NATIONAL ALLIANCE-MEDICAL IMAGING COMPUTING
国家医学影像计算联盟
基本信息
- 批准号:7955628
- 负责人:
- 金额:$ 0.34万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-08-01 至 2010-07-31
- 项目状态:已结题
- 来源:
- 关键词:AlgorithmsArtsAutomobile DrivingBiologicalBrainCommunitiesComputational algorithmComputer Retrieval of Information on Scientific Projects DatabaseComputer Vision SystemsComputersDataDevelopmentDiffusion Magnetic Resonance ImagingDiseaseEnvironmentEtiologyFoundationsFundingGenomicsGrantImageImage AnalysisImageryIndividualInstitutionMedicalMedical ImagingMedical ResearchMetabolicMethodsMicroscopicModalityModelingNeurosciencesOrganPatientsPatternPlayPopulationPositron-Emission TomographyResearchResearch InfrastructureResearch PersonnelResourcesRoleSchizophreniaScientistSoftware EngineeringSourceStructureSystemTechniquesTechnologyTimeTrainingUnited States National Institutes of HealthVision researchbasecomputational anatomycomputerized toolseggnervous system disorderopen sourceprogramsreceptorsoftware developmenttool
项目摘要
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.
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.
该副本是利用众多研究子项目之一
由NIH/NCRR资助的中心赠款提供的资源。子弹和
调查员(PI)可能已经从其他NIH来源获得了主要资金,
因此可以在其他清晰的条目中代表。列出的机构是
对于中心,这不一定是调查员的机构。
全国医学成像计算联盟(NAMIC)是一个多机构的,跨学科的计算机科学家,软件工程师和医学调查人员的团队,他们开发了用于分析和可视化医学图像数据的计算工具。该中心的目的是为开发计算算法和开源技术提供基础设施和环境,然后监督这些工具向医学研究界的培训和传播。这个世界一流的软件和开发环境是加速医学研究界很容易访问的计算工具的开发和部署的基础。该团队将最先进的计算机视觉研究(创建医学成像分析算法)与最先进的软件工程技术(基于分布式开源环境中的“极端”编程技术)相结合,以启用基本神经科学和神经疾病的计算检查。在开发此基础架构资源时,团队将在经过验证的开放系统技术和平台上大大扩展。驾驶生物学项目最初将来自精神分裂症的研究,但这些方法将适用于许多其他疾病。 NAMIC开发的计算工具和开放系统技术和平台最初将用于研究大脑中的解剖结构和连通性模式,而这些扰动长期以来一直被认为在精神分裂症的病因学中起着作用。总体分析将在一系列尺度上进行,并将发生在一系列范围内,包括扩散MRI,定量卵,代谢和受体PET,但可能包括微观,基因组和其他图像数据。它将适用于来自个别患者的图像数据,并适用于在大量人群中进行的研究。数据将从广泛的时间尺度的受试者中获取,并最终适用于广泛的器官中的广泛疾病。
项目成果
期刊论文数量(0)
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Ron Kikinis其他文献
Ron Kikinis的其他文献
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