CAREER: Compressed Sensing Magnetic Resonance Imaging

职业：压缩传感磁共振成像

• 批准号: 1265612
• 负责人: Leslie Ying
• 金额: $ 24.73万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265612&HistoricalAwards=false
• 关键词: CAREER; Compressed; Sensing; Magnetic; Resonance

0846514L. YingThe research objective of this CAREER proposal is to apply compressed sensing, a new theoretical framework, to magnetic resonance imaging (MRI) to revolutionize its imaging speed and enable real-time imaging. The proposed research investigates various issues in transforming the new mathematical theory into MRI practice and explores various ideas to address these issues. Specifically, to meet the conditions required by the compressed sensing theory, novel non-adaptive, non-Fourier encoding and sampling techniques will be developed, novel sparse transformations will be discovered to represent MR images most efficiently, and efficient and robust nonlinear reconstruction algorithms will also be developed to recover the images from sparsely sampled data. Integration with the existing fast MRI techniques for maximal acceleration will also be studied. Furthermore, the proposed research will evaluate performance by deriving theoretical bounds on speed enhancements and carrying out simulation and experiments. Applications of the developed techniques will be explored for various practical MR imaging problems. The educational objective of this proposal is to develop an effective educational framework for systematic delivery of both basic knowledge and new discoveries in biomedical imaging to a diverse body of students, and create an infrastructure for resource sharing. The proposed educational plan features a new biomedical imaging curriculum, activities to integrate research and education with special efforts to increase women and minority in engineering, and an open platform to disseminate results and share resources in image reconstruction.

0846514L。 Ying这个CAREER提案的研究目标是将压缩感知这一新的理论框架应用于磁共振成像（MRI），彻底改变其成像速度并实现实时成像。拟议的研究调查了将新数学理论转化为 MRI 实践的各种问题，并探索了解决这些问题的各种想法。具体来说，为了满足压缩感知理论所需的条件，将开发新颖的非自适应、非傅里叶编码和采样技术，将发现新颖的稀疏变换来最有效地表示MR图像，并且还将开发高效且鲁棒的非线性重建算法以从稀疏采样数据中恢复图像。还将研究与现有快速 MRI 技术的集成以获得最大加速度。此外，拟议的研究将通过推导速度增强的理论界限并进行模拟和实验来评估性能。将探索所开发技术的应用，以解决各种实际的磁共振成像问题。该提案的教育目标是开发一个有效的教育框架，向不同的学生系统地传授生物医学成像的基础知识和新发现，并创建资源共享的基础设施。拟议的教育计划包括新的生物医学成像课程、将研究和教育结合起来的活动、特别努力增加工程领域的女性和少数族裔的人数，以及传播图像重建成果和共享资源的开放平台。

项目成果

Highly accelerated cardiac cine parallel MRI using low-rank matrix completion and partial separability model

使用低秩矩阵完成和部分可分离模型的高度加速心脏电影并行 MRI

• DOI: 10.1117/12.2225490
• 发表时间: 2016
• 期刊: SPIE Proceedings
• 作者: Ahmad, Fauzia;Lyu, Jingyuan;Nakarmi, Ukash;Zhang, Chaoyi;Ying, Leslie
• 通讯作者: Ying, Leslie