CAREER: Compressed Sensing Magnetic Resonance Imaging

0846514L。Ying这项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），以彻底改变其成像速度并实现实时成像。拟议的研究调查各种问题，在新的数学理论转化为MRI的实践，并探讨各种想法来解决这些问题。具体而言，为了满足压缩感知理论所要求的条件，将开发新的非自适应、非傅立叶编码和采样技术，将发现新的稀疏变换以最有效地表示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恢复图像。还将研究与现有快速MRI技术的集成，以实现最大加速。此外，所提出的研究将通过推导速度提高的理论界限并进行仿真和实验来评估性能。将探讨各种实际的MR成像问题的开发技术的应用。本提案的教育目标是建立一个有效的教育框架，向不同的学生系统地提供生物医学成像的基本知识和新发现，并建立资源共享的基础设施。拟议的教育计划包括一个新的生物医学成像课程，将研究和教育与特别努力相结合的活动，以增加妇女和少数民族的工程，以及一个开放的平台，以传播成果和共享图像重建资源。

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.