This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Project Summary Grant Number U54EB005149 Project Start: 17-SEP-2004 Project End: 31-JUL-2009 The National Alliance for Medical Imaging Computing (NAMIC) 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 neuroscience and neurological disorders. In developing this infrastructure resource, the team will significantly expand upon proven open systems technology and platforms. The driving biological projects will come initially from the study of schizophrenia, but the methods will be applicable to many other diseases. The computational tools and open systems technologies and platforms developed by NAMIC will initially be 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 will occur at a range of scales, and will occur across a range of modalities including diffusion MRI, quantitative EGG, and metabolic and receptor PET, but potentially including microscopic, genomic, and other image data. It will apply to image data from individual patients, and to studies executed across large populations. The data will be taken from subjects across a wide range of time scales and ultimately apply to a broad range of diseases in a broad range of organs. Benefit to NCIGT NCIGT is leveraging the open source software methodology and packages developed by NA-MIC to create the """"""""Slicer IGT"""""""" software to support image-guided therapy. In addition, NCIGT is also adopting the extensive training and dissemination methodology developed by NA-MIC. By adopting and extending the software development and training best practices of NA-MIC, NCIGT has been able to accelerate its effort by several person-years. Benefit to Project While adding IGT extensions to the open source software Slicer, the NCIGT team is providing features in Slicer that are of use beyond image-guided therapy, and of benefit to the NA-MIC user community.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Cooperative Agreements (U41)
Project #
5U41RR019703-05
Application #
7960880
Study Section
Special Emphasis Panel (ZRG1-SBIB-L (40))
Project Start
2009-08-01
Project End
2010-07-31
Budget Start
2009-08-01
Budget End
2010-07-31
Support Year
5
Fiscal Year
2009
Total Cost
$54,793
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Schmidt, Ehud J; Halperin, Henry R (2018) MRI use for atrial tissue characterization in arrhythmias and for EP procedure guidance. Int J Cardiovasc Imaging 34:81-95
George, E; Liacouras, P; Lee, T C et al. (2017) 3D-Printed Patient-Specific Models for CT- and MRI-Guided Procedure Planning. AJNR Am J Neuroradiol 38:E46-E47
Mitsouras, Dimitris; Lee, Thomas C; Liacouras, Peter et al. (2017) Three-dimensional printing of MRI-visible phantoms and MR image-guided therapy simulation. Magn Reson Med 77:613-622
Guenette, Jeffrey P; Himes, Nathan; Giannopoulos, Andreas A et al. (2016) Computer-Based Vertebral Tumor Cryoablation Planning and Procedure Simulation Involving Two Cases Using MRI-Visible 3D Printing and Advanced Visualization. AJR Am J Roentgenol 207:1128-1131
Mitsouras, Dimitris; Mulkern, Robert V; Maier, Stephan E (2016) Multicomponent T2 relaxation studies of the avian egg. Magn Reson Med 75:2156-64
Li, Mao; Miller, Karol; Joldes, Grand Roman et al. (2016) Biomechanical model for computing deformations for whole-body image registration: A meshless approach. Int J Numer Method Biomed Eng 32:
Schmidt, Ehud J; Watkins, Ronald D; Zviman, Menekhem M et al. (2016) A Magnetic Resonance Imaging-Conditional External Cardiac Defibrillator for Resuscitation Within the Magnetic Resonance Imaging Scanner Bore. Circ Cardiovasc Imaging 9:
Patil, Vaibhav; Gupta, Rajiv; San José Estépar, Raúl et al. (2015) Smart stylet: the development and use of a bedside external ventricular drain image-guidance system. Stereotact Funct Neurosurg 93:50-8
Garlapati, Revanth Reddy; Mostayed, Ahmed; Joldes, Grand Roman et al. (2015) Towards measuring neuroimage misalignment. Comput Biol Med 64:12-23
Lu, Yi; Yeung, Cecil; Radmanesh, Alireza et al. (2015) Comparative effectiveness of frame-based, frameless, and intraoperative magnetic resonance imaging-guided brain biopsy techniques. World Neurosurg 83:261-8

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