SCIENTIFIC VISUALIZATION

科学可视化

• 批准号: 8172255
• 负责人: CHRISTOPHER R. JOHNSON
• 金额: $ 11.59万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8172255
• 关键词: Algorithms; Architecture; Area; Arts; Behavior; Biomedical Computing; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Data Set; Diffusion; Diffusion Magnetic Resonance Imaging; Engineering; Feedback; Funding; Goals; Grant; Image; Imagery; Institutes; Institution; Ion Transport; Measurement; Measures; Medicine; Methods; Performance; Research; Research Infrastructure; Research Personnel; Resources; Science; Simulate; Software Tools; Source; Structure; Techniques; Technology; Time; Uncertainty; United States National Institutes of Health; Update; Visual; Visualization software; Work; base; bioimaging; biomedical scientist; clinical application; flexibility; image visualization; improved; insight; meetings; prototype; research and development; scientific computing; simulation; tool; vector; voltage

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. Scientific visualization is concerned with helping researchers explore measured or simulated data to gain insight into structures and relationships within the data. The impact of scientific visualization can be seen in all areas of science, medicine, and engineering. A central aim of this core is to bring cutting-edge visualization research and technology to biomedical scientists. The goals of the visualization technology core are to develop and then to implement advanced, efficient, high-performance algorithms and software for visualizing large, spatially distributed and/or time varying data sets. In order to achieve its full potential as an effective scientific tool, visualization must be not just the natural end point of the biomedical computing pipeline but a ubiquitous component of every step within that pipeline: it must enable to user to see the data from raw images to finished simulation and then to visualize the errors and uncertainties that arise from the measurements and computations applied to those data. In order to achieve these goals, we aim to greatly increase the breadth and sophistication of visualization technologies available for biomedical researchers, first by leveraging existing expertise within the Scientific Computing and Imaging Institute, then by carrying out new research directed at such areas as time-dependent image data, flow fields from bioelectric fields and other ion-transport behaviors, diffusion weighted MRI image sets, and data error/uncertainty and by combining such data types into intuitive, quantitative, interactive displays. Three primary visualization goals focus on both research and development: (1) to research new visualization techniques for biomedical applications, (2) to develop visualization tools and software for biomedical visualization based upon state-of-the-art visualization research developed within the Scientific Computing and Imaging Institute and elsewhere, and (3) to leverage third-party visualization software to take advantage of existing software. These aims both reflect the existing expertise of the center's investigators and include substantial components that have originated with the collaborative projects. Such close research ties between the center and its collaborators will improve the quality of the projects by broadening the sources of feedback and intellectual contributions and so help to maximize their impact on the field. Our research will include new work directed at such areas as multi-dimensional transfer function volume visualization of image data, multi-field visualization for bioelectric fields and other ion-transport behaviors, visualization of diffusion weighted MRI, and