CIF: Small: Collaborative Research: Parallel Online Algorithms for Large-Scale MRI

CIF：小型：协作研究：大规模 MRI 的并行在线算法

• 批准号: 1632599
• 负责人: Gesualdo Scutari
• 金额: $ 12.14万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632599&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Parallel

This effort puts forth powerful models capturing the characteristics of big dynamic magnetic resonance imaging (MRI) data, and then offering architectures and algorithms, while revealing fundamental insights into various analytical and implementation trade-offs involved. The proposed framework will extract salient global trends to enable imputation for missing MRI data entries due to imaging speed limitations, and obtain parsimonious representations to process and draw inferences from big pools of MRI data. Leveraging advances in low-rank and sparsity-aware signal processing, learning and optimization, online, parallel, and decentralized algorithms based on matrix and tensor models, will enable streaming analytics of sequential measurements using parallel processors, and tracking dynamically evolving datasets. This project will directly impact high-resolution 3D dynamic MRI technology to improve medical diagnosis and treatment. The developed algorithms and tools will enable technology transfer to benefit a wide population and improve healthcare. Insights gained from this project's large-scale analytics context will also permeate benefits to big data mining, neuroscience, smart grid, and health informatics. Broader impact will be further effected by the integration of the proposed research with an educational plan designed to train the new cadre of next-generation of medical data science professionals, as well as promote cross-fertilization of academic research with health industry needs.

这项工作提出了强大的模型，捕获了大动态磁共振成像（MRI）数据的特征，然后提供架构和算法，同时揭示了对所涉及的各种分析和实施权衡的基本见解。所提出的框架将提取显着的全球趋势，以使由于成像速度限制而缺少MRI数据条目的插图，并获得简约的表示形式，以处理并从MRI数据的大池中提出推断。 利用基于矩阵和张量模型的低率和稀疏感知信号处理，学习和优化，在线，并行和分散算法，将使用并行处理器进行顺序测量的流式传输分析，并可以动态发展的数据集对顺序测量。该项目将直接影响高分辨率3D动态MRI技术，以改善医学诊断和治疗。开发的算法和工具将使技术转移能够受益，并改善医疗保健。从该项目的大规模分析环境中获得的见解也将渗透到大数据挖掘，神经科学，智能电网和健康信息学方面的好处。将拟议的研究与旨在培训新的医学数据科学专业人员的新成员的教育计划的整合以及促进对健康行业需求的学术研究的交叉侵入，将进一步影响更大的影响。