Deep Structured Low-rank Algorithms for Uncalibrated MRI

Structured low-rank algorithms (SLR) are efficient in capturing several redundancies in the data that classical compressed sensing (CS) methods fail to capture. For instance, classical multichannel CS methods and MODL require calibration scans to estimate the coil sensitivities. When calibration data is not available or is corrupted by subject motion, these methods offer poor performance. SLR scheme enables the calibration-free recovery of parallel MRI data, offering improved performance over calibration-based strategies, when there is motion errors. However, SLR methods are associated with high computational complexity. Deep SLR approach offers fast image recovery, while offering improved performance over calibration based methods.