the creation of new visual representations for data error/uncertainty in experimental and computational data sets. In addition to our research goals, we aim to develop a set of powerful, interactive, quantitative, usable, and integrated visualization tools for biomedical scientists. The utility and impact of the research lie not only in the specific techniques we propose to develop and implement, but also in the way that these techniques will be integrated into BioPSE. Some of the techniques will be tuned to the specific needs of our collaborators or the particular research or clinical application, and many others, such as the multi-dimensional volume rendering, error and uncertainty visualization, and multi-field visualization, will also be appropriate for a broader range of applications. As part of the BioPSE, BioImage, ImageVis3D, TensorVis3D, and Seg3D infrastructures, these techniques will become immediately available to all users of the software for a range of related purposes. Below we give a brief summary of the center's visualization research and development goals: 1. Investigate new diffusion tensor visualization and analysis techniques. 2. Develop and harden state-of-the-art Scientific Computing and Imaging visualization research prototypes in scalar, vector, and tensor field visualization into robust BioPSE components. 3. Supply techniques that support extensive and flexible examination of the quantitative aspects of bioelectric field data, such as voltage gradients and isochrone velocities. 4. Update the architecture of our "BioImage" software package transitioning to the "ImageVis3D" software package. 5. Expand the capabilities of map3d , especially in the areas of time-dependent geometry and multiple-data visualization to meet the needs of the collaborators and other users, especially those in application areas outside of bioelectric fields. 6. Develop visual methods for comparisons of simulation results based upon the proposed visual representation of error and uncertainty research. 7. Examine new file structures to better accommodate the growing size and complexity of biomedical images 8. Investigate methods for visualizing the error and uncertainty produced by measurement, simulation, and visualization techniques.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 科学可视化旨在帮助研究人员探索测量或模拟数据，以深入了解数据中的结构和关系。科学可视化的影响可以在科学、医学和工程的所有领域看到。 该核心的一个中心目标是为生物医学科学家带来尖端的可视化研究和技术。 可视化技术核心的目标是开发并实现先进、高效、高性能的算法和软件，用于可视化大型、空间分布和/或时变数据集。 为了充分发挥其作为有效科学工具的潜力，可视化必须不仅是生物医学计算管道的自然终点，而且是该管道中每一步的无处不在的组成部分：它必须使用户能够看到从原始图像到完成模拟的数据，然后可视化应用于这些数据的测量和计算所产生的误差和不确定性。 为了实现这些目标，我们的目标是大大增加生物医学研究人员可用的可视化技术的广度和复杂性，首先通过利用科学计算和成像研究所内的现有专业知识，然后通过开展新的研究，针对这些领域，如时间依赖的图像数据，生物电场和其他离子传输行为的流场，扩散加权MRI图像集，和数据误差/不确定性，并将这些数据类型组合成直观、定量、交互式显示。 三个主要的可视化目标集中在研究和开发方面：（1）研究新的生物医学应用可视化技术，（2）基于科学计算和成像研究所和其他地方开发的最先进的可视化研究，开发生物医学可视化的可视化工具和软件，以及（3）利用第三方可视化软件来利用现有的软件。这些目标既反映了该中心研究人员的现有专业知识，也包括源于合作项目的实质性组成部分。 该中心与合作者之间的这种密切的研究联系将通过扩大反馈和智力贡献的来源来提高项目的质量，从而有助于最大限度地发挥其对该领域的影响。 我们的研究将包括针对以下领域的新工作：图像数据的多维传递函数体积可视化，生物电场和其他离子传输行为的多场可视化，扩散加权MRI的可视化，以及为实验和计算数据集中的数据错误/不确定性创建新的视觉表示。 除了我们的研究目标，我们的目标是为生物医学科学家开发一套强大的，交互式的，定量的，可用的和集成的可视化工具。该研究的效用和影响不仅在于我们提出开发和实施的具体技术，还在于这些技术将被集成到BioPSE中的方式。其中一些技术将根据我们的合作者或特定研究或临床应用的特定需求进行调整，而其他许多技术，如多维体绘制，误差和不确定性可视化以及多场可视化，也将适用于更广泛的应用。作为BioPSE、BioImage、ImageVis 3D、TensorVis 3D和Seg 3D基础设施的一部分，这些技术将立即提供给软件的所有用户，用于一系列相关目的。下面我们对该中心的可视化研发目标做一个简要的总结： 1.研究新的扩散张量可视化和分析技术。 2.将标量、矢量和张量场可视化中最先进的科学计算和成像可视化研究原型开发并强化为强大的BioPSE组件。 3.提供技术，支持对生物电场数据的定量方面进行广泛和灵活的检查，例如电压梯度和等时线速度。 4.更新我们的“BioImage”软件包的架构，过渡到“ImageVis 3D”软件包。 5.扩展map 3d的功能，特别是在与时间相关的几何和多数据可视化领域，以满足合作者和其他用户的需求，特别是那些在生物电场以外的应用领域。 6.根据提出的误差和不确定性研究的可视化表示，开发可视化方法，用于比较模拟结果。 7.检查新的文件结构，以更好地适应生物医学图像不断增长的大小和复杂性 8.研究测量、模拟和可视化技术产生的误差和不确定性的可视化方法。