Super-resolution Magnetic Resonance Image Reconstruction with k-t SPARSE-SENSE at its Core

Magnetic Resonance Imaging (MRI) super-resolution image reconstruction algorithm, is presented in the paper. It is shown that the approach improves MRI spatial resolution in cases Compressed Sensing (CS) sequences are used. Compressed sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging technique struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study our goal is to combine Super-Resolution image enhancement algorithm with CS framework to achieve high resolution MR output. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements.