1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center & UNC-CH Dept of Psychiatry NA-MIC AHM Salt Lake City, UTJan 8, 2009

2. NA-MIC National Alliance for Medical Image Computing http://na-mic.org UNC DBP-2 Team: DBP-2 Co-PI: Heather Cody Hazlett, PhD Co-PI: Joseph Piven, MD CS Programmers: Clement Vachet MS, Cedric Matthieu BA Core 1: Martin Styner, UNC Chapel Hill UNC Algorithm: Ipek Oguz, Nicolas Augier, Marc Niethammer Utah Algorithm: Marcel Prastawa Core 2: Jim Miller, GE Research

3. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Project: Cortical thickness analysis of pediatric brain Project Goals: –Individual and group analysis of regional and local cortical thickness –Creation of an end-to-end application within Slicer3 –Workflow applied to our large pediatric dataset Why is this needed? - Existing tools (e.g. FreeSurfer) are tailored to work with adult brain - Pediatric brain shows more variability in brain shape and maturation (esp. white matter) than adult brain

4. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Regional cortical thickness

5. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Regional Cortical Thickness - Pipeline Overview A Slicer3 high-level module for individual cortical thickness analysis has been developed: ARCTIC (Automatic Regional Cortical ThICkness) Input: raw data (T1-weighted, T2-weighted, PD-weighted images) Three steps in the pipeline: 1. Tissue segmentation 2. Regional atlas deformable registration 3. Cortical Thickness

6. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Regional cortical thickness (ARCTIC) pipeline: Step 1: Tissue segmentation Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme Tool: itkEMS (UNC Slicer3 external module)

7. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Regional cortical thickness (ARCTIC) pipeline: Step 2. Regional atlas deformable registration 2.1 Skull stripping using previously computed tissue segmentation label image Tool: SegPostProcess (UNC Slicer3 external module) 2.2 T1-weighted atlas deformable registration using a B-spline pipeline registration Tool: RegisterImages (Slicer3 module) 2.3 Applying transformation to the parcellation map Tool: ResampleVolume2 (Slicer3 module)

8. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Regional cortical thickness (ARCTIC) pipeline: Step 3. Cortical Thickness Sparse asymmetric local cortical thickness Tool: CortThick (UNC Slicer3 module) Note: All the tools used in the current pipeline are Slicer3 modules, some of them being UNC external modules. The user can thus compute an individual regional cortical thickness analysis by running the 'RegionalCortThickPipeline' module, either within Slicer3 or as a command line.

9. NA-MIC National Alliance for Medical Image Computing http://na-mic.org ARCTIC Pipeline Validation Analysis on a small pediatric dataset: Initial tests have been computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases. N = 16 with Autism, 1 with Dev Delay, 3 Typ Developing Comparison to ‘state of the art’: ARCTIC vs. Freesurfer: We are currently doing a regional statistical analysis using Pearson's correlation coefficient on a dataset that includes ~ 90 cases and for two comparison groups (2 yr-old cases and 4 yr-old cases)

10. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Project Workload Timeline Completed: Workflow for individual analysis (Slicer3 external module using BatchMake) 2 Tutorials: "How to use the UNC modules to compute the regional cortical thickness" and "How to use ARCTIC" In progress: Pediatric atlases available to the community through MIDAS Comparison to FreeSurfer: pearson correlation analysis ARCTIC available to the community through NITRC: executables (UNC external modules for Slicer3), source code (SVN), and Tutorial dataset Future work: Workflow for group analysis (KWWidgets application using BatchMake)

11. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Downloads Executable and tutorial dataset: http://www.nitrc.org/projects/arctic/ Pediatric atlas: http://www.insight- journal.org/midas/item/view/2277

12. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness

13. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local Cortical Thickness - Pipeline Overview Input: Raw T1-weighted, T2-weighted, or PD-weighted images Eleven steps in the pipeline: 7. White matter surface inflation 8. Cortical correspondence 9. Label map creation 10. Cortical thickness 11. Group statistical analysis 1. Tissue segmentation 2. Atlas-based ROI segmentation 3. White matter map creation 4. White matter map post-processing 5. Genus zero white matter map image & surface creation 6. Gray matter map creation

14. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 1: Tissue segmentation Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme Tool: itkEMS (UNC Slicer3 external module)

15. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 2: Atlas-based ROI segmentation: subcortical structures, lateral ventricles, parcellation 2.1 T1-weighted atlas deformable registration B-spline pipeline registration Tool: RegisterImages (Slicer3 module) 2.2 Applying transformations to the structures Tool: ResampleVolume2 (Slicer3 module)

16. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 3: White matter map creation Brainstem and cerebellum extraction Adding subcortical structures (except amygdala & hippocampus) Tool: ImageMath (NITRC module)

17. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 4: White matter map post-processing Largest component computation White matter filling Smoothing: Level set smoothing or weighted average filter Connectivity enforcement (6-connectivity) Tool: SegPostProcessB (Slicer3 external module)

18. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 5: Genus zero white matter map image and surface creation Tool: GenusZeroImageFilter (UNC Slicer3 external module) Step 6: Gray matter map creation Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem) Tool: ImageMath

19. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 7: White matter surface inflation Iterative smoothing using relaxation operator (considering average vertex) and L2 norm of the mean curvature as a stopping criterion Fixing is necessary: remove vertices that have too high curvature (extremities) Tool: MeshInflation (UNC Slicer3 external module)

20. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 8: Cortical correspondence Correspondence on inflated surface using particle system Tool: ParticleCorrespondence (UNC Slicer3 external module) Step 9: Label map creation Label map creation for cortical thickness computation (WM + GM + "CSF") Tool: ImageMath

21. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Local cortical thickness pipeline: Step 10: Cortical thickness Asymmetric local cortical thickness or Laplacian cortical thickness Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules) Step 11: Group statistical analysis Tool: QDEC Slicer module or StatNonParamPDM

22. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Pipeline validation Analysis on a small pediatric dataset: (to be done) Tests will be computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases. N = 16 with Autism, 1 with Dev Delay, 3 Typ Developing Comparison to ‘state of the art’: (ongoing) Pipeline vs. Freesurfer: We are currently doing a regional statistical analysis using Pearson's correlation coefficient on a dataset that includes ~ 90 cases and for two comparison groups (2 yr-old cases and 4 yr-old cases)

23. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Project Workload Timeline In progress Cortical surface inflation: module in progress Mesh needs to be fixed at some location to have a correct inflation Future work Workflow for individual analysis as a Slicer3 high- level module using BatchMake Workflow for group analysis

24. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Joe Piven, MD Guido Gerig, PhD Martin Styner, PhD Clement Vachet, MS Cedric Matthieu, BA Rachel Smith, BA Mike Graves, MChE Sarah Peterson, BA Matt Mosconi, PhD Parent grant funded by the National Institutes of Health Contributors: NA-MIC Team Jim Miller Ipek Oguz Nicolas Augier Marc Niethammer Brad Davis