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

CIF：协作研究：大规模 MRI 的并行在线算法

基本信息

批准号：
1514056
负责人：
Georgios Giannakis
金额：
$ 16.49万
依托单位：
University of Minnesota-Twin Cities
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=1514056&HistoricalAwards=false
关键词：
CIF Collaborative Research Parallel Online

项目摘要

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 benefit 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，捕捉大动态磁共振成像（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现权衡的基本见解。拟议的框架将提取显著的全球趋势，以填补由于成像速度限制而缺失的MRI数据条目，并获得简约的表示，以处理和从大量MRI数据中得出推论。利用低秩和稀疏感知信号处理、学习和优化、基于矩阵和张量模型的在线、并行和分散算法的进步，将实现使用并行处理器对顺序测量的流式分析，并跟踪动态演变的数据集。该项目将直接影响高分辨率3D动态MRI技术，以改善医疗诊断和治疗。开发的算法和工具将使技术转让能够使广大人群受益并改善医疗保健。从该项目的大规模分析背景中获得的见解也将有利于大数据挖掘，神经科学，智能电网和健康信息学。更广泛的影响将通过将拟议的研究与旨在培养下一代医疗数据科学专业人员的新干部的教育计划相结合来进一步实现，并促进学术研究与健康行业需求的相互促进。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Topology Identification and Learning Over Graphs: Accounting for Nonlinearities and Dynamics

DOI：
10.1109/jproc.2018.2804318
发表时间：
2018-05-01
期刊：
PROCEEDINGS OF THE IEEE
影响因子：
20.6
作者：
Giannakis, Georgios B.;Shen, Yanning;Karanikolas, Georgios Vasileios
通讯作者：
Karanikolas, Georgios Vasileios

Reinforcement Learning for 5G Caching with Dynamic Cost

DOI：
10.1109/icassp.2018.8462673
发表时间：
2018-04
期刊：
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
A. Sadeghi;Fatemeh Sheikholeslami;A. Marques;G. Giannakis
通讯作者：
A. Sadeghi;Fatemeh Sheikholeslami;A. Marques;G. Giannakis

Active sampling for graph-aware classification

DOI：
10.1109/globalsip.2017.8309039
发表时间：
2017-11
期刊：
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
影响因子：
0
作者：
Dimitris Berberidis;G. Giannakis
通讯作者：
Dimitris Berberidis;G. Giannakis

Multi-kernel Change Detection for Dynamic Functional Connectivity Graphs

动态功能连接图的多内核变化检测