1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 2 Medical Image Computing More image data, more complexity MIC: Extract relevant information Algorithms, Tools, Applications Provided by Odonnell, et al. Provided by Kindlmann, et al. Golby, Archip et al.

3. National Alliance for Medical Image Computing http://na-mic.org 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 for Medical Image Computing http://na-mic.org National Alliance http://wiki.na-mic.org/Wiki/index.php/Leadership:Main My research-  our research

5. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org Outreach Websites Self-Training 2006 2005 2007 Hands-on Workshops Project Weeks

11. National Alliance for Medical Image Computing http://na-mic.org Links NA-MIC website: www.na-mic.orgwww.na-mic.org Slicer website: www.slicer.orgwww.slicer.org

12. National Alliance for Medical Image Computing http://na-mic.org Additional Materials

13. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org 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. National Alliance for Medical Image Computing http://na-mic.org BWH CWM 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. Example: Non-rigid Deformation (*) 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.