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（由申请人提供）：使用不敏感的全球措施，这些措施不能充分捕获肺部的复杂性，并且只能通过重大局部疾病而对肺部疾病进行最小化的改变，不仅会导致对肺病理生理学的不完全了解，而且还需要长期研究大量受试者，以评估新的治疗方法。基于图像的措施，包括评估静态和动态结构和功能，现在被认为是局部亚临床疾病的非常敏感的指标，并且看起来可以更好地描述这些复杂的肺部过程。很容易检测和量化小变化，尤其是使用计算机辅助分析，从而对疾病进展和治疗结果进行了更快，更客观的评估。作为一组相互关联的研究人员，通过成像研究心脏和肺部，我们的图像数据集以及计算任务以及数据的可视化使我们满足了远远超出传统台式计算机或PC簇的资源。在此请求共享仪器授予的请求中，我们概述了共享计算和可视化集群系统的需求，以同时提供超级计算机级的计算性能，即单个过程链接到大型RAM的能力，可以交互性可视化大型数据集，并将两个链接到这些过程中，以链接这些过程以密切容纳大型在线数据存储能力。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促进肺医学，以定量评估肺以及心脏结构和功能。具体目的是：1）克服我们有限的计算资源所施加的计算瓶颈，这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，图像匹配和注册以及四维（4D）（4D）成像和数据分析等方面的进步，以及与广泛的研究社区分享我们的经验和资源。提出的集群系统将使我们能够通过提供计算能力，显示和分析环境来实现首个目标，这对于我们的CT，MRI和MICRO CT分析，计算流体和结构力学研究和4D量化图像评估对大量健康和患病的人类受试者的研究至关重要。第二个目标将通过组建一个基于广泛的内部咨询委员会来协助招募与生物医学工程，健康科学和医学有关的不同学科的新群集用户和合作者。拟议的系统将帮助我们实现并超过我们资助的NIH项目中提出的研究目标。它可以进一步导致对人类肺和心脏的更好描述和理解，及其对疾病，伤害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，这对肺医学的未来至关重要。