The 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.
