DBP2:UNCFinal:2010

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Overview

The data analysis of neuroimaging data from pediatric populations presents several challenges. There are normal variations in brain shape from infancy to adulthood and normal developmental changes related to tissue maturation. Measurement of cortical thickness is one important way to analyze such developmental tissue changes.

• We developed a novel tool called ARCTIC to perform individual regional cortical thickness analysis and compute cortical thickness maps. ARCTIC’s setup allows for efficient batch processing and grid computing via BatchMake (Kitware Inc). The pipeline entails probabilistic atlas-based automatic tissue segmentation, followed by atlas based lobar parcellation and cortical thickness measurement
• We also developed a novel framework that allows group-wise automatic mesh-based analysis of cortical thickness. Our analysis framework consists of a pipeline of C++ based automated 3D Slicer compatible modules. The approach is divided into four parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module. This framework has been tested on a small pediatric dataset and incorporated in an open source C++ based high-level module called GAMBIT. GAMBIT’s setup allows efficient batch processing, grid computing and quality control. The current research focuses on the use of an average template for correspondence and surface re-sampling, as well as thorough validation of the framework and its application to clinical pediatric studies.

Software

• ARCTIC: Automatic Regional Cortical ThICkness
  • Description:
    • ARCTIC is an end-to-end application allowing individual lobar analysis of cortical thickness
    • Pipeline: performs tissue segmentation, regional atlas deformable registration, cortical thickness measurements, volume information stored in spreadsheets
    • Visualization: white matter and gray matter mesh creation
    • Quality control: optimal QC via 3D Slicer MRML scenes
  • Download:
    • Latest stable version is directly available as an extension in Slicer 3.6.1 release and soon in Slicer 3.6.2
    • Source code, executables and tutorial are available on NITRC
  • Documentation:
    • NITRC wiki page
    • Online documentation within Slicer 3.6
  • Tutorials:
    • ARCTIC tutorials: ‏ [ppt]‏ [pdf]
      • 1st Prize: NAMIC tutorial contest AHM 2009
      • 2nd Prize: NAMIC tutorial contest summer project week 2009

• GAMBIT: Group-wise Automatic Mesh Based analysis of cortIcal Thickness
  • Description:
    • GAMBIT is an end-to-end application allowing allowing group-wise automatic mesh-based analysis of cortical thickness as well as other surface area measurements
    • Pipeline: individual preprocessing pipeline, group-wise particle-based correspondence on inflated genus-zero white matter surfaces, group-wise statistical analysis
    • Visualization: inflated and folded white matter surfaces in correspondence, with cortical thickness and sulcal depth as overlays
    • Quality control: optimal QC via 3D Slicer MRML scenes
  • Download:
    • Latest stable version is available soon as an extension in Slicer 3.6.2
    • Source code, executables and tutorial are available on NITRC
  • Documentation:
    • NITRC wiki page
  • Tutorials:
    • GAMBIT tutorial presentations: [ppt] [pdf]

Listing and short description of the sample data

• Pediatric Brain MRI data available on MIDAS
  • Data of 2 autistic children and 2 normal controls (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.
• Brain Atlases available on MIDAS
  • Average T1-weighted images (with/without skull) are provided with tissue segmentation probability maps (white matter, gray matter, csf, rest), subcortical structures probability maps (amygdala, caudate, hippocampus, pallidus, putamen) and lobar parcellation maps
  • Pediatric atlas
  • Adult atlas
  • Elderly atlas
  • Primate atlas
• Tutorial datasets:
  • Download ARCTIC tutorial dataset
  • Download GAMBIT tutorial dataset

Related pages

• Slicer 3.6 download
• Slicer 3.6 documentation
• Neuro Image Research and Analysis Laboratory, UNC Chapel Hill
• UNC Carolina Institute for Developmental Disabilities

References

• C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Group-wise Automatic Mesh-Based Analysis of Cortical Thickness, accepted to SPIE 2011
• I. Oguz, M. Niethammer, J. Cates, R. Whitaker, T. Fletcher, C. Vachet, and M. Styner, Cortical Correspondence with Probabilistic Fiber Connectivity, Information Processing in Medical Imaging, IPMI 2009, LNCS, 21:651-63
• H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009
• C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract
• Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637– 1640
• J. Cates, P. Fletcher, M. Styner, H. Hazlett, R. Whitaker, Particle-based shape analysis of multi-object complexes, MICCAI 2008, 477-85