Keywords: Image acquisition: Reconstruction
Low-rank models that exploit the intrinsic redundancy in multidimensional MR signals for image reconstruction from sparse, noisy, and/or corrupted data have been widely used. These models serve as effective constraints for high-dimensional imaging problems that arise in many applications, e.g., dynamic MRI, quantitative MRI, and spectroscopic imaging. This talk will review what low-rank models are, how low-rank structures emerge or can be purposely induced from multidimensional MR data, and how they may be used in image reconstruction. Potential synergy with recent deep learning based reconstruction approaches will also be discussed.