may take more processing time Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

26 Pujol S, Gollub R -26- National Alliance for Medical Image Computing IJK/RAS switch Leave this box checked if your scans are in IJK space. We will check the box for the tutorial. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

27 Pujol S, Gollub R -27- National Alliance for Medical Image Computing Diffusion Tensor Tab Check Enabled as well as FA, TRACE, and/or MODE to output anisotropy index images. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

28 Pujol S, Gollub R -28- National Alliance for Medical Image Computing Tractography Tab Set Total Tracts to the number on tracts you want to be seeded from each ROI voxel. Hundreds are needed to ensure a reliable study. For a quick and dirty tutorial example well use 50. Set Maximum Tract Length to the length (in mm). For the tutorial, we use 200mm so that no tracts are longer than the length of the brain. Set Step Size as the distance between each re-estimation of tensors (usually between 0.5 and 1 mm). If this is larger than your voxel size, you can possibly jump over whole slices. In the tutorial, well use 0.8mm. Terry, von Siebenthal, Bouix, Kubicki Check Stopping criteria and enter FA value from 0 to 1 to terminate tracts (e.g. to stop fibers from traveling through the CSF). Normally this is not used in stochastic tractography, and we will not check it in this tutorial. Rausch, von Siebenthal, Bouix, Kubicki

29 Pujol S, Gollub R -29- National Alliance for Medical Image Computing Connectivity Map Tab Choose a Computation Mode. binary (each voxel is counted only once if a fiber passes through it) cumulative (tracts are summed by voxel independently) Well use this setting for the tutorial. weighted (tracts are summed by voxel depending on their length). Length Based only shows tracts meeting length criteria. It is not used in this tutorial. small shows only the shortest 1/3 rd of tracts medium shows only the middle1/3 rd large shows only the longest 1/3 rd Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

30 Pujol S, Gollub R -30- National Alliance for Medical Image Computing Connectivity Map Tab The remaining options only apply to 2-ROI experiments, and so will not be used in the tutorial. Threshold between 0 and 1 will cut out tracts that have a lower fraction of tracts on the connectivity map Tract Offset defines how far back along a tract after its termination the algorithm will search for membership in a second ROI Use spherical ROI vicinity takes a second ROI and expands it spherically. Useful for very small target ROIs. Vicinity sets the size of the spherical expansion. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

31 Pujol S, Gollub R -31- National Alliance for Medical Image Computing Automatic Server Initialisation In Linux and Mac only, this allows the server running the algorithm to automatically be started Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

32 Pujol S, Gollub R -32- National Alliance for Medical Image Computing Running Tractography Now that settings are properly configured, hit Apply to begin tractography. Terry, von Siebenthal, Bouix, Kubicki If a pop-up appears asking to allow external connections from , click Yes. Rausch, von Siebenthal, Bouix, Kubicki

33 Pujol S, Gollub R -33- National Alliance for Medical Image Computing …wait. Wait until computation is completed. Completed! Rausch, von Siebenthal, Bouix, Kubicki

34 Pujol S, Gollub R -34- National Alliance for Medical Image Computing Module Outputs Once tractography is complete, it should output 6 new images brain_### is the mask created by the module tensor_### is the tensor image fa_### is an fa map trace_### is a trace map mode_### is a mode map cmFA_### is a normalized connectivity map, where each voxel has a value from 0 to 1 describing the percentage of the total number of tracts that go through this voxel Rausch, von Siebenthal, Bouix, Kubicki

35 Pujol S, Gollub R -35- National Alliance for Medical Image Computing Connectivity Map The connectivity map is the key output of stochastic tractography, defining a probability of a tract coming from your seed ROI being present at a given point. The image output of stochastic is a grey scale image with values from 0 to 1 describing this probability. Rausch, von Siebenthal, Bouix, Kubicki

36 Pujol S, Gollub R -36- 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 the Editor, or examine it in 3D with the Volume Rendering Module. Terry, von Siebenthal, Bouix, KubickiRausch, von Siebenthal, Bouix, Kubicki

37 Pujol S, Gollub R -37- National Alliance for Medical Image Computing Saving Outputs Go to File at the top right corner of the screen and select Save. Rename the newly created files as you choose and save them in preferred directory. Rausch, von Siebenthal, Bouix, Kubicki

38 Pujol S, Gollub R -38- National Alliance for Medical Image Computing Visualizations Connectivity files have now been created and can be visualized in 2D or 3D with a couple modules in Slicer. Editor VolumeRendering Rausch, von Siebenthal, Bouix, Kubicki

39 Pujol S, Gollub R -39- National Alliance for Medical Image Computing Threshold Label Maps Go to the Editor from the Modules dropdown menu Next to Create Label Map From select cmFA_### Select the Threshold tool Change the lower bound to 0.1 so that only voxels with a 10% chance of connecting or higher will be included Press Apply This label map can be used for label statistics analysis of FA, Mode, etc. Rausch, von Siebenthal, Bouix, Kubicki

40 Pujol S, Gollub R -40- National Alliance for Medical Image Computing Volume Rendering Tab VolumeRendering will allow you to create a 3D model of your probability cloud. Drop down the Modules tab and select Volume Rendering. Under Background Volume, next to Source, select cmFA_###. This will create a probability cloud of the tracts out by stochastic tractography. If you would like to threshold this cloud to only contain connections with >10% chance of connection, under the Rendering tab, select the Volume Property pane, check the Use Thresholding box to enable, and set the lower value to 0.1. Rausch, von Siebenthal, Bouix, Kubicki

41 Pujol S, Gollub R -41- National Alliance for Medical Image Computing Acknowledgements Terry, von Siebenthal, Bouix, Kubicki National Alliance for Medical Image Computing NIH U54 EB Morphometry Biomedical Informatics Research Network NIH U24 RRO21382 Surgical Planning Laboratory (BWH) Psychiatry Neuroimaging Laboratory Marek Kubickis NIH Grant Rausch, von Siebenthal, Bouix, Kubicki

42 Pujol S, Gollub R -42- 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 brain based models are seeded from a corpus callosum ROI from the data of Anna Rotarska-Jagiela at Goethe-Universität. The helix based models are seeded from an arbitrary ROI from the phantom of Sonia Pujol at BWH. Terry, von Siebenthal, Bouix, Kubicki

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

44 Pujol S, Gollub R -44- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

45 Pujol S, Gollub R -45- National Alliance for Medical Image Computing Glyphs Turned on under Volumes Terry, von Siebenthal, Bouix, Kubicki

46 Pujol S, Gollub R -46- National Alliance for Medical Image Computing Visualize glyphs with models Terry, von Siebenthal, Bouix, Kubicki

47 Pujol S, Gollub R -47- National Alliance for Medical Image Computing Thresholded Terry, von Siebenthal, Bouix, Kubicki

48 Pujol S, Gollub R -48- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

49 Pujol S, Gollub R -49- National Alliance for Medical Image Computing Visualize Tensor (FA) Connectivity Terry, von Siebenthal, Bouix, Kubicki

50 Pujol S, Gollub R -50- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

51 Pujol S, Gollub R -51- National Alliance for Medical Image Computing From Helix Phantom with ROI Terry, von Siebenthal, Bouix, Kubicki

52 Pujol S, Gollub R -52- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

53 Pujol S, Gollub R -53- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki

54 Pujol S, Gollub R -54- National Alliance for Medical Image Computing Terry, von Siebenthal, Bouix, Kubicki


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