This project seeks to design a novel MR imaging scheme based on a new mathematical framework that provides guarantee for stable reconstruction. This new approach may run much faster than past implementations, further enhancing the data acquisition efficiency.
Postdoctoral fellow: Dr. Xiteng Liu, Mathematics and Statistics, York University
Lead faculty member: Dr. Hongmei Zhu, Mathematics and Statistics, York University
Magnetic Resonance Imaging (MRI) is an important medical imaging technology for clinical diagnostics. However, its slowness in data acquisition poses major problems in practice. In recent years, many research efforts to accelerate MRI data acquisition were based on the compressed sensing (CS) theory. CS is effective for signals that have highly sparse representations. However, it suffers from high computational complexity and lack of performance stability. In the proposed research, we aim to design a novel MR imaging scheme based on the system compression (SC) theory, a new mathematical framework with similar goals as CS but provides guarantee for stable reconstruction. This new approach may run much faster than the CS-based implementations, further enhancing the data acquisition efficiency. Our research will benefit the health care industry with the development of faster and better diagnostic MR imaging sequences.