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NA-MIC

NA-MIC. Ron Kikinis, M.D., Professor of Radiology, Harvard Medical School, Director, Surgical Planning Laboratory, Brigham and Women’s Hospital kikinis@bwh.harvard.edu. Founding Director, Surgical Planning Laboratory, Brigham and Women’s Hospital

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NA-MIC

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  1. NA-MIC Ron Kikinis, M.D., Professor of Radiology, Harvard Medical School, Director, Surgical Planning Laboratory, Brigham and Women’s Hospital kikinis@bwh.harvard.edu Founding Director, Surgical Planning Laboratory, Brigham and Women’s Hospital Principal Investigator, the National Alliance for Medical Image Computing, and the Neuroimage Analysis Center Research Director, National Center for Image Guided Therapy

  2. Medical Image Computing • More image data, more complexity • MIC: Extract relevant information • Algorithms, Tools, Applications Golby, Archip et al. Provided by Odonnell, et al. Provided by Kindlmann, et al.

  3. Vision • To develop a platform for analyzing biomedical images • Both algorithm science and software technology • Enable research (algorithm and biomedical) and commercial use • Portable, modular and expandable

  4. National Alliance My research-our research http://wiki.na-mic.org/Wiki/index.php/Leadership:Main

  5. NA-MIC Algorithms • Shape representation and analysis • Multiscale/wavelets • Ensemble-based correspondences & multimodal data • Hypothesis testing • Diffusion MRI • Filtering, registration, and tensor estimation • Stochastic tractography and optimal paths • Tract clustering and atlases • Hypothesis testing and validation • Segmentation/classification • Shape priors and posterior estimation • Statistical atlases • PDEs and efficient numerical implementations • Functional imaging • Multimodal registration and distortion correction • Statistical analysis, regularization, and networks

  6. The NA-MIC Kit Modular set of tools and applications • Interoperable, tested, maintainable, multi-platform components • 3D Slicer, ITK, VTK, XNAT etc. Free Open Source Software (FOSS) • Cost effective: Reduced duplication • High quality: Openness enables validation, debugging and local control • Lowers barriers for scientific exchange • 3D Slicer: A Platform for Delivering MIC Technologies to Biomedical Scientist

  7. Driving Biological Projects I • 2004-2007 • Dartmouth/Indiana • Examines DW-MRI and fMRI data in patients with schizophrenia to determine association with brain activation during memory tasks • Harvard • Uses structural MRI, diffusion-weighted MRI, and fMRI to study the neural bases of schizophrenia and related psychiatric disorders. • UCI • Investigate the connections between neuroanatomy and schizophrenia. • Toronto • Investigate genetic links in schizophrenia.

  8. Driving Biological Projects II • 2007-2010 • Harvard • Collect high-res DTI, structural and fMRI data from patients with VCFS and use NAMIC tools to analyse the data. • JHU / Queens • Developing novel systems and procedures for prostate cancer interventions, such as biopsy and needle-based local therapies. • Mind • Evaluation of existing tools and the development new tools within SLICER for the time series analysis of brain lesions in lupus. • UNC • Longitudinal study of early brain development by cortical thickness in autistic children and controls (2 years with follow-up at 4 years).

  9. External Collaborations • 1 Projects funded by "Collaborations with NCBC PAR" • 1.1 PAR-05-063: R01EB005973 Automated FE Mesh Development • 1.2 PAR-07-249: R01AA016748 Measuring Alcohol and Stress Interaction with Structural and Perfusion MRI • 1.3 PAR-05-063: R01CA124377 An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors • 2 Additional External Collaborations • 2.1 PAR-05-057: BRAINS Morphology and Image Analysis • 2.2 Vascular Modeling Toolkit Collaboration • 2.3 Children's Pediatric Cardiology Collaboration with SCI/SPL/Northeastern • 2.4 NA-MIC Collaboration with NITRC • 2.5 NA-MIC Collaboration with NAC • 2.6 NA-MIC Collaboration with NCIGT • 2.7 NA-MIC Collaboration with Research and Development Project on Intelligent Surgical Instruments • 2.8 Real Time Computer Simulation of Human Soft Organ Deformation for Computer Assisted Surgery • 2.9 Real-Time Computing for Image Guided Neurosurgery • 2.10 NA-MIC support for Harvard CTSC Translational Imaging Consortium

  10. Outreach 2007 2006 2005 Project Weeks Self-Training Websites Hands-on Workshops

  11. Links NA-MIC website: www.na-mic.org Slicer website: www.slicer.org

  12. Additional Materials

  13. Patient-Specific Finite Element Model Development • Iowa: Kiran H. Shivanna, Vincent A. Magnotta, Nicole M. Grosland, • NA-MIC: Steve Pieper, Curt Lisle • Automate the generation of high quality hexahedral meshes • Inclusion of soft tissues such as cartilage • Automated Segmentation • Validation • Published / Accepted • Devries NA, Gassman EE, Kallemeyn NA, Shivanna KH, Magnotta VA, Grosland NM. Validation of phalanx bone three-dimensional surface segmentation from computed tomography images using laser scanning. Skeletal Radiol. 2008 Jan;37(1):35-42. Epub 2007 Oct 25. • Gassman EE, Powell SM, Kallemeyn NA, DeVries NA, Shivanna KH, Magnotta VA, Ramme AJ, Adams BD, Grosland NM, Automated Bony Region Identification Using Artificial Neural Networks: Reliability and Validation Measurements. Skeletal Radiology (accepted / online). • Grant funding NIH • R21 (EB001501) • R01 (EB005973)

  14. Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI in Monkeys • Virginia Tech: Ch. Wyatt, Wake Forrest: J. Daunais • NA-MIC: Kilian Pohl, W. Wells • Implement and validate algorithms for: • brain extraction • white-gray matter segmentation • subcortical structure segmentation • Grant funding NIH • R01AA016748

  15. NA-MIC NCBC Collaboration:An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors • Georgetown: Enrique Campos-Nanez, Patrick (Peng) Cheng, Kevin Cleary, Ziv Yaniv • NA-MIC: Nobuhiko Hata • Implement and validate algorithms for: • brain extraction • white-gray matter segmentation • subcortical structure segmentation • Grant funding NIH • R01CA124377

  16. Example: Non-rigid Deformation CWM BWH Toward real-time image guided neurosurgery using distributed and grid computing (with Andriy Fedorov, Andriy Kot, Neculai Archip, Peter Black, Olivier Clatz, Alexandra Golby, Ron Kikinis, and Simon K. Warfield. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, Tampa, Florida, November 11- 17, 2006. (*) Non-rigid alignment of preoperative MRI, fMRI, DT-MRI, with intra-operative MRI for enhanced visualization and navigation In image-guided neurosurgery(with N. Archip, O. Clatz, A. Fedorov, A. Kot, S. Whalen, D. Kacher, F. Jolesz, A. Golby, P.Black, S. Warfield) in NeuroImage, 35(2):609-624, 2007.

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