CAREER: Compressed Sensing Magnetic Resonance Imaging

0846514 l。本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。本研究探讨了将新的数学理论转化为MRI实践的各种问题，并探讨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要求的条件，新的非自适应、非傅立叶编码和采样技术将被开发，新的稀疏变换将被发现以最有效地表示MR图像，高效和鲁棒的非线性重建算法也将被开发，以从稀疏采样数据中恢复图像。与现有的最大加速度快速MRI技术的集成也将被研究。此外，该研究将通过推导速度增强的理论界限并进行仿真和实验来评估性能。开发的技术的应用将探讨各种实际的磁共振成像问题。本提案的教育目标是开发一个有效的教育框架，以便系统地向不同的学生提供生物医学成像的基础知识和新发现，并为资源共享创建基础设施。拟议的教育计划包括新的生物医学成像课程，整合研究和教育的活动，特别努力增加工程中的女性和少数民族，以及一个开放的平台，传播图像重建方面的成果和共享资源。

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.