NA-MIC National Alliance for Medical Image Computing NA-MIC Ron Kikinis, M.D., Professor of Radiology, Harvard Medical School, Director, Surgical Planning Laboratory, Brigham and Women’s Hospital 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

National Alliance for Medical Image Computing 2 MIC: The Problem More image data, more complexity Medical Image Computing aims to extract relevant information from images Provided by Odonnell, et al. Provided by Kindlmann, et al. Golby, Archip et al.

National Alliance for Medical Image Computing 3 MIC: The Science Algorithm research Software tool development Biomedical research (applications) Courtesy S. Pieper et al.Courtesy P. Black et al.Courtesy R. Whitaker et al.

National Alliance for Medical Image Computing 4 MIC: The Approach Research and development conducted by interdisciplinary teams Pohl et al.

National Alliance for Medical Image Computing The NA-MIC Kit Modular Kit Designed for Research (but compatible with commercial activities) –Applications and toolkits: 3D Slicer, ITK, VTK, KWW, XNAT, GridWizard –Software engineering methodology Multi-platform support (cmake), multi-site development (dart), quality assurance (ctest) –Free Open Source Software (FOSS) Cost effective: Reduced duplication High quality: Openness enables validation, debugging and local control Lowers barriers for scientific exchange Fischl et al.

National Alliance for Medical Image Computing 6 Slicer 3: Software A Visualization Platform –Designed for research –Interface to trackers –Interface to devices: scanners, robots –Modular Analysis routines can be used as plugins or command line executables for batch processing –Draws on Multi-Institution Community Courtesy S. Pieper

National Alliance for Medical Image Computing Slicer Features Multi-Platform Visualization Filtering Registration Segmentation DTI Quantification IGT Capabilities: device interfaces Plug-in architecture Interfaces into informatics frameworks Specialties Involved: Medical Imaging Applied Math Software Engineering Visualization Statistics Computer Vision Neuroscience Robotics User Interface Information Design …

National Alliance for Medical Image Computing B. Davis, S. Barre, Y. Yuan, W. Schroeder, P. Golland, K. Pohl Segmentation

National Alliance for Medical Image Computing Registration Overlay Before: After:

National Alliance for Medical Image Computing Informatics Query Atlas XNAT Courtesy W. Plesniak Courtesy S. Pieper

National Alliance for Medical Image Computing Beyond Medical Exploring Astrocytes Courtesy Brian Smith, Mark Ellisman et al. National Center for Microscopic Imaging Research Detecting Outflows from Young Stars Courtesy of Michelle Borkin, M. Halle, A Goodman et al. Initiative in Innovative Computing, Harvard From Nanometers to Parsecs Velocity DEC RA

National Alliance for Medical Image Computing 12 Image Gallery

National Alliance for Medical Image Computing National Alliance

National Alliance for Medical Image Computing 14 Plus –“Big Science” can be a force multiplier –Development and adoption of best practices –Faster and higher-quality dissemination of new techniques and of new science Minus –Change in culture needed: Replace: –“My research” with –“Our research” NA-MIC is “Big Science”

National Alliance for Medical Image Computing Collaborations Internal: among NA-MIC participants –Algorithms, Engineering, DBP’s External: –Funded through PAR / –Funded by other Mechanisms

National Alliance for Medical Image Computing Driving Biological Projects I –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.

National Alliance for Medical Image Computing Driving Biological Projects II –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).

National Alliance for Medical Image Computing 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 Jan;37(1): 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)

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing External Collaborations Funded through a variety of mechanisms PAR : BRAINS Morphology and Image Analysis –This project is a funded under a Continued Development and Maintenance of Software grant to PIs Vincent Magnotta, Hans Johnson, Jeremy Bockholt, and Nancy Andreasen at the University of Iowa. The goal of this project is to update the BRAINS image analysis software developed at the University of Iowa. Vascular Modeling Toolkit –Collaboration with Luca Antiga of the Mario Negri Institute, Italy. Children's Pediatric Cardiology Collaboration with SCI/SPL/Northeastern –Collaboration with John Triedman, Matt Jolley, Dana Brooks, SCI. NA-MIC Collaboration with NITRC –The NA-MIC Project is working to make NA-MIC neuroimaging software available through the NITRC web site. Supplemental support is helping to create the Slicer3 Loadable Modules project so that slicer plugins can be hosted on NITRC, allowing greater scalability for developers and users of Slicer. NA-MIC Collaboration with NAC –NAC, the neuroimage analysis center, is a national resource center. NAC is relying on the NA-MIC kit for its general software environment. The mission of NAC is to develop novel concepts for the analysis of images of the brain and develop and disseminate tools based on those concepts. NA-MIC Collaboration with NCIGT –The National Center for Image Guided Therapy is using the NA-MIC kit as the platform for its software tool development. NA-MIC Collaboration with the Japanese Research and Development Project on Intelligent Surgical Instruments –Intelligent Surgical Instruments Projects uses Open-source software engineering tools developed by NA-MIC, and leverage it to surgical robotics, funded by the Japanese Government

National Alliance for Medical Image Computing 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 , 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): , 2007.

National Alliance for Medical Image Computing Outreach wiki.na-mic.orgwww.na-mic.org

National Alliance for Medical Image Computing Our didactic vision… Published! Multidisciplinary teaching Integration of theory and practice Translation of concepts into skills through interactive instructor-led training sessions

National Alliance for Medical Image Computing NAMIC Training Portfolio

National Alliance for Medical Image Computing NAMIC Workshops

National Alliance for Medical Image Computing 27 More Information NA-MIC wiki: 3D Slicer