National Alliance for Medical Imaging Computing

National Alliance for Medical Imaging Computing

Center URL: http://www.na-mic.org/
PI: Ron Kikinis, M.D.
PI Institution: Brigham and Women's Hospital

NIH Program Officer (PO): Zohara Cohen, Ph.D. (NIBIB)
NIH Lead Science Officer (LSO): Michael Ackerman, Ph.D. (NLM)
NIH Science Officers (SO): German Cavelier (NIMH), Laurence Clark (NCI), Zohara Cohen (NIBIB), Keyvan Farahani (NCI), John Haller (NIBIB), John Matochik (NIAAA), Doug Meinecke (NIMH), Grace Peng (NIBIB) , Karen Skinner (NIDA) , Laurence Stanford (NIDA), Terry Yoo (NLM)

ID: 1-U54-EB005149
 

NA-MIC Summary: The National Alliance for Medical Imaging Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the center is to provide the infrastructure and environment for the development of computational algorithms and open source technologies, and then oversee the training and dissemination of these tools to the medical research community. This world-class software and development environment serves as a foundation for accelerating the development and deployment of computational tools that are readily accessible to the medical research community. The team combines cutting-edge computer vision research (to create medical imaging analysis algorithms) with state of the art software engineering techniques (based on "extreme" programming techniques in a distributed, open-source environment) to enable computational examination of both basic neurosience and neurological disorders. In developing this infrastructure resource, the team is significantly expanding upon proven open systems technology and platforms.

The driving biological projects initially come from the study of schizophrenia, but the methods are applicable to many other diseases. The computational tools and open systems technologies and platforms developed by NA-MIC is initially being used to study anatomical structures and connectivity patterns in the brain, derangements of which have long been thought to play a role in the etiology of schizophrenia. The overall analysis occurs at a range of scales, and across a range of modalities including diffusion MRI, quantitative EGG, and metabolic and receptor PET, and will potentially include microscopic, genomic, and other image data. It applies to image data from individual patients, and to studies executed across large populations. The data is taken from subjects across a wide range of time scales and will ultimately apply to a broad range of diseases in a broad range of organs.