SPARSE SAMPLING FOR MAGNETIC RESONANCE IMAGING

Functional Magnetic Resonance Imaging (MRI) and other dynamic MRI applications, require rapid acquisition to measure dynamic processes changes. Experimental data are collected in the k-space by following different trajectories to cover the whole space. Complete data acquisition needs several minutes: the reduction of the number of collected trajectories allows proportional acquisition time reduction but undersampling occurs, producing artefacts. In what follows, MRI sparse sampling acquisition and reconstruction methods are overviewed. In particular, sparse methods are grouped in two classes: the first contains methods in which the sampling scheme is independent of the sample shape, the most important is Compressed Sensing (CS); the other contains methods that adapt their sampling pattern, by modifying the acquisition trajectories (both in number and directions) during the acquisition, to the sample internal structure. In this second class, an emerging set of methods, hybrid forms of adaptive CS, are included and discussed. The current paper clarify the importance of using adaptive CS strategies in MRI to reduce acquisition time and undersampling artefacts and to improve the signal to noise ratio (SNR) of the resulting image.