Large-Scale Computing and Visualization for Cardiopulmonary Imaging

心肺成像的大规模计算和可视化

• 批准号: 7388316
• 负责人: CHING-LONG LIN
• 金额: $ 47.36万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-02-15 至 2010-08-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7388316
• 关键词: Advisory Committees; Biomechanics; Biomedical Engineering; Cardiopulmonary; Communities; Complex; Computer Assisted; Computers; Data; Data Analyses; Data Set; Data Storage and Retrieval; Discipline; Disease; Disease Progression; Environment; Evaluation; Four-dimensional; Functional disorder; Funding; Future; Grant; Health Sciences; Heart; Housing; Human; Image; Image Analysis; Imagery; Injury; Lead; Link; Liquid substance; Localized; Lung; Magnetic Resonance Imaging; Measures; Mechanics; Medicine; Morphology; Numbers; Outcome; Performance; Population; Process; Pulmonology; Quantitative Evaluations; Research; Research Personnel; Resources; Structure; Structure of parenchyma of lung; System; Texture; Therapeutic; Time; United States National Institutes of Health; base; computing resources; experience; human subject; instrumentation; response; supercomputer

DESCRIPTION (provided by applicant): The use of insensitive global measures, which do not adequately capture lung complexity and may be only minimally altered by significant local disease, not only foments an incomplete understanding of lung pathophysiology but also results in the need to study large numbers of subjects over long time periods in order to evaluate new treatments. Image-based measures, including evaluation of static and dynamic structure and function, are now recognized as very sensitive indicators of localized subclinical disease and appear to much better describe these complex lung processes. Small changes are easily detected and quantified, particularly using computer-aided analysis, resulting in a more rapid and more objective assessment of disease progression and therapeutic outcomes. As a group of interrelated investigators studying the heart and lungs through imaging, our image data sets along with the computational tasks and the visualization of the data have brought us to the need for resources well beyond conventional desk top computers or PC clusters. In this request for a shared instrumentation grant, we outline the need for a shared computation and visualization cluster system to simultaneously provide for supercomputer-level computational performance, the ability for a single process to link to large RAM, to interactively visualize large data sets, and to link both of these to closely housed large online data storage capacity. Our long-term objective is to advance pulmonary medicine through use of imaging-based, computer-aided approaches for quantitative evaluation of lung as well as heart structure and function. The specific aims are to: 1) overcome the computational bottlenecks imposed by our limited computing resources that hinder our progress on computational pulmonary fluid dynamics and lung tissue biomechanics, texture analysis, image matching and registration, and four-dimensional (4D) imaging and data analyses, and 2) share our experience and resources with a broader research community. The proposed cluster system will allow us to achieve the first aim by providing the computing power, display and analysis environment which is critical for our CT, MRI and Micro CT analyses, the computational fluid and structural mechanics studies, and 4D quantitative image assessment on a large population of healthy and diseased human subjects for study of lung-morphology-pathophysiology relationships. The second aim will be achieved by forming a broadly-based internal advisory committee to assisting with recruitment of new cluster users and collaborators from different disciplines in relation to biomedical engineering, health sciences and medicine. The proposed system will help us to achieve and exceed the research objectives proposed in our funded NIH projects. It can further lead to a better description and understanding of the human lung and heart and their response to disease, injury, and treatment- based not upon single global measures but upon quantifiable regional features, which is fundamentally important to the future of pulmonary medicine.

描述（由申请方提供）：使用不敏感的整体指标，无法充分捕获肺部复杂性，并且可能仅因显著的局部疾病而发生最小程度的改变，不仅导致对肺部病理生理学的不完全理解，还导致需要在长时间内研究大量受试者，以评价新治疗。基于图像的测量，包括静态和动态结构和功能的评估，现在被认为是局部亚临床疾病的非常敏感的指标，并且似乎更好地描述了这些复杂的肺部过程。小的变化很容易检测和量化，特别是使用计算机辅助分析，从而更快速，更客观地评估疾病进展和治疗结果。作为一组通过成像研究心脏和肺部的相互关联的研究人员，我们的图像数据集沿着计算任务和数据的可视化使我们对资源的需求远远超出了传统的台式计算机或PC集群。在这个请求共享仪器补助金，我们概述了需要一个共享的计算和可视化集群系统，同时提供超级计算机级的计算性能，一个单一的进程连接到大型RAM的能力，以交互方式可视化大型数据集，并将这两个紧密结合的大型在线数据存储容量。我们的长期目标是通过使用基于成像的计算机辅助方法来定量评估肺和心脏的结构和功能，从而推动肺医学的发展。具体目标是：1）克服我们有限的计算资源所带来的计算瓶颈，这些计算瓶颈阻碍了我们在计算肺流体动力学和肺组织生物力学、纹理分析、图像匹配和配准以及四维（4D）成像和数据分析方面的进展，2）与更广泛的研究社区分享我们的经验和资源。拟议的集群系统将使我们能够实现第一个目标，通过提供计算能力，显示和分析环境，这是至关重要的，我们的CT，MRI和Micro CT分析，计算流体和结构力学的研究，和4D定量图像评估的健康和患病的人类受试者的大量人口的肺形态病理生理学关系的研究。第二个目标将通过成立一个基础广泛的内部咨询委员会来实现，以协助从生物医学工程、健康科学和医学等不同学科招募新的集群用户和合作者。拟议的系统将帮助我们实现并超越我们资助的NIH项目中提出的研究目标。它可以进一步导致更好地描述和理解人类肺和心脏及其对疾病，损伤和治疗的反应-不是基于单一的全球措施，而是基于可量化的区域特征，这对肺部医学的未来至关重要。