Fig. 1.

Comparison of image reconstruction processes. Common undersampling factors for each method group and a few example algorithms are noted. If no method is used, a simple Fourier transform results in an aliased image if the acquisition is undersampled. Parallel imaging reconstruction methods, such as the generalized autocalibrating partial parallel acquisition (GRAPPA) or sensitivity encoding (SENSE) algorithms, can produce acceptable results up to undersampling rates of 4. Compressed sensing can achieve similar results with increased levels of undersampling. Deep learning methods, such as KIKI, automated transform by manifold approximation (AUTOMAP), and GrappaNet, have shown the potential to achieve good results with significantly higher levels of undersampling